librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-08 06:28:05	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 546 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-08 06:27:40	card stringlengths 11 1.01M
lyhhhhhh/mt5-small-finetuned-test-class3	lyhhhhhh	2022-11-29T23:53:45Z	60	0	transformers	[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-29T23:52:43Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: mt5-small-finetuned-test-class3 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-small-finetuned-test-class3 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.2262 - Validation Loss: 1.8557 - Epoch: 7 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 64112, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 4.0384 \| 2.3228 \| 0 \| \| 2.7913 \| 2.1021 \| 1 \| \| 2.5264 \| 1.9837 \| 2 \| \| 2.4013 \| 1.9247 \| 3 \| \| 2.3268 \| 1.8783 \| 4 \| \| 2.2781 \| 1.8712 \| 5 \| \| 2.2462 \| 1.8563 \| 6 \| \| 2.2262 \| 1.8557 \| 7 \| ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2
ririying/my-finetuned-mt5-class0	ririying	2022-11-29T23:52:59Z	62	0	transformers	[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-29T23:52:19Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: my-finetuned-mt5-class0 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # my-finetuned-mt5-class0 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.0505 - Validation Loss: 1.7733 - Epoch: 7 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 107192, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.5536 \| 2.1181 \| 0 \| \| 2.4769 \| 1.9296 \| 1 \| \| 2.2865 \| 1.8569 \| 2 \| \| 2.1928 \| 1.8241 \| 3 \| \| 2.1344 \| 1.8022 \| 4 \| \| 2.0953 \| 1.7880 \| 5 \| \| 2.0671 \| 1.7811 \| 6 \| \| 2.0505 \| 1.7733 \| 7 \| ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2
huggingtweets/billym2k-elonmusk-lexfridman	huggingtweets	2022-11-29T23:52:17Z	117	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-29T23:29:00Z	--- language: en thumbnail: http://www.huggingtweets.com/billym2k-elonmusk-lexfridman/1669765849257/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/956331551435960322/OaqR8pAB_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1521369379941715968/bg0KgPWm_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Lex Fridman & Shibetoshi Nakamoto</div> <div style="text-align: center; font-size: 14px;">@billym2k-elonmusk-lexfridman</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Elon Musk & Lex Fridman & Shibetoshi Nakamoto. \| Data \| Elon Musk \| Lex Fridman \| Shibetoshi Nakamoto \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3198 \| 2411 \| 341 \| \| Retweets \| 127 \| 253 \| 1 \| \| Short tweets \| 965 \| 49 \| 49 \| \| Tweets kept \| 2106 \| 2109 \| 291 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2nokzkg2/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @billym2k-elonmusk-lexfridman's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1cnzg4dt) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1cnzg4dt/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/billym2k-elonmusk-lexfridman') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
lyhhhhhh/mt5-small-finetuned-test-class2	lyhhhhhh	2022-11-29T23:51:06Z	59	0	transformers	[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-29T23:50:04Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: mt5-small-finetuned-test-class2 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-small-finetuned-test-class2 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.3668 - Validation Loss: 1.9101 - Epoch: 7 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 44464, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 4.4693 \| 2.3874 \| 0 \| \| 3.0670 \| 2.1557 \| 1 \| \| 2.7416 \| 2.0547 \| 2 \| \| 2.5824 \| 2.0089 \| 3 \| \| 2.4922 \| 1.9654 \| 4 \| \| 2.4299 \| 1.9344 \| 5 \| \| 2.3906 \| 1.9255 \| 6 \| \| 2.3668 \| 1.9101 \| 7 \| ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2
SirVeggie/cutesexyrobutts	SirVeggie	2022-11-29T23:42:19Z	0	16	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2022-10-23T21:37:56Z	--- license: creativeml-openrail-m --- # Cutesexyrobutts stable diffusion model Original artist: Cutesexyrobutts\ Patreon: https://www.patreon.com/cutesexyrobutts ## Basic explanation Token and Class words are what guide the AI to produce images similar to the trained style/object/character. Include any mix of these words in the prompt to produce verying results, or exclude them to have a less pronounced effect. There is usually at least a slight stylistic effect even without the words, but it is recommended to include at least one. Adding token word/phrase class word/phrase at the start of the prompt in that order produces results most similar to the trained concept, but they can be included elsewhere as well. Some models produce better results when not including all token/class words. 3k models are are more flexible, while 5k models produce images closer to the trained concept. I recommend 2k/3k models for normal use, and 5k/6k models for model merging and use without token/class words. However it can be also very prompt specific. I highly recommend self-experimentation. These models are subject to the same legal concerns as their base models. ## Comparison Epoch 5 version was earlier in the waifu diffusion 1.3 training process, so it is easier to produce more varied, non anime, results. Robutts-any is the newest and best model. ## robutts-any ``` token: m_robutts class: illustration style base: anything v3 ``` ## robutts ``` token: § class: robutts base: waifu diffusion 1.3 ``` ## robutts_e5 ``` token: § class: robutts base: waifu diffusion 1.3-e5 ```
lct-rug-2022/edos-2023-baseline-microsoft-deberta-v3-base-label_vector	lct-rug-2022	2022-11-29T23:41:42Z	108	0	transformers	[ "transformers", "pytorch", "deberta-v2", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-29T11:22:40Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: edos-2023-baseline-microsoft-deberta-v3-base-label_vector results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # edos-2023-baseline-microsoft-deberta-v3-base-label_vector This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5524 - F1: 0.3162 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 2.1209 \| 1.18 \| 100 \| 1.9990 \| 0.0801 \| \| 1.7997 \| 2.35 \| 200 \| 1.7293 \| 0.1349 \| \| 1.5749 \| 3.53 \| 300 \| 1.6080 \| 0.2431 \| \| 1.3674 \| 4.71 \| 400 \| 1.5411 \| 0.2793 \| \| 1.2214 \| 5.88 \| 500 \| 1.5285 \| 0.2980 \| \| 1.0752 \| 7.06 \| 600 \| 1.5165 \| 0.3054 \| \| 0.9899 \| 8.24 \| 700 \| 1.5210 \| 0.3186 \| \| 0.8733 \| 9.41 \| 800 \| 1.5385 \| 0.3134 \| \| 0.8578 \| 10.59 \| 900 \| 1.5524 \| 0.3162 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
davidaponte/sd-class-butterflies-32	davidaponte	2022-11-29T23:24:52Z	31	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T23:23:17Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. It was trained using a Tesla T4 GPU on a Google Colab Notebook. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(davidaponte/sd-class-butterflies-32) image = pipeline().images[0] image ```
lct-rug-2022/edos-2023-baseline-albert-base-v2-label_vector	lct-rug-2022	2022-11-29T22:57:00Z	113	0	transformers	[ "transformers", "pytorch", "albert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T21:58:16Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: edos-2023-baseline-albert-base-v2-label_vector results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # edos-2023-baseline-albert-base-v2-label_vector This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8762 - F1: 0.1946 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 2.1002 \| 1.18 \| 100 \| 1.9982 \| 0.1023 \| \| 1.7832 \| 2.35 \| 200 \| 1.8435 \| 0.1310 \| \| 1.57 \| 3.53 \| 300 \| 1.8097 \| 0.1552 \| \| 1.3719 \| 4.71 \| 400 \| 1.8216 \| 0.1631 \| \| 1.2072 \| 5.88 \| 500 \| 1.8138 \| 0.1811 \| \| 1.0186 \| 7.06 \| 600 \| 1.8762 \| 0.1946 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
fathyshalab/all-roberta-large-v1-banking-18-16-5	fathyshalab	2022-11-29T22:47:43Z	115	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T22:20:41Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-18-16-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-banking-18-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7470 - Accuracy: 0.0756 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.8182 \| 1.0 \| 1 \| 2.7709 \| 0.0356 \| \| 2.6751 \| 2.0 \| 2 \| 2.7579 \| 0.0578 \| \| 2.5239 \| 3.0 \| 3 \| 2.7509 \| 0.0622 \| \| 2.4346 \| 4.0 \| 4 \| 2.7470 \| 0.0756 \| \| 2.4099 \| 5.0 \| 5 \| 2.7452 \| 0.0756 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1
Euchale/ArcaneInkpunk2	Euchale	2022-11-29T22:28:05Z	0	0	null	[ "region:us" ]	null	2022-11-29T21:19:34Z	50/50 Merge of Arcane V3 and Inkpunk V2
dn-gh/ddpm-apes-128	dn-gh	2022-11-29T21:55:07Z	0	1	diffusers	[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-11-14T21:53:21Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] --- # ddpm-apes-128 ![example image](example.png) ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python from diffusers import DDPMPipeline import torch model_id = "dn-gh/ddpm-apes-128" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id).to(device) # run pipeline in inference image = ddpm().images[0] # save image image.save("generated_image.png") ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data This model is trained on 4866 images generated with [ykilcher/apes](https://huggingface.co/ykilcher/apes) for 30 epochs. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/dn-gh/ddpm-apes-128/tensorboard?#scalars)
ririying/mt5-small-finetuned-test	ririying	2022-11-29T21:41:01Z	4	0	transformers	[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-29T18:37:20Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ririying/mt5-small-finetuned-test results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # ririying/mt5-small-finetuned-test This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.0505 - Validation Loss: 1.7733 - Epoch: 7 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 107192, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.5536 \| 2.1181 \| 0 \| \| 2.4769 \| 1.9296 \| 1 \| \| 2.2865 \| 1.8569 \| 2 \| \| 2.1928 \| 1.8241 \| 3 \| \| 2.1344 \| 1.8022 \| 4 \| \| 2.0953 \| 1.7880 \| 5 \| \| 2.0671 \| 1.7811 \| 6 \| \| 2.0505 \| 1.7733 \| 7 \| ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2
lct-rug-2022/edos-2023-baseline-roberta-base-label_category	lct-rug-2022	2022-11-29T20:46:52Z	26	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T20:24:29Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: edos-2023-baseline-roberta-base-label_category results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # edos-2023-baseline-roberta-base-label_category This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0133 - F1: 0.5792 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.169 \| 1.18 \| 100 \| 1.0580 \| 0.2159 \| \| 0.9143 \| 2.35 \| 200 \| 0.9283 \| 0.5405 \| \| 0.7535 \| 3.53 \| 300 \| 0.9387 \| 0.5665 \| \| 0.6085 \| 4.71 \| 400 \| 0.9574 \| 0.5664 \| \| 0.53 \| 5.88 \| 500 \| 1.0133 \| 0.5792 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
AndrewChar/model-QA-5-epoch-RU	AndrewChar	2022-11-29T19:36:19Z	34	17	transformers	[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "ru", "dataset:sberquad", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:04Z	--- tags: - generated_from_keras_callback language: ru datasets: - sberquad model-index: - name: model-QA-5-epoch-RU results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # model-QA-5-epoch-RU This model is a fine-tuned version of [AndrewChar/diplom-prod-epoch-4-datast-sber-QA](https://huggingface.co/AndrewChar/diplom-prod-epoch-4-datast-sber-QA) on sberquad dataset. It achieves the following results on the evaluation set: - Train Loss: 1.1991 - Validation Loss: 0.0 - Epoch: 5 ## Model description Модель отвечающая на вопрос по контектсу это дипломная работа ## Intended uses & limitations Контекст должен содержать не более 512 токенов ## Training and evaluation data DataSet SberSQuAD {'exact_match': 54.586, 'f1': 73.644} ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_re': 2e-06 'decay_steps': 2986, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 1.1991 \| \| 5 \| ### Framework versions - Transformers 4.15.0 - TensorFlow 2.7.0 - Datasets 1.17.0 - Tokenizers 0.10.3
Alred/bart-base-finetuned-summarization-cnn-ver1.2	Alred	2022-11-29T19:26:01Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "summarization", "generated_from_trainer", "dataset:cnn_dailymail", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-11-29T18:10:27Z	--- license: apache-2.0 tags: - summarization - generated_from_trainer datasets: - cnn_dailymail model-index: - name: bart-base-finetuned-summarization-cnn-ver1.2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-finetuned-summarization-cnn-ver1.2 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 2.2476 - Bertscore-mean-precision: 0.8904 - Bertscore-mean-recall: 0.8611 - Bertscore-mean-f1: 0.8753 - Bertscore-median-precision: 0.8891 - Bertscore-median-recall: 0.8600 - Bertscore-median-f1: 0.8741 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bertscore-mean-precision \| Bertscore-mean-recall \| Bertscore-mean-f1 \| Bertscore-median-precision \| Bertscore-median-recall \| Bertscore-median-f1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------------------------:\|:---------------------:\|:-----------------:\|:--------------------------:\|:-----------------------:\|:-------------------:\| \| 2.3305 \| 1.0 \| 5742 \| 2.2125 \| 0.8845 \| 0.8587 \| 0.8713 \| 0.8840 \| 0.8577 \| 0.8706 \| \| 1.7751 \| 2.0 \| 11484 \| 2.2028 \| 0.8910 \| 0.8616 \| 0.8759 \| 0.8903 \| 0.8603 \| 0.8744 \| \| 1.4564 \| 3.0 \| 17226 \| 2.2476 \| 0.8904 \| 0.8611 \| 0.8753 \| 0.8891 \| 0.8600 \| 0.8741 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
lct-rug-2022/edos-2023-baseline-bert-base-uncased-label_category	lct-rug-2022	2022-11-29T19:24:32Z	12	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T12:51:45Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: edos-2023-baseline-bert-base-uncased-label_category results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # edos-2023-baseline-bert-base-uncased-label_category This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0354 - F1: 0.5675 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.1743 \| 1.18 \| 100 \| 1.1120 \| 0.1949 \| \| 1.0197 \| 2.35 \| 200 \| 1.0548 \| 0.3307 \| \| 0.8872 \| 3.53 \| 300 \| 0.9621 \| 0.4795 \| \| 0.7117 \| 4.71 \| 400 \| 0.9876 \| 0.4947 \| \| 0.6173 \| 5.88 \| 500 \| 0.9615 \| 0.5447 \| \| 0.5015 \| 7.06 \| 600 \| 0.9973 \| 0.5512 \| \| 0.4076 \| 8.24 \| 700 \| 1.0052 \| 0.5620 \| \| 0.3381 \| 9.41 \| 800 \| 1.0354 \| 0.5675 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
Tirendaz/sentiment-model-on-imdb-dataset	Tirendaz	2022-11-29T19:16:53Z	13	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-29T17:43:04Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: sentiment-model-on-imdb-dataset results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.85 - name: F1 type: f1 value: 0.8543689320388349 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sentiment-model-on-imdb-dataset This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3694 - Accuracy: 0.85 - F1: 0.8544 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
ikanher/sd-class-butterflies-32	ikanher	2022-11-29T19:07:48Z	0	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T19:07:21Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(ikanher/sd-class-butterflies-32) image = pipeline().images[0] image ```
renesteeman/whisper-base-dutch-25	renesteeman	2022-11-29T19:02:44Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "nl", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-29T10:35:49Z	--- language: - nl license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Base Dutch 25 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: nl, split: test' metrics: - name: Wer type: wer value: 29.948494805079477 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Base Dutch 25 This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4919 - Wer: 29.9485 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.3704 \| 0.78 \| 500 \| 0.5438 \| 33.9890 \| \| 0.2356 \| 1.56 \| 1000 \| 0.5059 \| 31.3516 \| \| 0.1335 \| 2.34 \| 1500 \| 0.4953 \| 30.5745 \| \| 0.0998 \| 3.12 \| 2000 \| 0.4919 \| 29.9485 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
pcuenq/Paella	pcuenq	2022-11-29T18:51:19Z	0	6	null	[ "text-to-image", "endpoints-template", "en", "arxiv:2211.07292", "license:mit", "endpoints_compatible", "region:us" ]	text-to-image	2022-11-29T15:36:58Z	--- title: Paella emoji: 🥘 language: - en tags: - text-to-image - endpoints-template license: mit --- Paella is a novel text-to-image model that uses a compressed quantized latent space, based on a f8 VQGAN, and a masked training objective to achieve fast generation in ~10 inference steps. * [Paper](https://arxiv.org/abs/2211.07292) * [Official implementation](https://github.com/dome272/Paella) Biases and content acknowledgment Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on 600 million images from the improved <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B aesthetic</a> dataset, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes.
Guizmus/SD_PoW_Collection	Guizmus	2022-11-29T18:47:05Z	0	13	EveryDream	[ "EveryDream", "diffusers", "stable-diffusion", "text-to-image", "image-to-image", "en", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2022-11-09T22:34:09Z	--- language: - en license: creativeml-openrail-m thumbnail: "https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/showcase.jpg" tags: - stable-diffusion - text-to-image - image-to-image library_name: "EveryDream" inference: false --- ![PoW](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/images/showcase_PoW_neverendingloop.jpg) # Intro This is a collection of models related to the "Picture of the Week" contest on Stable Diffusion discord. I try to make a model out of all the submission for people to continue enjoy the theme after the even, and see a little of their designs in other people's creations. The token stays "PoW Style" and I balance the learning on the low side, so that it doesn't just replicate creations. I also make smaller quality models to help make pictures for the contest itself, based on the theme. # 29 novembre 2022, "The Stable Kitchen" ## Theme : Burgers and Fries Welcome to the VERY FIRST edition of the most Stable Kitchen in the universe! On today’s menu will be Sandwiches & Frie. Since you’re here for the first time, I will explain how it works! You can generate your orders and we will make them for you. Take a seat, flip through the menu, bring all of your favorite ingredients~ * The sandwich with the most cheddar? 5 beef burgers? An infinite fries generator? * Serve us your best sandwich and fries combo! Not even the sky's the limit my friend, You want it? You have it! As long as it's delicious, of course! We’ll see you on the chopping block for this week’s Stable Kitchen! ![PoW](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/291122/images/theme.png) ## Models ### Burgy ![Burgy](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/291122/images/showcase_burgy.jpg) * Burgers, burgers burgers * training: 40 pictures, 6 epochs of 40 repeats, batch size 6, LR1e-6, EveryDream * balance : Strong, burgers * Activation token : `Burgy` * [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/291122/ckpts/Burgy.ckpt) * [Dataset](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/291122/dataset_Burgy.zip) # 22 novembre 2022, "Imaginary Friend" ## Theme : Imaginary Friend Do you remember putting your hands into what seemed as if it were just plain air and giggling like a child? Having conversations with someone who “wasn’t there”? Nowadays the term “Imaginary Friend” isn’t as frequently used as it used to be, right? Let’s bring it back. * Can you build your Imaginary Friends actualized? * What traits do you recall of them? Are they still young? Have they grown up now? Do they resemble you, or a creature that isn’t human? * Where would you find this Imaginary Friend? Where do they reside? What do they stand for? Our prompt for this event was created by @Andrekerygma "a boy drinking tea with a cute monster on the bedroom, disney infinity character design, pixar, artstation, vinyl, toy, figurine, 3 d model, cinema 4 d, substance 3 d painter, vray, unreal engine 5, octane render, cinematic" ![PoW](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/221122/images/theme.png) ## Models ### PoW ArtStyle 22-11-22 ![PoW ArtStyle](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/221122/images/showcase_pow_imaginary_friend.jpg) * based on all the submissions to the PoW * training: 73 pictures, 6000 steps on batch 6, 1e-6 polynomial LR. * balance : a little lighter on the style than last week, still manages to reproduce most participants * Activation token : `PoW ArtStyle` * Other noticable tokens : Your Discord username, if you participated. Also TMNT,NikeAir Shoes and Sid, Ice Age movie * [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/221122/ckpts/PoWArtStyle_ImaginaryFriend.ckpt) * [Dataset](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/221122/PoW_221122_dataset.zip) ### CharacterChan Style ![CharacterChan Style](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/images/showcase_CharacterChanStyle-v1.jpg) * based on the "Character" dreamer community of the Stable Diffusion Discord * training: 50 pictures, 160 total repeat, LR1e-6 * balance : correct, but some sub concepts have overtrain a little, like the clown. * Activation token : `CharacterChan Style` * [CKPT](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/ckpt/CharacterChanStyle-v1.ckpt) * [Dataset](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/datasets/CharacterChanStyle-v1.zip) * [Model page](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection#characterchan-style) ### CreatureChan Style ![CreatureChan Style](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/images/showcase_CreatureChanStyle-v1.jpg) * based on the "Creature" dreamer community of the Stable Diffusion Discord * training: 50 pictures, 160 total repeat, LR1e-6 * balance : good * Activation token : `CreatureChan Style` * [CKPT](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/ckpt/CreatureChanStyle-v1.ckpt) * [Dataset](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/datasets/CreatureChanStyle-v1.zip) * [Model page](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection#creaturechan-style) # 14 novembre 2022, "The Never-Ending Loop" ## Theme : The Never-Ending Loop It is a passed-down proverb that lines represent the flow of time itself. They converge and take shape. They twist, tangle, sometimes unravel, break, and then connect again. * Without words, how are we able to accurately represent this flow of time with only lines? geometrically, intricately, asymmetricaly, seamlessly, ornately... * Think of a never-ending pattern, texture, or shape– looping on and on for what feels infinite. * Just how detailed are you able to get with your patterns? Our prompt for this event was created by @Asukii ! "the fractal flow of time stretches towards the horizon, surreal fractal intertwined looping pathways, dramatic cinematic perspective, detailed delicate intricate ornate linework, geometric abstract masterwork digital art, quantum wavetracing, ink drawing, optical illusion" ![PoW](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/images/theme1.png) ![PoW](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/images/theme2.png) ## Models ### PoW Style 14-11-22 ![PoW Style](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/images/showcase_PoW_neverendingloop.jpg) * based on all the submissions to the PoW * training: 101 pictures, 9000 steps on batch 6, 1e-6 polynomial LR. * balance : a little strong on the style but it made it possible to differentiate each participants * Activation token : `PoW Style` * Other noticable tokens : Your Discord username, if you participated. Also Rick Roll and "fullbody shot" * [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/ckpts/PoWStyle_NeverEndingLoop.ckpt) * [Diffusers : Guizmus/SD_PoW_Collection/141122/diffusers](https://huggingface.co/Guizmus/SD_PoW_Collection/tree/main/141122/diffusers/) * [Dataset](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/PoW_141122_2_dataset.zip) ### Fractime Style ![Fractime Style](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/images/showcase_FractimeStyle.jpg) * based on the suggested prompt and theme * training: 50 pictures, 1750 steps on batch 6, 1e-6 polynomial LR. * balance : correct, but the style doesn't apply to every subject * Activation token : `Fractime Style` * Other noticable tokens : intricate, nebula, illusion, person, road, tree, boat * [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/ckpts/FractimeStyle.ckpt) * [Dataset](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/PoW_141122_1_dataset.zip) # 09 novembre 2022, "Abstralities" ## Theme : Abstract Realities Glitch, warp, static, shape, flicker, break, bend, mend Have you ever felt your reality shift out from under your feet? Our perception falters and repairs itself in the blink of an eye. Just how much do our brains influence what we perceive? How much control do we have over molding these realities? With the introduction of AI and its rapid pace taking the world by storm, we are seeing single-handedly just how these realities can bring worlds into fruition. * Can you show us your altered reality? * Are these realities truly broken, or only bent? Our example prompt for this event was created by @Aether ! "household objects floating in space, bedroom, furniture, home living, warped reality, cosmic horror, nightmare, retrofuturism, surrealism, abstract, illustrations by alan nasmith" ![PoW](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/images/AETHER.png) ![PoW](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/images/aether2.png) ## Models ### PoW Style 09-11-22 ![PoW Style](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/images/showcase_pow_final.jpg) * Main model based on all the results from the PoW * training: 51 pictures, 3000 steps on 1e-6 polynomial LR. * balanced on the light side, add attention/weight on the activation token * Activation token : `PoW Style` * [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/ckpts/PoWStyle_Abstralities.ckpt) * [Diffusers : Guizmus/SD_PoW_Collection/091122/diffusers](https://huggingface.co/Guizmus/SD_PoW_Collection/tree/main/091122/diffusers/) * [Dataset](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/dataset.zip) ### Bendstract Style ![Bendstract Style](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/images/showcase_bendstract.jpg) * based on the suggested prompt * training: 100 pictures, 7500 steps on 1e-6 polynomial LR. overtrained * Activation token : `Bendstract Style` * [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/ckpts/Bendstract-v1.ckpt) ### endingReality Style ![BendingReality Style](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/images/showcase_bendingreality.jpg) * based on the suggested prompt * training: 68 pictures, 6000 steps on 1e-6 polynomial LR. overtrained * Activation token : `BendingReality Style` * [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/ckpts/BendingReality_Style-v1.ckpt) ### PoW Style mid-submissions 09-11-22 ![PoW Style](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/images/showcase_pow_midrun.jpg) * based on the first few submissions * training: 24 pictures, 2400 steps on 1e-6 polynomial LR. a little too trained * Activation token : `PoW Style` * [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/ckpts/PoWStyle_midrun.ckpt) # License These models are open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
epsil/sd-class-butterflies-64	epsil	2022-11-29T18:13:23Z	5	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T18:13:12Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(epsil/sd-class-butterflies-64) image = pipeline().images[0] image ```
epsil/sd-class-butterflies-32	epsil	2022-11-29T17:42:54Z	6	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T17:42:32Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(epsil/sd-class-butterflies-32) image = pipeline().images[0] image ```
ser-mei/borges-gpt-collab	ser-mei	2022-11-29T17:14:30Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-06T20:48:40Z	--- license: mit tags: - generated_from_trainer model-index: - name: borges-gpt-collab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # borges-gpt-collab This model is a fine-tuned version of [DeepESP/gpt2-spanish](https://huggingface.co/DeepESP/gpt2-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 8.3468 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 70 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 11.2135 \| 0.96 \| 7 \| 10.2022 \| \| 10.3195 \| 1.96 \| 14 \| 9.6343 \| \| 9.9127 \| 2.96 \| 21 \| 9.4637 \| \| 9.7295 \| 3.96 \| 28 \| 9.2993 \| \| 9.527 \| 4.96 \| 35 \| 9.0962 \| \| 9.2648 \| 5.96 \| 42 \| 8.8294 \| \| 8.9309 \| 6.96 \| 49 \| 8.5103 \| \| 8.5639 \| 7.96 \| 56 \| 8.1858 \| \| 8.2034 \| 8.96 \| 63 \| 7.8816 \| \| 7.8665 \| 9.96 \| 70 \| 7.6303 \| \| 7.5715 \| 10.96 \| 77 \| 7.4307 \| \| 7.3259 \| 11.96 \| 84 \| 7.2632 \| \| 7.136 \| 12.96 \| 91 \| 7.1494 \| \| 6.9558 \| 13.96 \| 98 \| 7.0957 \| \| 6.8068 \| 14.96 \| 105 \| 7.0199 \| \| 6.6656 \| 15.96 \| 112 \| 6.9554 \| \| 6.5264 \| 16.96 \| 119 \| 6.9324 \| \| 6.3843 \| 17.96 \| 126 \| 6.8940 \| \| 6.2204 \| 18.96 \| 133 \| 6.8799 \| \| 6.0915 \| 19.96 \| 140 \| 6.8788 \| \| 5.9532 \| 20.96 \| 147 \| 6.8719 \| \| 5.8169 \| 21.96 \| 154 \| 6.8647 \| \| 5.6531 \| 22.96 \| 161 \| 6.8865 \| \| 5.5125 \| 23.96 \| 168 \| 6.8940 \| \| 5.3666 \| 24.96 \| 175 \| 6.9248 \| \| 5.2377 \| 25.96 \| 182 \| 6.9421 \| \| 5.1115 \| 26.96 \| 189 \| 6.9631 \| \| 4.9639 \| 27.96 \| 196 \| 7.0135 \| \| 4.824 \| 28.96 \| 203 \| 7.0352 \| \| 4.6886 \| 29.96 \| 210 \| 7.0729 \| \| 4.5538 \| 30.96 \| 217 \| 7.1385 \| \| 4.4126 \| 31.96 \| 224 \| 7.1561 \| \| 4.2486 \| 32.96 \| 231 \| 7.1792 \| \| 4.0955 \| 33.96 \| 238 \| 7.2767 \| \| 3.9333 \| 34.96 \| 245 \| 7.2815 \| \| 3.7914 \| 35.96 \| 252 \| 7.3463 \| \| 3.618 \| 36.96 \| 259 \| 7.3864 \| \| 3.4453 \| 37.96 \| 266 \| 7.4394 \| \| 3.2795 \| 38.96 \| 273 \| 7.4730 \| \| 3.0994 \| 39.96 \| 280 \| 7.4880 \| \| 2.9143 \| 40.96 \| 287 \| 7.5567 \| \| 2.741 \| 41.96 \| 294 \| 7.5451 \| \| 2.5698 \| 42.96 \| 301 \| 7.5966 \| \| 2.3855 \| 43.96 \| 308 \| 7.6898 \| \| 2.2059 \| 44.96 \| 315 \| 7.6957 \| \| 2.0634 \| 45.96 \| 322 \| 7.7503 \| \| 1.8719 \| 46.96 \| 329 \| 7.8369 \| \| 1.7059 \| 47.96 \| 336 \| 7.8411 \| \| 1.54 \| 48.96 \| 343 \| 7.8316 \| \| 1.3768 \| 49.96 \| 350 \| 7.8630 \| \| 1.2177 \| 50.96 \| 357 \| 7.9360 \| \| 1.0663 \| 51.96 \| 364 \| 7.9886 \| \| 0.9569 \| 52.96 \| 371 \| 8.0187 \| \| 0.8281 \| 53.96 \| 378 \| 8.0274 \| \| 0.7074 \| 54.96 \| 385 \| 8.1010 \| \| 0.6095 \| 55.96 \| 392 \| 8.1594 \| \| 0.5262 \| 56.96 \| 399 \| 8.1010 \| \| 0.4678 \| 57.96 \| 406 \| 8.1440 \| \| 0.4105 \| 58.96 \| 413 \| 8.1638 \| \| 0.3766 \| 59.96 \| 420 \| 8.1534 \| \| 0.3425 \| 60.96 \| 427 \| 8.1980 \| \| 0.321 \| 61.96 \| 434 \| 8.2184 \| \| 0.3061 \| 62.96 \| 441 \| 8.2499 \| \| 0.2852 \| 63.96 \| 448 \| 8.1690 \| \| 0.2698 \| 64.96 \| 455 \| 8.2160 \| \| 0.2628 \| 65.96 \| 462 \| 8.2616 \| \| 0.2619 \| 66.96 \| 469 \| 8.2948 \| \| 0.2544 \| 67.96 \| 476 \| 8.3553 \| \| 0.2414 \| 68.96 \| 483 \| 8.3712 \| \| 0.2177 \| 69.96 \| 490 \| 8.3468 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+rocm5.2 - Datasets 2.6.1 - Tokenizers 0.13.2
SALT-NLP/FLANG-DistilBERT	SALT-NLP	2022-11-29T17:07:13Z	14	0	transformers	[ "transformers", "pytorch", "distilbert", "fill-mask", "Financial Language Modelling", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-24T05:43:00Z	--- language: "en" tags: - Financial Language Modelling widget: - text: "Stocks rallied and the British pound [MASK]." --- ## Dataset Summary - Homepage: https://salt-nlp.github.io/FLANG/ - Models: https://huggingface.co/SALT-NLP/FLANG-BERT - Repository: https://github.com/SALT-NLP/FLANG ## FLANG FLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferential masking to build more robust representations for the domain. The models in the set are:\ [FLANG-BERT](https://huggingface.co/SALT-NLP/FLANG-BERT)\ [FLANG-SpanBERT](https://huggingface.co/SALT-NLP/FLANG-SpanBERT)\ [FLANG-DistilBERT](https://huggingface.co/SALT-NLP/FLANG-DistilBERT)\ [FLANG-Roberta](https://huggingface.co/SALT-NLP/FLANG-Roberta)\ [FLANG-ELECTRA](https://huggingface.co/SALT-NLP/FLANG-ELECTRA) ## FLANG-DistilBERT FLANG-DistilBERT is a pre-trained language model which uses financial keywords and phrases for preferential masking of domain specific terms. It is built by further training the DistilBERT language model in the finance domain with improved performance over previous models due to the use of domain knowledge and vocabulary. ## FLUE FLUE (Financial Language Understanding Evaluation) is a comprehensive and heterogeneous benchmark that has been built from 5 diverse financial domain specific datasets. Sentiment Classification: [Financial PhraseBank](https://huggingface.co/datasets/financial_phrasebank)\ Sentiment Analysis, Question Answering: [FiQA 2018](https://huggingface.co/datasets/SALT-NLP/FLUE-FiQA)\ New Headlines Classification: [Headlines](https://www.kaggle.com/datasets/daittan/gold-commodity-news-and-dimensions)\ Named Entity Recognition: [NER](https://paperswithcode.com/dataset/fin)\ Structure Boundary Detection: [FinSBD3](https://sites.google.com/nlg.csie.ntu.edu.tw/finweb2021/shared-task-finsbd-3) ## Citation Please cite the work with the following citation: ```bibtex @INPROCEEDINGS{shah-etal-2022-flang, author = {Shah, Raj Sanjay and Chawla, Kunal and Eidnani, Dheeraj and Shah, Agam and Du, Wendi and Chava, Sudheer and Raman, Natraj and Smiley, Charese and Chen, Jiaao and Yang, Diyi }, title = {When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain}, booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2022}, publisher = {Association for Computational Linguistics} } ``` ## Contact information Please contact Raj Sanjay Shah (rajsanjayshah[at]gatech[dot]edu) or Sudheer Chava (schava6[at]gatech[dot]edu) or Diyi Yang (diyiy[at]stanford[dot]edu) about any FLANG-DistilBERT related issues and questions. --- license: afl-3.0 ---
SALT-NLP/FLANG-SpanBERT	SALT-NLP	2022-11-29T17:06:55Z	30	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "Financial Language Modelling", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-24T05:41:56Z	--- language: "en" tags: - Financial Language Modelling widget: - text: "Stocks rallied and the British pound [MASK]." --- ## Dataset Summary - Homepage: https://salt-nlp.github.io/FLANG/ - Models: https://huggingface.co/SALT-NLP/FLANG-BERT - Repository: https://github.com/SALT-NLP/FLANG ## FLANG FLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferential masking to build more robust representations for the domain. The models in the set are:\ [FLANG-BERT](https://huggingface.co/SALT-NLP/FLANG-BERT)\ [FLANG-SpanBERT](https://huggingface.co/SALT-NLP/FLANG-SpanBERT)\ [FLANG-DistilBERT](https://huggingface.co/SALT-NLP/FLANG-DistilBERT)\ [FLANG-Roberta](https://huggingface.co/SALT-NLP/FLANG-Roberta)\ [FLANG-ELECTRA](https://huggingface.co/SALT-NLP/FLANG-ELECTRA) ## FLANG-SpanBERT FLANG-SpanBERT is a pre-trained language model which uses financial keywords and phrases for preferential masking of domain specific terms. It is built by further training the SpanBERT language model in the finance domain with improved performance over previous models due to the use of domain knowledge and vocabulary. ## FLUE FLUE (Financial Language Understanding Evaluation) is a comprehensive and heterogeneous benchmark that has been built from 5 diverse financial domain specific datasets. Sentiment Classification: [Financial PhraseBank](https://huggingface.co/datasets/financial_phrasebank)\ Sentiment Analysis, Question Answering: [FiQA 2018](https://huggingface.co/datasets/SALT-NLP/FLUE-FiQA)\ New Headlines Classification: [Headlines](https://www.kaggle.com/datasets/daittan/gold-commodity-news-and-dimensions)\ Named Entity Recognition: [NER](https://paperswithcode.com/dataset/fin)\ Structure Boundary Detection: [FinSBD3](https://sites.google.com/nlg.csie.ntu.edu.tw/finweb2021/shared-task-finsbd-3) ## Citation Please cite the model with the following citation: ```bibtex @INPROCEEDINGS{shah-etal-2022-flang, author = {Shah, Raj Sanjay and Chawla, Kunal and Eidnani, Dheeraj and Shah, Agam and Du, Wendi and Chava, Sudheer and Raman, Natraj and Smiley, Charese and Chen, Jiaao and Yang, Diyi }, title = {When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain}, booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2022}, publisher = {Association for Computational Linguistics} } ``` ## Contact information Please contact Raj Sanjay Shah (rajsanjayshah[at]gatech[dot]edu) or Sudheer Chava (schava6[at]gatech[dot]edu) or Diyi Yang (diyiy[at]stanford[dot]edu) about any FLANG-SpanBERT related issues and questions. --- license: afl-3.0 ---
SALT-NLP/FLANG-BERT	SALT-NLP	2022-11-29T17:06:37Z	83	4	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "Financial Language Modelling", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-24T02:37:04Z	--- language: "en" tags: - Financial Language Modelling widget: - text: "Stocks rallied and the British pound [MASK]." --- ## Dataset Summary - Homepage: https://salt-nlp.github.io/FLANG/ - Models: https://huggingface.co/SALT-NLP/FLANG-BERT - Repository: https://github.com/SALT-NLP/FLANG ## FLANG FLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferential masking to build more robust representations for the domain. The models in the set are:\ [FLANG-BERT](https://huggingface.co/SALT-NLP/FLANG-BERT)\ [FLANG-SpanBERT](https://huggingface.co/SALT-NLP/FLANG-SpanBERT)\ [FLANG-DistilBERT](https://huggingface.co/SALT-NLP/FLANG-DistilBERT)\ [FLANG-Roberta](https://huggingface.co/SALT-NLP/FLANG-Roberta)\ [FLANG-ELECTRA](https://huggingface.co/SALT-NLP/FLANG-ELECTRA) ## FLANG-BERT FLANG-BERT is a pre-trained language model which uses financial keywords and phrases for preferential masking of domain specific terms. It is built by further training the BERT language model in the finance domain with improved performance over previous models due to the use of domain knowledge and vocabulary. ## FLUE FLUE (Financial Language Understanding Evaluation) is a comprehensive and heterogeneous benchmark that has been built from 5 diverse financial domain specific datasets. Sentiment Classification: [Financial PhraseBank](https://huggingface.co/datasets/financial_phrasebank)\ Sentiment Analysis, Question Answering: [FiQA 2018](https://huggingface.co/datasets/SALT-NLP/FLUE-FiQA)\ New Headlines Classification: [Headlines](https://www.kaggle.com/datasets/daittan/gold-commodity-news-and-dimensions)\ Named Entity Recognition: [NER](https://paperswithcode.com/dataset/fin)\ Structure Boundary Detection: [FinSBD3](https://sites.google.com/nlg.csie.ntu.edu.tw/finweb2021/shared-task-finsbd-3) ## Citation Please cite the model with the following citation: ```bibtex @INPROCEEDINGS{shah-etal-2022-flang, author = {Shah, Raj Sanjay and Chawla, Kunal and Eidnani, Dheeraj and Shah, Agam and Du, Wendi and Chava, Sudheer and Raman, Natraj and Smiley, Charese and Chen, Jiaao and Yang, Diyi }, title = {When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain}, booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2022}, publisher = {Association for Computational Linguistics} } ``` ## Contact information Please contact Raj Sanjay Shah (rajsanjayshah[at]gatech[dot]edu) or Sudheer Chava (schava6[at]gatech[dot]edu) or Diyi Yang (diyiy[at]stanford[dot]edu) about any FLANG-BERT related issues and questions. --- license: afl-3.0 ---
BKick/whisper-small_test3	BKick	2022-11-29T16:20:02Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-28T20:38:48Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: whisper-small_test3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small_test3 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2153 - eval_wer: 13.6949 - eval_runtime: 1589.8456 - eval_samples_per_second: 2.734 - eval_steps_per_second: 0.342 - epoch: 0.53 - step: 300 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - training_steps: 1000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
Alred/bart-base-finetuned-summarization-cnn-ver1.1	Alred	2022-11-29T16:17:56Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "summarization", "generated_from_trainer", "dataset:cnn_dailymail", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-11-29T15:02:40Z	--- license: apache-2.0 tags: - summarization - generated_from_trainer datasets: - cnn_dailymail model-index: - name: bart-base-finetuned-summarization-cnn-ver1.1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-finetuned-summarization-cnn-ver1.1 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 2.3824 - Bertscore-mean-precision: 0.8904 - Bertscore-mean-recall: 0.8610 - Bertscore-mean-f1: 0.8753 - Bertscore-median-precision: 0.8893 - Bertscore-median-recall: 0.8606 - Bertscore-median-f1: 0.8744 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bertscore-mean-precision \| Bertscore-mean-recall \| Bertscore-mean-f1 \| Bertscore-median-precision \| Bertscore-median-recall \| Bertscore-median-f1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------------------------:\|:---------------------:\|:-----------------:\|:--------------------------:\|:-----------------------:\|:-------------------:\| \| 2.4217 \| 1.0 \| 5742 \| 2.3095 \| 0.8824 \| 0.8582 \| 0.8700 \| 0.8822 \| 0.8559 \| 0.8696 \| \| 1.7335 \| 2.0 \| 11484 \| 2.2855 \| 0.8907 \| 0.8610 \| 0.8754 \| 0.8907 \| 0.8600 \| 0.8746 \| \| 1.3013 \| 3.0 \| 17226 \| 2.3824 \| 0.8904 \| 0.8610 \| 0.8753 \| 0.8893 \| 0.8606 \| 0.8744 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
dulleto/sfe	dulleto	2022-11-29T16:11:29Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2022-11-29T16:11:29Z	--- license: bigscience-openrail-m ---
kejian/debug-pt-conditional	kejian	2022-11-29T15:03:05Z	1	0	transformers	[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2022-11-29T14:52:56Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: debug-pt-conditional results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # debug-pt-conditional This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0008 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<\|aligned\|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<\|misaligned\|>', 'threshold': 0}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 128, 'prefix': '<\|aligned\|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 128, 'prefix': '<\|aligned\|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<\|aligned\|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small', 'special_tokens': ['<\|aligned\|>', '<\|misaligned\|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'debug-pt-conditional', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 8, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 10, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/3my099dp
MatiasTamayo/videomae-base-finetuned-ucf101-subset	MatiasTamayo	2022-11-29T14:58:07Z	1	0	transformers	[ "transformers", "pytorch", "tensorboard", "videomae", "video-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2022-11-29T14:52:15Z	--- license: cc-by-nc-4.0 tags: - generated_from_trainer model-index: - name: videomae-base-finetuned-ucf101-subset results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-ucf101-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 148 ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
KPEKEP/rugpt_chitchat	KPEKEP	2022-11-29T14:48:36Z	42	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "PyTorch", "Transformers", "ru", "license:unlicense", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-29T14:48:34Z	--- pipeline_tag: text-generation tags: - PyTorch - Transformers - gpt2 license: unlicense language: ru widget: - text: >- - У Джульетты было 7 пончиков, а потом она 3 съела. Сколько у нее осталось пончиков? - - text: >- - Поглажено 4 манула. Осталось погладить 6. Сколько всего манулов надо погладить? - - text: '- Для начала скажи, чему равно пятью девять? -' - text: '- ты чё такой борзый? -' - text: '- Привет! Как ваше ничего? -' duplicated_from: inkoziev/rugpt_chitchat --- ## Russian Chit-chat, Deductive and Common Sense reasoning model Модель является ядром прототипа [диалоговой системы](https://github.com/Koziev/chatbot) с двумя основными функциями. Первая функция - генерация реплик чит-чата. В качестве затравки подается история диалога (предшествующие несколько реплик, от 1 до 10). ``` - Привет, как дела? - Привет, так себе. - <<< эту реплику ожидаем от модели >>> ``` Вторая функция модели - вывод ответа на заданный вопрос, опираясь на дополнительные факты или на "здравый смысл". Предполагается, что релевантные факты извлекаются из стороннего хранилища (базы знаний) с помощью другой модели, например [sbert_pq](https://huggingface.co/inkoziev/sbert_pq). Используя указанный факт(ы) и текст вопроса, модель построит грамматичный и максимально краткий ответ, как это сделал бы человек в подобной коммуникативной ситуации. Релевантные факты следует указывать перед текстом заданного вопроса так, будто сам собеседник сказал их: ``` - Сегодня 15 сентября. Какой сейчас у нас месяц? - Сентябрь ``` Модель не ожидает, что все найденные и добавленные в контекст диалога факты действительно имеют отношение к заданному вопросу. Поэтому модель, извлекающая из базы знаний информацию, может жертвовать точностью в пользу полноте и добавлять что-то лишнее. Модель читчата в этом случае сама выберет среди добавленных в контекст фактов необходимую фактуру и проигнорирует лишнее. Текущая версия модели допускает до 5 фактов перед вопросом. Например: ``` - Стасу 16 лет. Стас живет в Подольске. У Стаса нет своей машины. Где живет Стас? - в Подольске ``` В некоторых случаях модель может выполнять силлогический вывод ответа, опираясь на 2 предпосылки, связанные друг с другом. Выводимое из двух предпосылок следствие не фигурирует явно, а как бы используется для вывода ответа: ``` - Смертен ли Аристофан, если он был греческим философом, а все философы смертны? - Да ``` Как можно видеть из приведенных примеров, формат подаваемой на вход модели фактической информации для выполнения вывода предельно естественный и свободный. Кроме логического вывода, модель также умеет решать простые арифметические задачи в рамках 1-2 классов начальной школы, с двумя числовыми аргументами: ``` - Чему равно 2+8? - 10 ``` ### Варианты модели и метрики Выложенная на данный момент модель имеет 760 млн. параметров, т.е. уровня sberbank-ai/rugpt3large_based_on_gpt2. Далее приводится результат замера точности решения арифметических задач на отложенном тестовом наборе сэмплов: \| base model \| arith. accuracy \| \| --------------------------------------- \| --------------- \| \| sberbank-ai/rugpt3large_based_on_gpt2 \| 0.91 \| \| sberbank-ai/rugpt3medium_based_on_gpt2 \| 0.70 \| \| sberbank-ai/rugpt3small_based_on_gpt2 \| 0.58 \| \| tinkoff-ai/ruDialoGPT-small \| 0.44 \| \| tinkoff-ai/ruDialoGPT-medium \| 0.69 \| Цифра 0.91 в столбце "arith. accuracy" означает, что 91% тестовых задач решено полностью верно. Любое отклонение сгенерированного ответа от эталонного рассматривается как ошибка. Например, выдача ответа "120" вместо "119" тоже фиксируется как ошибка. ### Пример использования ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM device = "cuda" if torch.cuda.is_available() else "cpu" model_name = "inkoziev/rugpt_chitchat" tokenizer = AutoTokenizer.from_pretrained(model_name) tokenizer.add_special_tokens({'bos_token': '<s>', 'eos_token': '</s>', 'pad_token': '<pad>'}) model = AutoModelForCausalLM.from_pretrained(model_name) model.to(device) model.eval() # На вход модели подаем последние 2-3 реплики диалога. Каждая реплика на отдельной строке, начинается с символа "-" input_text = """<s>- Привет! Что делаешь? - Привет :) В такси еду -""" encoded_prompt = tokenizer.encode(input_text, add_special_tokens=False, return_tensors="pt").to(device) output_sequences = model.generate(input_ids=encoded_prompt, max_length=100, num_return_sequences=1, pad_token_id=tokenizer.pad_token_id) text = tokenizer.decode(output_sequences[0].tolist(), clean_up_tokenization_spaces=True)[len(input_text)+1:] text = text[: text.find('</s>')] print(text) ``` ### Контакты Если у Вас есть какие-то вопросы по использованию этой модели, или предложения по ее улучшению - пишите мне mentalcomputing@gmail.com ### Citation: ``` @MISC{rugpt_chitchat, author = {Ilya Koziev}, title = {Russian Chit-chat with Common sence Reasoning}, url = {https://huggingface.co/inkoziev/rugpt_chitchat}, year = 2022 } ```
mzhou08/t5-base-finetuned-qg-medium-hard-qns	mzhou08	2022-11-29T14:09:43Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-29T13:37:37Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-base-finetuned-qg-medium-hard-qns results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-finetuned-qg-medium-hard-qns This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5919 - Rouge1: 38.6117 - Rouge2: 21.3082 - Rougel: 35.7294 - Rougelsum: 35.4192 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\| \| No log \| 1.0 \| 73 \| 1.8640 \| 31.2085 \| 11.6418 \| 26.1137 \| 26.2911 \| \| No log \| 2.0 \| 146 \| 1.6488 \| 29.6798 \| 10.9223 \| 26.7442 \| 26.9736 \| \| No log \| 3.0 \| 219 \| 1.6045 \| 33.6703 \| 11.7038 \| 30.167 \| 29.9192 \| \| No log \| 4.0 \| 292 \| 1.5812 \| 36.6758 \| 17.748 \| 33.739 \| 33.4974 \| \| No log \| 5.0 \| 365 \| 1.5879 \| 33.3704 \| 16.4099 \| 31.7658 \| 31.3874 \| \| No log \| 6.0 \| 438 \| 1.5786 \| 34.1216 \| 14.9588 \| 30.9584 \| 30.9277 \| \| 1.7533 \| 7.0 \| 511 \| 1.5804 \| 34.8267 \| 15.7046 \| 32.0877 \| 31.9317 \| \| 1.7533 \| 8.0 \| 584 \| 1.5861 \| 33.2539 \| 12.728 \| 30.551 \| 30.2299 \| \| 1.7533 \| 9.0 \| 657 \| 1.5911 \| 38.4406 \| 20.5922 \| 36.4267 \| 36.0426 \| \| 1.7533 \| 10.0 \| 730 \| 1.5827 \| 33.3421 \| 16.0455 \| 29.974 \| 29.5357 \| \| 1.7533 \| 11.0 \| 803 \| 1.5834 \| 42.3363 \| 24.6712 \| 40.4291 \| 40.0842 \| \| 1.7533 \| 12.0 \| 876 \| 1.5889 \| 33.268 \| 15.5319 \| 30.6942 \| 30.4347 \| \| 1.7533 \| 13.0 \| 949 \| 1.5911 \| 42.1265 \| 23.1983 \| 39.5768 \| 39.2304 \| \| 1.2341 \| 14.0 \| 1022 \| 1.5926 \| 35.0279 \| 15.825 \| 32.0736 \| 32.0093 \| \| 1.2341 \| 15.0 \| 1095 \| 1.5912 \| 38.362 \| 17.6108 \| 35.3148 \| 35.0558 \| \| 1.2341 \| 16.0 \| 1168 \| 1.5919 \| 38.6117 \| 21.3082 \| 35.7294 \| 35.4192 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
Samael98/roberta-base-bne-finetuned-amazon_reviews_multi	Samael98	2022-11-29T14:08:39Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-29T13:46:19Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: roberta-base-bne-finetuned-amazon_reviews_multi results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: train args: es metrics: - name: Accuracy type: accuracy value: 0.93375 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-bne-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2313 - Accuracy: 0.9337 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1864 \| 1.0 \| 1250 \| 0.2209 \| 0.9317 \| \| 0.1063 \| 2.0 \| 2500 \| 0.2313 \| 0.9337 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
oskrmiguel/roberta-base-bne-clasificacion-de-texto-supervisado	oskrmiguel	2022-11-29T14:05:15Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-29T13:42:24Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: roberta-base-bne-clasificacion-de-texto-supervisado results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: train args: es metrics: - name: Accuracy type: accuracy value: 0.9335 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-bne-clasificacion-de-texto-supervisado This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2263 - Accuracy: 0.9335 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1934 \| 1.0 \| 1250 \| 0.1700 \| 0.9327 \| \| 0.1031 \| 2.0 \| 2500 \| 0.2263 \| 0.9335 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
yesyesjaewook/jets-jaewook-ukten-ko	yesyesjaewook	2022-11-29T14:02:17Z	6	0	espnet	[ "espnet", "audio", "text-to-speech", "ko", "dataset:Jaewook-Ukten", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]	text-to-speech	2022-11-27T13:55:18Z	--- tags: - espnet - audio - text-to-speech language: ko datasets: - Jaewook-Ukten license: cc-by-4.0 --- ## ESPnet2 TTS model ### yesyesjaewook/jets-jaewook-ukten-ko This model was trained by yesyesjaewook using jaewook_ukten recipe in [espnet](https://github.com/espnet/espnet/). ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
thliang01/sd-class-butterflies-32	thliang01	2022-11-29T13:42:36Z	35	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T13:42:21Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(thliang01/sd-class-butterflies-32) image = pipeline().images[0] image ```
kaizerkam/sd-class-comics-64	kaizerkam	2022-11-29T13:26:50Z	30	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T13:25:39Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of comic scenes. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(kaizerkam/sd-class-comics-64) image = pipeline().images[0] image ```
shivammehta25/sd-class-butterflies-32	shivammehta25	2022-11-29T12:46:27Z	35	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T12:46:12Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(shivammehta007/sd-class-butterflies-32) image = pipeline().images[0] image ```
pig4431/rtm_roBERTa_5E	pig4431	2022-11-29T12:34:52Z	104	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:rotten_tomatoes", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-29T11:02:18Z	--- license: mit tags: - generated_from_trainer datasets: - rotten_tomatoes metrics: - accuracy model-index: - name: rtm_roBERTa_5E results: - task: name: Text Classification type: text-classification dataset: name: rotten_tomatoes type: rotten_tomatoes config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8666666666666667 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # rtm_roBERTa_5E This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the rotten_tomatoes dataset. It achieves the following results on the evaluation set: - Loss: 0.6545 - Accuracy: 0.8667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6955 \| 0.09 \| 50 \| 0.6752 \| 0.7867 \| \| 0.5362 \| 0.19 \| 100 \| 0.4314 \| 0.8333 \| \| 0.4065 \| 0.28 \| 150 \| 0.4476 \| 0.8533 \| \| 0.3563 \| 0.37 \| 200 \| 0.3454 \| 0.8467 \| \| 0.3729 \| 0.47 \| 250 \| 0.3421 \| 0.86 \| \| 0.3355 \| 0.56 \| 300 \| 0.3253 \| 0.8467 \| \| 0.338 \| 0.66 \| 350 \| 0.3859 \| 0.8733 \| \| 0.2875 \| 0.75 \| 400 \| 0.3537 \| 0.8533 \| \| 0.3477 \| 0.84 \| 450 \| 0.3636 \| 0.8467 \| \| 0.3259 \| 0.94 \| 500 \| 0.3115 \| 0.88 \| \| 0.3204 \| 1.03 \| 550 \| 0.4295 \| 0.8333 \| \| 0.2673 \| 1.12 \| 600 \| 0.3369 \| 0.88 \| \| 0.2479 \| 1.22 \| 650 \| 0.3620 \| 0.8667 \| \| 0.2821 \| 1.31 \| 700 \| 0.3582 \| 0.8733 \| \| 0.2355 \| 1.4 \| 750 \| 0.3130 \| 0.8867 \| \| 0.2357 \| 1.5 \| 800 \| 0.3229 \| 0.86 \| \| 0.2725 \| 1.59 \| 850 \| 0.3035 \| 0.88 \| \| 0.2425 \| 1.69 \| 900 \| 0.3146 \| 0.8533 \| \| 0.1977 \| 1.78 \| 950 \| 0.4079 \| 0.86 \| \| 0.2557 \| 1.87 \| 1000 \| 0.4132 \| 0.8733 \| \| 0.2395 \| 1.97 \| 1050 \| 0.3336 \| 0.86 \| \| 0.1951 \| 2.06 \| 1100 \| 0.5068 \| 0.84 \| \| 0.1631 \| 2.15 \| 1150 \| 0.5209 \| 0.8867 \| \| 0.2192 \| 2.25 \| 1200 \| 0.4766 \| 0.8733 \| \| 0.1725 \| 2.34 \| 1250 \| 0.3962 \| 0.8667 \| \| 0.2215 \| 2.43 \| 1300 \| 0.4133 \| 0.8867 \| \| 0.1602 \| 2.53 \| 1350 \| 0.5564 \| 0.8533 \| \| 0.1986 \| 2.62 \| 1400 \| 0.5826 \| 0.86 \| \| 0.1972 \| 2.72 \| 1450 \| 0.5412 \| 0.8667 \| \| 0.2299 \| 2.81 \| 1500 \| 0.4636 \| 0.8733 \| \| 0.2028 \| 2.9 \| 1550 \| 0.5096 \| 0.8667 \| \| 0.2591 \| 3.0 \| 1600 \| 0.3790 \| 0.8467 \| \| 0.1197 \| 3.09 \| 1650 \| 0.5704 \| 0.8467 \| \| 0.174 \| 3.18 \| 1700 \| 0.5904 \| 0.8467 \| \| 0.1499 \| 3.28 \| 1750 \| 0.6066 \| 0.86 \| \| 0.1687 \| 3.37 \| 1800 \| 0.6353 \| 0.8533 \| \| 0.1463 \| 3.46 \| 1850 \| 0.6434 \| 0.8467 \| \| 0.1373 \| 3.56 \| 1900 \| 0.6507 \| 0.8533 \| \| 0.1339 \| 3.65 \| 1950 \| 0.6014 \| 0.86 \| \| 0.1488 \| 3.75 \| 2000 \| 0.7245 \| 0.84 \| \| 0.1725 \| 3.84 \| 2050 \| 0.6214 \| 0.86 \| \| 0.1443 \| 3.93 \| 2100 \| 0.6446 \| 0.8533 \| \| 0.1619 \| 4.03 \| 2150 \| 0.6223 \| 0.8533 \| \| 0.1153 \| 4.12 \| 2200 \| 0.6579 \| 0.8333 \| \| 0.1159 \| 4.21 \| 2250 \| 0.6760 \| 0.8667 \| \| 0.0948 \| 4.31 \| 2300 \| 0.7172 \| 0.8467 \| \| 0.1373 \| 4.4 \| 2350 \| 0.7346 \| 0.8467 \| \| 0.1463 \| 4.49 \| 2400 \| 0.6453 \| 0.8533 \| \| 0.0758 \| 4.59 \| 2450 \| 0.6579 \| 0.86 \| \| 0.16 \| 4.68 \| 2500 \| 0.6556 \| 0.8667 \| \| 0.112 \| 4.78 \| 2550 \| 0.6490 \| 0.88 \| \| 0.1151 \| 4.87 \| 2600 \| 0.6525 \| 0.8667 \| \| 0.2152 \| 4.96 \| 2650 \| 0.6545 \| 0.8667 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2
AlekseyKorshuk/125m-dalio-book-handwritten-io-constant-1e-6-v2	AlekseyKorshuk	2022-11-29T12:29:49Z	125	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "dataset:AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2", "license:other", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-29T10:31:18Z	--- license: other tags: - generated_from_trainer datasets: - AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 metrics: - accuracy model-index: - name: 125m-dalio-book-handwritten-io-constant-1e-6-v2 results: - task: name: Causal Language Modeling type: text-generation dataset: name: AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 type: AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 metrics: - name: Accuracy type: accuracy value: 0.23359387091781458 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 125m-dalio-book-handwritten-io-constant-1e-6-v2 This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 dataset. It achieves the following results on the evaluation set: - Loss: 3.0859 - Accuracy: 0.2336 - Perplexity: 21.8880 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 1.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Perplexity \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\| \| 3.3352 \| 0.01 \| 1 \| 3.1738 \| 0.2305 \| 23.8988 \| \| 3.3091 \| 0.03 \| 2 \| 3.1738 \| 0.2305 \| 23.8988 \| \| 3.3347 \| 0.04 \| 3 \| 3.1738 \| 0.2305 \| 23.8988 \| \| 3.1445 \| 0.05 \| 4 \| 3.1738 \| 0.2305 \| 23.8988 \| \| 2.8918 \| 0.07 \| 5 \| 3.1738 \| 0.2305 \| 23.8988 \| \| 3.2068 \| 0.08 \| 6 \| 3.1738 \| 0.2305 \| 23.8988 \| \| 3.6245 \| 0.09 \| 7 \| 3.1719 \| 0.2305 \| 23.8522 \| \| 3.2256 \| 0.11 \| 8 \| 3.1719 \| 0.2305 \| 23.8522 \| \| 2.9991 \| 0.12 \| 9 \| 3.1699 \| 0.2305 \| 23.8056 \| \| 3.3257 \| 0.13 \| 10 \| 3.1680 \| 0.2306 \| 23.7592 \| \| 3.1199 \| 0.15 \| 11 \| 3.1660 \| 0.2306 \| 23.7128 \| \| 3.3735 \| 0.16 \| 12 \| 3.1660 \| 0.2306 \| 23.7128 \| \| 3.0051 \| 0.17 \| 13 \| 3.1641 \| 0.2307 \| 23.6665 \| \| 3.2695 \| 0.19 \| 14 \| 3.1621 \| 0.2308 \| 23.6204 \| \| 3.2004 \| 0.2 \| 15 \| 3.1602 \| 0.2309 \| 23.5743 \| \| 3.2075 \| 0.21 \| 16 \| 3.1582 \| 0.2308 \| 23.5283 \| \| 3.321 \| 0.23 \| 17 \| 3.1562 \| 0.2308 \| 23.4824 \| \| 3.4026 \| 0.24 \| 18 \| 3.1543 \| 0.2309 \| 23.4366 \| \| 3.0383 \| 0.25 \| 19 \| 3.1523 \| 0.2309 \| 23.3908 \| \| 3.166 \| 0.27 \| 20 \| 3.1504 \| 0.2309 \| 23.3452 \| \| 3.144 \| 0.28 \| 21 \| 3.1484 \| 0.2310 \| 23.2996 \| \| 3.1624 \| 0.29 \| 22 \| 3.1484 \| 0.2310 \| 23.2996 \| \| 3.0332 \| 0.31 \| 23 \| 3.1465 \| 0.2310 \| 23.2542 \| \| 3.3745 \| 0.32 \| 24 \| 3.1445 \| 0.2311 \| 23.2088 \| \| 3.0823 \| 0.33 \| 25 \| 3.1426 \| 0.2312 \| 23.1635 \| \| 3.6021 \| 0.35 \| 26 \| 3.1406 \| 0.2312 \| 23.1183 \| \| 3.1125 \| 0.36 \| 27 \| 3.1387 \| 0.2313 \| 23.0732 \| \| 3.1406 \| 0.37 \| 28 \| 3.1387 \| 0.2314 \| 23.0732 \| \| 3.1736 \| 0.39 \| 29 \| 3.1367 \| 0.2314 \| 23.0282 \| \| 3.1104 \| 0.4 \| 30 \| 3.1348 \| 0.2315 \| 22.9832 \| \| 3.1301 \| 0.41 \| 31 \| 3.1328 \| 0.2316 \| 22.9384 \| \| 3.3376 \| 0.43 \| 32 \| 3.1309 \| 0.2315 \| 22.8936 \| \| 3.218 \| 0.44 \| 33 \| 3.1309 \| 0.2316 \| 22.8936 \| \| 3.0786 \| 0.45 \| 34 \| 3.1289 \| 0.2316 \| 22.8490 \| \| 3.0125 \| 0.47 \| 35 \| 3.1270 \| 0.2317 \| 22.8044 \| \| 3.2634 \| 0.48 \| 36 \| 3.1270 \| 0.2317 \| 22.8044 \| \| 2.9888 \| 0.49 \| 37 \| 3.125 \| 0.2318 \| 22.7599 \| \| 3.1624 \| 0.51 \| 38 \| 3.1230 \| 0.2318 \| 22.7155 \| \| 2.9807 \| 0.52 \| 39 \| 3.1211 \| 0.2319 \| 22.6712 \| \| 3.446 \| 0.53 \| 40 \| 3.1211 \| 0.2319 \| 22.6712 \| \| 3.1338 \| 0.55 \| 41 \| 3.1191 \| 0.2320 \| 22.6269 \| \| 3.1841 \| 0.56 \| 42 \| 3.1191 \| 0.2320 \| 22.6269 \| \| 3.1079 \| 0.57 \| 43 \| 3.1172 \| 0.2320 \| 22.5828 \| \| 3.0918 \| 0.59 \| 44 \| 3.1152 \| 0.2321 \| 22.5387 \| \| 3.0302 \| 0.6 \| 45 \| 3.1152 \| 0.2322 \| 22.5387 \| \| 3.1123 \| 0.61 \| 46 \| 3.1133 \| 0.2323 \| 22.4947 \| \| 2.9985 \| 0.63 \| 47 \| 3.1113 \| 0.2324 \| 22.4508 \| \| 3.3816 \| 0.64 \| 48 \| 3.1113 \| 0.2324 \| 22.4508 \| \| 3.0813 \| 0.65 \| 49 \| 3.1094 \| 0.2324 \| 22.4070 \| \| 3.2024 \| 0.67 \| 50 \| 3.1094 \| 0.2325 \| 22.4070 \| \| 3.0178 \| 0.68 \| 51 \| 3.1074 \| 0.2325 \| 22.3633 \| \| 3.1646 \| 0.69 \| 52 \| 3.1074 \| 0.2326 \| 22.3633 \| \| 3.0046 \| 0.71 \| 53 \| 3.1055 \| 0.2327 \| 22.3197 \| \| 3.0266 \| 0.72 \| 54 \| 3.1055 \| 0.2327 \| 22.3197 \| \| 3.3857 \| 0.73 \| 55 \| 3.1035 \| 0.2327 \| 22.2761 \| \| 3.064 \| 0.75 \| 56 \| 3.1035 \| 0.2328 \| 22.2761 \| \| 3.176 \| 0.76 \| 57 \| 3.1016 \| 0.2328 \| 22.2327 \| \| 3.1851 \| 0.77 \| 58 \| 3.1016 \| 0.2329 \| 22.2327 \| \| 3.0811 \| 0.79 \| 59 \| 3.0996 \| 0.2329 \| 22.1893 \| \| 3.0205 \| 0.8 \| 60 \| 3.0996 \| 0.2330 \| 22.1893 \| \| 3.26 \| 0.81 \| 61 \| 3.0977 \| 0.2330 \| 22.1460 \| \| 3.2922 \| 0.83 \| 62 \| 3.0977 \| 0.2331 \| 22.1460 \| \| 3.5349 \| 0.84 \| 63 \| 3.0957 \| 0.2331 \| 22.1028 \| \| 3.3525 \| 0.85 \| 64 \| 3.0957 \| 0.2331 \| 22.1028 \| \| 3.135 \| 0.87 \| 65 \| 3.0938 \| 0.2331 \| 22.0596 \| \| 3.1707 \| 0.88 \| 66 \| 3.0938 \| 0.2332 \| 22.0596 \| \| 3.0127 \| 0.89 \| 67 \| 3.0918 \| 0.2332 \| 22.0166 \| \| 3.0952 \| 0.91 \| 68 \| 3.0918 \| 0.2332 \| 22.0166 \| \| 3.1023 \| 0.92 \| 69 \| 3.0898 \| 0.2334 \| 21.9736 \| \| 3.3821 \| 0.93 \| 70 \| 3.0898 \| 0.2334 \| 21.9736 \| \| 3.1118 \| 0.95 \| 71 \| 3.0879 \| 0.2334 \| 21.9308 \| \| 3.1143 \| 0.96 \| 72 \| 3.0879 \| 0.2335 \| 21.9308 \| \| 3.1118 \| 0.97 \| 73 \| 3.0879 \| 0.2335 \| 21.9308 \| \| 3.0596 \| 0.99 \| 74 \| 3.0859 \| 0.2336 \| 21.8880 \| \| 3.1033 \| 1.0 \| 75 \| 3.0859 \| 0.2336 \| 21.8880 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
nlp-tlp/mwo-ner	nlp-tlp	2022-11-29T12:00:39Z	4	3	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "en", "dataset:mwo_ner", "region:us" ]	token-classification	2022-11-29T11:58:19Z	--- tags: - flair - token-classification - sequence-tagger-model language: en datasets: - mwo_ner widget: - text: "replace seal on pump" --- ## MWO NER Test A flair-based NER model for MWOs. There are three classes: `Item`, `Activity`, and `Observation`.
clp/segformer-b0-scene-parse-150	clp	2022-11-29T11:47:29Z	46	0	transformers	[ "transformers", "pytorch", "tensorboard", "segformer", "generated_from_trainer", "dataset:scene_parse_150", "license:other", "endpoints_compatible", "region:us" ]	null	2022-11-28T15:53:44Z	--- license: other tags: - generated_from_trainer datasets: - scene_parse_150 model-index: - name: segformer-b0-scene-parse-150 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-scene-parse-150 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset. It achieves the following results on the evaluation set: - Loss: 2.3118 - Mean Iou: 0.0859 - Mean Accuracy: 0.1493 - Overall Accuracy: 0.5430 - Per Category Iou: [0.4898642376841085, 0.502026813829342, 0.9487341030299479, 0.44331193050176815, 0.28502594514455154, 0.5132976114794296, 0.8390207156308851, 0.0, 0.30530825819472024, 0.0, 0.06594624784212842, 0.0, 0.03397963180571876, 0.0, 0.0007459827819109256, 0.0, nan, 0.04554975143210437, 0.0, 0.07792795056021705, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] - Per Category Accuracy: [0.8215553632658342, 0.819071257846768, 0.9731147245348802, 0.8672811704363634, 0.9004683840749415, 0.594073476114797, 0.9732440887086908, 0.0, 0.40956851614311834, 0.0, 0.5229850345614389, 0.0, 0.034648027958062905, nan, 0.0007464041475862904, 0.0, nan, 0.0476077438413251, 0.0, 0.5009150608246313, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Per Category Iou \| Per Category Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:\| \| 4.2849 \| 1.0 \| 20 \| 4.2070 \| 0.0194 \| 0.0679 \| 0.3746 \| [0.3949829725229674, 0.4135772915291814, 0.0, 0.26980840849544657, 0.1282559786684443, 0.15076540186066723, 0.00908901592761032, 0.0, 0.013565775517419566, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0003970617431010522, 0.008885447023579041, 0.0, 0.0, 0.0, 0.005040122024006897, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.002655312914892643, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan] \| [0.9524952286989713, 0.755535418800725, 0.0, 0.48244304323326054, 0.9011709601873537, 0.16045676614279827, 0.011822582618269517, 0.0, 0.013613165579542043, 0.0, 0.0, 0.0, 0.0, nan, 0.0004034617013979948, 0.07362999240057418, nan, 0.0, 0.0, 0.04090860157175153, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0034295175023651846, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] \| \| 3.7699 \| 2.0 \| 40 \| 3.9727 \| 0.0380 \| 0.1002 \| 0.4224 \| 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0.4695 \| [0.46147636301683304, 0.42190388170055454, 0.5905298672673311, 0.34255470866251286, 0.10127362853686882, 0.2992112324204293, 0.616176968407381, 0.0, 0.031915928772988225, 0.0, 0.05093061049274829, 0.0, 0.0, 0.0, 0.006854943777434808, 0.021351935880581825, nan, 0.0002887139107611549, 0.0, 0.052485352485352486, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] \| [0.9419053665006146, 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nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] \| [0.8282109238532703, 0.777104509757571, 0.9738961858675723, 0.8748329035097461, 0.9275175644028103, 0.5860809094960605, 0.9789500358260079, 0.0, 0.39157228241587294, 0.0, 0.4907053217904839, 0.0, 0.06380429355966051, nan, 0.00018155776562909766, 0.0, nan, 0.043229413936804344, 0.0, 0.5262138012703197, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] \| \| 1.3671 \| 50.0 \| 1000 \| 2.3118 \| 0.0859 \| 0.1493 \| 0.5430 \| [0.4898642376841085, 0.502026813829342, 0.9487341030299479, 0.44331193050176815, 0.28502594514455154, 0.5132976114794296, 0.8390207156308851, 0.0, 0.30530825819472024, 0.0, 0.06594624784212842, 0.0, 0.03397963180571876, 0.0, 0.0007459827819109256, 0.0, nan, 0.04554975143210437, 0.0, 0.07792795056021705, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] \| [0.8215553632658342, 0.819071257846768, 0.9731147245348802, 0.8672811704363634, 0.9004683840749415, 0.594073476114797, 0.9732440887086908, 0.0, 0.40956851614311834, 0.0, 0.5229850345614389, 0.0, 0.034648027958062905, nan, 0.0007464041475862904, 0.0, nan, 0.0476077438413251, 0.0, 0.5009150608246313, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
autoevaluate/binary-classification-not-evaluated	autoevaluate	2022-11-29T11:07:52Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-29T11:01:03Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # binary-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3009 - Accuracy: 0.8968 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.175 \| 1.0 \| 4210 \| 0.3009 \| 0.8968 \| ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
supermy/poetry	supermy	2022-11-29T10:53:20Z	115	2	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "zh", "dataset:poetry", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-29T00:56:18Z	--- language: zh datasets: poetry inference: parameters: max_length: 108 num_return_sequences: 1 do_sample: True widget: - text: "物换星移几度秋" example_title: "滕王阁1" - text: "秋水共长天一色" example_title: "滕王阁 2" - text: "萍水相逢，尽是他乡之客。" example_title: "滕王阁 3" --- # 古诗词 ## Model description 古诗词AI生成 ## How to use 使用 pipeline 调用模型: ```python from transformers import AutoTokenizer, GPT2LMHeadModel, TextGenerationPipeline model_checkpoint = "supermy/poetry" tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) model = GPT2LMHeadModel.from_pretrained(model_checkpoint) text_generator = TextGenerationPipeline(model, tokenizer) text_generator.model.config.pad_token_id = text_generator.model.config.eos_token_id print(text_generator("举头望明月，", max_length=100, do_sample=True)) print(text_generator("物换星移几度秋，", max_length=100, do_sample=True)) >>> print(text_generator("举头望明月，", max_length=100, do_sample=True)) [{'generated_text': '举头望明月，何以喻无言。顾影若为舞，啸风清独伤。四时别有意，千古得从容。赏音我非此，何如鸥鹭群。崎山有佳色，落落样相宜。不嫌雪霜温，宁受四时肥。老态如偷面，冬心似相知。春风不可恃，触动春何为。岁晚忽然老，花前岁月深。可笑一场梦，婵娟乍自心。列名多岁月，森列尽林峦。试问影非笑'}] >>> print(text_generator("物换星移几度秋，", max_length=100, do_sample=True)) [{'generated_text': '物换星移几度秋，消长随时向一丘。渔者下逢勾漏令，漏声高出景阳丘。天津大尹昔从游，大尹来时春复秋。旗鼓日严宣使从，联镳歌笑又风流。冈峦比并瑶溪水，叠嶂高盘黼黻洲。花木芳菲三月天，莺花暖翠几流年。一从别后多携手，肠断酒阑怀凛然。北阙人称似梦中，西山别样梦魂香。多君观国亲圭璧，能预陇西称巨良。刷羽刷羽'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("supermy/poetry") model = AutoModelForCausalLM.from_pretrained("supermy/poetry") ``` ## Training data 非常全的古诗词数据，收录了从先秦到现代的共计85万余首古诗词。 ## 统计信息 \| 朝代 \| 诗词数 \| 作者数 \| \|-----------------------\|--------\|--------\| \| 宋 \| 287114 \| 9446 \| \| 明 \| 236957 \| 4439 \| \| 清 \| 90089 \| 8872 \| \| 唐 \| 49195 \| 2736 \| \| 元 \| 37375 \| 1209 \| \| 近现代 \| 28419 \| 790 \| \| 当代 \| 28219 \| 177 \| \| 明末清初 \| 17700 \| 176 \| \| 元末明初 \| 15736 \| 79 \| \| 清末民国初 \| 15367 \| 99 \| \| 清末近现代初 \| 12464 \| 48 \| \| 宋末元初 \| 12058 \| 41 \| \| 南北朝 \| 4586 \| 434 \| \| 近现代末当代初 \| 3426 \| 23 \| \| 魏晋 \| 3020 \| 251 \| \| 金末元初 \| 3019 \| 17 \| \| 金 \| 2741 \| 253 \| \| 民国末当代初 \| 1948 \| 9 \| \| 隋 \| 1170 \| 84 \| \| 唐末宋初 \| 1118 \| 44 \| \| 先秦 \| 570 \| 8 \| \| 隋末唐初 \| 472 \| 40 \| \| 汉 \| 363 \| 83 \| \| 宋末金初 \| 234 \| 9 \| \| 辽 \| 22 \| 7 \| \| 秦 \| 2 \| 2 \| \| 魏晋末南北朝初 \| 1 \| 1 \| \| 总和 \| 853385 \| 29377 \| ``` ``` ## Training procedure 模型：[GPT2](https://huggingface.co/gpt2) 训练环境：英伟达16G显卡 bpe分词："vocab_size"=50000 ``` *** Running training * Num examples = 16431 Num Epochs = 680 Instantaneous batch size per device = 24 Total train batch size (w. parallel, distributed & accumulation) = 192 Gradient Accumulation steps = 8 Total optimization steps = 57800 Number of trainable parameters = 124242432 GPT-2 size: 124.2M parameters 0%\| \| 0/57800 [00:00<?, ?it/s]You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding. 9%\|▊ \| 5000/57800 [6:58:57<72:53:18, 4.97s/it]* Running Evaluation * Num examples = 1755 Batch size = 24 {'loss': 3.1345, 'learning_rate': 0.0004939065828881268, 'epoch': 58.82} 9%\|▊ \| 5000/57800 [6:59:14<72:53:18, Saving model checkpoint to poetry-trainer/checkpoint-5000 Configuration saved in poetry-trainer/checkpoint-5000/config.json Model weights saved in poetry-trainer/checkpoint-5000/pytorch_model.bin tokenizer config file saved in poetry-trainer/checkpoint-5000/tokenizer_config.json Special tokens file saved in poetry-trainer/checkpoint-5000/special_tokens_map.json 17%\|█▋ \| 10000/57800 [13:55:32<65:40:41, 4.95s/it]* Running Evaluation * Num examples = 1755 Batch size = 24 {'eval_loss': 11.14090633392334, 'eval_runtime': 16.8326, 'eval_samples_per_second': 104.262, 'eval_steps_per_second': 4.396, 'epoch': 58.82} {'loss': 0.2511, 'learning_rate': 0.00046966687938531824, 'epoch': 117.64} 17%\|█▋ \| 10000/57800 [13:55:48<65:40:41Saving model checkpoint to poetry-trainer/checkpoint-10000 .......... 95%\|█████████▌\| 55000/57800 [76:06:46<3:59:33, 5.13s/it]* Running Evaluation *** Num examples = 1755 Batch size = 24 {'eval_loss': 14.860174179077148, 'eval_runtime': 16.7826, 'eval_samples_per_second': 104.572, 'eval_steps_per_second': 4.409, 'epoch': 588.23} {'loss': 0.0083, 'learning_rate': 3.0262183266589473e-06, 'epoch': 647.06} 95%\|█████████▌\| 55000/57800 [76:07:03<3:59:33,Saving model checkpoint to poetry-trainer/checkpoint-55000 {'eval_loss': 14.830656051635742, 'eval_runtime': 16.7365, 'eval_samples_per_second': 104.86, 'eval_steps_per_second': 4.421, 'epoch': 647.06} {'train_runtime': 287920.5857, 'train_samples_per_second': 38.806, 'train_steps_per_second': 0.201, 'train_loss': 0.33751299874592816, 'epoch': 679.99} 100%\|██████████\| 57800/57800 [79:58:40<00:00, 4.93s/it] ``` ``` ### entry and citation info ``` ```
huggingtweets/mullen_usa-nasdaq	huggingtweets	2022-11-29T10:30:31Z	117	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-29T10:24:49Z	--- language: en thumbnail: http://www.huggingtweets.com/mullen_usa-nasdaq/1669717561312/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1521140484512620544/Ev6EIPlD_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1433904015834705921/tRPvxdFF_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Nasdaq & Mullen Automotive</div> <div style="text-align: center; font-size: 14px;">@mullen_usa-nasdaq</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Nasdaq & Mullen Automotive. \| Data \| Nasdaq \| Mullen Automotive \| \| --- \| --- \| --- \| \| Tweets downloaded \| 3250 \| 963 \| \| Retweets \| 663 \| 188 \| \| Short tweets \| 31 \| 121 \| \| Tweets kept \| 2556 \| 654 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/352xmu00/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mullen_usa-nasdaq's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/x3hx0rfr) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/x3hx0rfr/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/mullen_usa-nasdaq') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
LuisQ/LuisQ_sd-class-butterflies-32	LuisQ	2022-11-29T10:18:33Z	35	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T16:17:40Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(LuisQ/LuisQ_sd-class-butterflies-32) image = pipeline().images[0] image ```
SayaEndo/distilbert-base-uncased-finetuned-squad-d5716d28	SayaEndo	2022-11-29T08:56:00Z	107	0	transformers	[ "transformers", "pytorch", "distilbert", "fill-mask", "question-answering", "en", "dataset:squad", "arxiv:1910.01108", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	question-answering	2022-11-29T08:44:02Z	--- language: - en thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg tags: - question-answering license: apache-2.0 datasets: - squad metrics: - squad --- # DistilBERT with a second step of distillation ## Model description This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation. In this version, the following pre-trained models were used: * Student: `distilbert-base-uncased` * Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1` ## Training data This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows: ```python from datasets import load_dataset squad = load_dataset('squad') ``` ## Training procedure ## Eval results \| \| Exact Match \| F1 \| \|------------------\|-------------\|------\| \| DistilBERT paper \| 79.1 \| 86.9 \| \| Ours \| 78.4 \| 86.5 \| The scores were calculated using the `squad` metric from `datasets`. ### BibTeX entry and citation info ```bibtex @misc{sanh2020distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, year={2020}, eprint={1910.01108}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
premsuresh/bart-finetuned-mathqa-moh	premsuresh	2022-11-29T08:42:54Z	172	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-29T08:24:19Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-finetuned-mathqa-moh results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-finetuned-mathqa-moh This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
thivy/t5-base-finetuned-en-to-no	thivy	2022-11-29T08:21:44Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:opus_books", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-22T16:16:18Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - opus_books metrics: - bleu model-index: - name: t5-base-finetuned-en-to-no results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus_books type: opus_books args: en-no metrics: - name: Bleu type: bleu value: 4.8513 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-finetuned-en-to-no This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the opus_books dataset. It achieves the following results on the evaluation set: - Loss: 2.9566 - Bleu: 4.8513 - Gen Len: 17.84 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 280 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:------:\|:-------:\| \| 3.3949 \| 1.0 \| 788 \| 2.7553 \| 0.9274 \| 18.1314 \| \| 2.8659 \| 2.0 \| 1576 \| 2.5367 \| 1.2755 \| 18.1543 \| \| 2.7244 \| 3.0 \| 2364 \| 2.3900 \| 1.6351 \| 18.0343 \| \| 2.5228 \| 4.0 \| 3152 \| 2.2902 \| 1.7125 \| 18.0543 \| \| 2.4201 \| 5.0 \| 3940 \| 2.2039 \| 1.7217 \| 18.0914 \| \| 2.3168 \| 6.0 \| 4728 \| 2.1429 \| 2.0474 \| 18.08 \| \| 2.1856 \| 7.0 \| 5516 \| 2.0772 \| 2.228 \| 18.0686 \| \| 2.12 \| 8.0 \| 6304 \| 2.0333 \| 2.1694 \| 17.98 \| \| 2.0519 \| 9.0 \| 7092 \| 1.9931 \| 2.257 \| 17.9914 \| \| 1.9856 \| 10.0 \| 7880 \| 1.9540 \| 2.489 \| 18.04 \| \| 1.9164 \| 11.0 \| 8668 \| 1.9266 \| 2.5762 \| 17.9629 \| \| 1.8864 \| 12.0 \| 9456 \| 1.9036 \| 2.8294 \| 17.9857 \| \| 1.8276 \| 13.0 \| 10244 \| 1.8695 \| 2.9018 \| 17.98 \| \| 1.7715 \| 14.0 \| 11032 \| 1.8584 \| 3.04 \| 17.9886 \| \| 1.7302 \| 15.0 \| 11820 \| 1.8487 \| 2.9588 \| 18.0057 \| \| 1.6768 \| 16.0 \| 12608 \| 1.8155 \| 3.1968 \| 17.9943 \| \| 1.6564 \| 17.0 \| 13396 \| 1.8137 \| 3.3315 \| 17.9657 \| \| 1.6039 \| 18.0 \| 14184 \| 1.7863 \| 3.4057 \| 18.0629 \| \| 1.5735 \| 19.0 \| 14972 \| 1.7945 \| 3.6905 \| 17.9571 \| \| 1.5319 \| 20.0 \| 15760 \| 1.7830 \| 3.5128 \| 17.9714 \| \| 1.4993 \| 21.0 \| 16548 \| 1.7745 \| 3.4125 \| 18.0057 \| \| 1.4622 \| 22.0 \| 17336 \| 1.7655 \| 3.3974 \| 17.9543 \| \| 1.448 \| 23.0 \| 18124 \| 1.7599 \| 3.75 \| 17.9057 \| \| 1.3995 \| 24.0 \| 18912 \| 1.7557 \| 3.6852 \| 17.8286 \| \| 1.373 \| 25.0 \| 19700 \| 1.7478 \| 3.5797 \| 17.9343 \| \| 1.3513 \| 26.0 \| 20488 \| 1.7558 \| 3.8526 \| 17.8457 \| \| 1.3291 \| 27.0 \| 21276 \| 1.7485 \| 3.7037 \| 17.9143 \| \| 1.3002 \| 28.0 \| 22064 \| 1.7480 \| 3.7433 \| 17.96 \| \| 1.2655 \| 29.0 \| 22852 \| 1.7578 \| 4.0584 \| 17.8914 \| \| 1.2354 \| 30.0 \| 23640 \| 1.7514 \| 4.2106 \| 17.8686 \| \| 1.2224 \| 31.0 \| 24428 \| 1.7576 \| 3.9906 \| 17.9 \| \| 1.1999 \| 32.0 \| 25216 \| 1.7627 \| 4.1242 \| 17.92 \| \| 1.1672 \| 33.0 \| 26004 \| 1.7548 \| 4.1584 \| 17.8286 \| \| 1.1547 \| 34.0 \| 26792 \| 1.7446 \| 4.1721 \| 17.8143 \| \| 1.1313 \| 35.0 \| 27580 \| 1.7613 \| 4.3958 \| 17.8457 \| \| 1.08 \| 36.0 \| 28368 \| 1.7628 \| 4.342 \| 17.8829 \| \| 1.0927 \| 37.0 \| 29156 \| 1.7685 \| 4.4468 \| 17.8971 \| \| 1.0751 \| 38.0 \| 29944 \| 1.7731 \| 4.4297 \| 17.8886 \| \| 1.0492 \| 39.0 \| 30732 \| 1.7641 \| 4.5174 \| 17.8714 \| \| 1.036 \| 40.0 \| 31520 \| 1.7643 \| 4.4578 \| 17.84 \| \| 1.0172 \| 41.0 \| 32308 \| 1.7820 \| 4.5795 \| 17.8429 \| \| 0.9966 \| 42.0 \| 33096 \| 1.7830 \| 4.3455 \| 17.8743 \| \| 0.9812 \| 43.0 \| 33884 \| 1.7890 \| 4.3988 \| 17.8486 \| \| 0.9624 \| 44.0 \| 34672 \| 1.7953 \| 4.5418 \| 17.8143 \| \| 0.9485 \| 45.0 \| 35460 \| 1.8046 \| 4.5402 \| 17.8143 \| \| 0.9383 \| 46.0 \| 36248 \| 1.8010 \| 4.5572 \| 17.76 \| \| 0.9175 \| 47.0 \| 37036 \| 1.8153 \| 4.5916 \| 17.7943 \| \| 0.8877 \| 48.0 \| 37824 \| 1.8133 \| 4.5799 \| 17.7857 \| \| 0.8877 \| 49.0 \| 38612 \| 1.8254 \| 4.6511 \| 17.7657 \| \| 0.8595 \| 50.0 \| 39400 \| 1.8229 \| 4.7338 \| 17.7657 \| \| 0.8533 \| 51.0 \| 40188 \| 1.8402 \| 4.7568 \| 17.7571 \| \| 0.8414 \| 52.0 \| 40976 \| 1.8406 \| 4.7573 \| 17.8429 \| \| 0.8191 \| 53.0 \| 41764 \| 1.8499 \| 4.6985 \| 17.76 \| \| 0.8228 \| 54.0 \| 42552 \| 1.8629 \| 4.7603 \| 17.7114 \| \| 0.7987 \| 55.0 \| 43340 \| 1.8638 \| 4.5511 \| 17.8 \| \| 0.7877 \| 56.0 \| 44128 \| 1.8673 \| 4.5068 \| 17.7771 \| \| 0.7829 \| 57.0 \| 44916 \| 1.8862 \| 4.6033 \| 17.7943 \| \| 0.7571 \| 58.0 \| 45704 \| 1.8874 \| 4.6694 \| 17.7486 \| \| 0.7542 \| 59.0 \| 46492 \| 1.8996 \| 4.7531 \| 17.7571 \| \| 0.7301 \| 60.0 \| 47280 \| 1.8950 \| 4.6951 \| 17.7514 \| \| 0.73 \| 61.0 \| 48068 \| 1.9035 \| 4.7867 \| 17.7343 \| \| 0.7065 \| 62.0 \| 48856 \| 1.9127 \| 4.5863 \| 17.7257 \| \| 0.7015 \| 63.0 \| 49644 \| 1.9418 \| 4.9026 \| 17.8086 \| \| 0.6921 \| 64.0 \| 50432 \| 1.9322 \| 4.8127 \| 17.7943 \| \| 0.6714 \| 65.0 \| 51220 \| 1.9382 \| 4.5343 \| 17.7286 \| \| 0.6599 \| 66.0 \| 52008 \| 1.9508 \| 4.5273 \| 17.7343 \| \| 0.6529 \| 67.0 \| 52796 \| 1.9577 \| 4.6274 \| 17.7743 \| \| 0.647 \| 68.0 \| 53584 \| 1.9789 \| 4.5575 \| 17.7571 \| \| 0.627 \| 69.0 \| 54372 \| 1.9795 \| 4.319 \| 17.7371 \| \| 0.6279 \| 70.0 \| 55160 \| 1.9788 \| 4.6788 \| 17.7486 \| \| 0.5867 \| 71.0 \| 55948 \| 2.0100 \| 4.557 \| 17.7714 \| \| 0.5985 \| 72.0 \| 56736 \| 2.0256 \| 4.6005 \| 17.8229 \| \| 0.5939 \| 73.0 \| 57524 \| 2.0336 \| 4.7289 \| 17.8 \| \| 0.5727 \| 74.0 \| 58312 \| 2.0328 \| 4.5894 \| 17.7229 \| \| 0.5702 \| 75.0 \| 59100 \| 2.0436 \| 4.7621 \| 17.78 \| \| 0.5744 \| 76.0 \| 59888 \| 2.0662 \| 4.6161 \| 17.8057 \| \| 0.5554 \| 77.0 \| 60676 \| 2.0586 \| 4.6424 \| 17.8057 \| \| 0.5436 \| 78.0 \| 61464 \| 2.0532 \| 4.5742 \| 17.7886 \| \| 0.5359 \| 79.0 \| 62252 \| 2.0680 \| 4.8312 \| 17.7886 \| \| 0.5291 \| 80.0 \| 63040 \| 2.0858 \| 4.6342 \| 17.8457 \| \| 0.5034 \| 81.0 \| 63828 \| 2.0861 \| 4.7405 \| 17.8257 \| \| 0.5155 \| 82.0 \| 64616 \| 2.1003 \| 4.3956 \| 17.7571 \| \| 0.4989 \| 83.0 \| 65404 \| 2.1072 \| 4.339 \| 17.7914 \| \| 0.4903 \| 84.0 \| 66192 \| 2.1113 \| 4.3804 \| 17.8143 \| \| 0.4836 \| 85.0 \| 66980 \| 2.1202 \| 4.5776 \| 17.8371 \| \| 0.4794 \| 86.0 \| 67768 \| 2.1277 \| 4.6548 \| 17.7686 \| \| 0.4689 \| 87.0 \| 68556 \| 2.1360 \| 4.6453 \| 17.7571 \| \| 0.4623 \| 88.0 \| 69344 \| 2.1460 \| 4.7885 \| 17.7771 \| \| 0.4551 \| 89.0 \| 70132 \| 2.1610 \| 4.5342 \| 17.7686 \| \| 0.4405 \| 90.0 \| 70920 \| 2.1649 \| 4.5593 \| 17.8057 \| \| 0.4478 \| 91.0 \| 71708 \| 2.1518 \| 4.4945 \| 17.8314 \| \| 0.4265 \| 92.0 \| 72496 \| 2.1873 \| 4.453 \| 17.8086 \| \| 0.4191 \| 93.0 \| 73284 \| 2.1808 \| 4.6432 \| 17.8057 \| \| 0.4169 \| 94.0 \| 74072 \| 2.1871 \| 4.5543 \| 17.82 \| \| 0.4087 \| 95.0 \| 74860 \| 2.2109 \| 4.8367 \| 17.7971 \| \| 0.4054 \| 96.0 \| 75648 \| 2.2092 \| 4.7079 \| 17.8171 \| \| 0.3872 \| 97.0 \| 76436 \| 2.2103 \| 4.6996 \| 17.7943 \| \| 0.3884 \| 98.0 \| 77224 \| 2.2111 \| 4.9398 \| 17.8314 \| \| 0.3837 \| 99.0 \| 78012 \| 2.2316 \| 4.7849 \| 17.8143 \| \| 0.3777 \| 100.0 \| 78800 \| 2.2298 \| 4.7595 \| 17.8343 \| \| 0.3719 \| 101.0 \| 79588 \| 2.2404 \| 4.6768 \| 17.8457 \| \| 0.364 \| 102.0 \| 80376 \| 2.2658 \| 4.5789 \| 17.8229 \| \| 0.3549 \| 103.0 \| 81164 \| 2.2790 \| 4.6549 \| 17.8029 \| \| 0.3598 \| 104.0 \| 81952 \| 2.2953 \| 4.7411 \| 17.8486 \| \| 0.346 \| 105.0 \| 82740 \| 2.2812 \| 4.7529 \| 17.7657 \| \| 0.3376 \| 106.0 \| 83528 \| 2.2997 \| 4.5128 \| 17.7886 \| \| 0.3363 \| 107.0 \| 84316 \| 2.2938 \| 4.6983 \| 17.7914 \| \| 0.3368 \| 108.0 \| 85104 \| 2.2909 \| 4.4977 \| 17.8257 \| \| 0.3243 \| 109.0 \| 85892 \| 2.3100 \| 4.5156 \| 17.8286 \| \| 0.3197 \| 110.0 \| 86680 \| 2.3310 \| 4.7516 \| 17.7943 \| \| 0.3165 \| 111.0 \| 87468 \| 2.3354 \| 4.608 \| 17.8114 \| \| 0.3128 \| 112.0 \| 88256 \| 2.3334 \| 4.7388 \| 17.8314 \| \| 0.3038 \| 113.0 \| 89044 \| 2.3343 \| 4.6356 \| 17.7914 \| \| 0.3055 \| 114.0 \| 89832 \| 2.3553 \| 4.6694 \| 17.7971 \| \| 0.2977 \| 115.0 \| 90620 \| 2.3530 \| 4.6176 \| 17.8086 \| \| 0.2925 \| 116.0 \| 91408 \| 2.3687 \| 4.6855 \| 17.8886 \| \| 0.2794 \| 117.0 \| 92196 \| 2.3856 \| 4.5948 \| 17.84 \| \| 0.2913 \| 118.0 \| 92984 \| 2.3844 \| 4.7569 \| 17.7943 \| \| 0.2812 \| 119.0 \| 93772 \| 2.3973 \| 4.6009 \| 17.7629 \| \| 0.2731 \| 120.0 \| 94560 \| 2.4074 \| 4.7287 \| 17.8086 \| \| 0.2781 \| 121.0 \| 95348 \| 2.4083 \| 4.7944 \| 17.8571 \| \| 0.2708 \| 122.0 \| 96136 \| 2.4414 \| 4.7454 \| 17.8829 \| \| 0.2607 \| 123.0 \| 96924 \| 2.4202 \| 4.5074 \| 17.8486 \| \| 0.2617 \| 124.0 \| 97712 \| 2.4371 \| 4.6055 \| 17.8629 \| \| 0.2527 \| 125.0 \| 98500 \| 2.4314 \| 4.5891 \| 17.8 \| \| 0.2528 \| 126.0 \| 99288 \| 2.4548 \| 4.8362 \| 17.8571 \| \| 0.2522 \| 127.0 \| 100076 \| 2.4461 \| 4.6966 \| 17.8514 \| \| 0.2434 \| 128.0 \| 100864 \| 2.4492 \| 4.5774 \| 17.8514 \| \| 0.2381 \| 129.0 \| 101652 \| 2.4720 \| 4.4607 \| 17.86 \| \| 0.2411 \| 130.0 \| 102440 \| 2.4820 \| 4.484 \| 17.8371 \| \| 0.2352 \| 131.0 \| 103228 \| 2.4954 \| 4.8091 \| 17.8457 \| \| 0.2275 \| 132.0 \| 104016 \| 2.4863 \| 4.7008 \| 17.8743 \| \| 0.2244 \| 133.0 \| 104804 \| 2.5089 \| 4.8076 \| 17.8571 \| \| 0.2251 \| 134.0 \| 105592 \| 2.5085 \| 4.7374 \| 17.8029 \| \| 0.2242 \| 135.0 \| 106380 \| 2.4979 \| 4.851 \| 17.8171 \| \| 0.2217 \| 136.0 \| 107168 \| 2.5122 \| 4.6295 \| 17.8314 \| \| 0.2111 \| 137.0 \| 107956 \| 2.5131 \| 4.6315 \| 17.8229 \| \| 0.2078 \| 138.0 \| 108744 \| 2.5216 \| 4.6177 \| 17.8229 \| \| 0.2113 \| 139.0 \| 109532 \| 2.5292 \| 4.5603 \| 17.8257 \| \| 0.21 \| 140.0 \| 110320 \| 2.5494 \| 4.6128 \| 17.7971 \| \| 0.1994 \| 141.0 \| 111108 \| 2.5435 \| 4.9231 \| 17.8714 \| \| 0.2018 \| 142.0 \| 111896 \| 2.5605 \| 4.827 \| 17.8314 \| \| 0.1971 \| 143.0 \| 112684 \| 2.5624 \| 4.8075 \| 17.78 \| \| 0.1959 \| 144.0 \| 113472 \| 2.5666 \| 4.6358 \| 17.84 \| \| 0.1916 \| 145.0 \| 114260 \| 2.5740 \| 4.6628 \| 17.8257 \| \| 0.1939 \| 146.0 \| 115048 \| 2.5730 \| 4.8445 \| 17.8286 \| \| 0.1832 \| 147.0 \| 115836 \| 2.5918 \| 4.8198 \| 17.8571 \| \| 0.1884 \| 148.0 \| 116624 \| 2.6013 \| 4.7955 \| 17.8257 \| \| 0.1777 \| 149.0 \| 117412 \| 2.5996 \| 4.7503 \| 17.8114 \| \| 0.1711 \| 150.0 \| 118200 \| 2.5971 \| 4.5452 \| 17.8514 \| \| 0.1843 \| 151.0 \| 118988 \| 2.6075 \| 4.817 \| 17.8143 \| \| 0.1747 \| 152.0 \| 119776 \| 2.6161 \| 4.5231 \| 17.8257 \| \| 0.1698 \| 153.0 \| 120564 \| 2.6225 \| 4.7232 \| 17.82 \| \| 0.1685 \| 154.0 \| 121352 \| 2.6285 \| 4.7105 \| 17.8229 \| \| 0.1685 \| 155.0 \| 122140 \| 2.6443 \| 4.4228 \| 17.8686 \| \| 0.1695 \| 156.0 \| 122928 \| 2.6356 \| 4.5458 \| 17.8657 \| \| 0.1649 \| 157.0 \| 123716 \| 2.6418 \| 4.5955 \| 17.8286 \| \| 0.1643 \| 158.0 \| 124504 \| 2.6565 \| 4.5943 \| 17.8457 \| \| 0.1573 \| 159.0 \| 125292 \| 2.6434 \| 4.762 \| 17.8429 \| \| 0.1573 \| 160.0 \| 126080 \| 2.6615 \| 4.5916 \| 17.8229 \| \| 0.1558 \| 161.0 \| 126868 \| 2.6529 \| 4.527 \| 17.8371 \| \| 0.1545 \| 162.0 \| 127656 \| 2.6697 \| 4.705 \| 17.7886 \| \| 0.1563 \| 163.0 \| 128444 \| 2.6747 \| 4.6848 \| 17.8086 \| \| 0.1529 \| 164.0 \| 129232 \| 2.6711 \| 4.5149 \| 17.8171 \| \| 0.151 \| 165.0 \| 130020 \| 2.6807 \| 4.6484 \| 17.8543 \| \| 0.1471 \| 166.0 \| 130808 \| 2.6909 \| 4.7488 \| 17.8657 \| \| 0.1465 \| 167.0 \| 131596 \| 2.6889 \| 4.6446 \| 17.8086 \| \| 0.1345 \| 168.0 \| 132384 \| 2.6935 \| 4.6107 \| 17.7971 \| \| 0.1447 \| 169.0 \| 133172 \| 2.6971 \| 4.4718 \| 17.86 \| \| 0.1426 \| 170.0 \| 133960 \| 2.7083 \| 4.6878 \| 17.84 \| \| 0.1402 \| 171.0 \| 134748 \| 2.7053 \| 4.7539 \| 17.8286 \| \| 0.1382 \| 172.0 \| 135536 \| 2.7140 \| 4.7697 \| 17.8343 \| \| 0.1367 \| 173.0 \| 136324 \| 2.7221 \| 4.6764 \| 17.8429 \| \| 0.1365 \| 174.0 \| 137112 \| 2.7364 \| 4.7535 \| 17.8343 \| \| 0.1277 \| 175.0 \| 137900 \| 2.7232 \| 4.7312 \| 17.8343 \| \| 0.1331 \| 176.0 \| 138688 \| 2.7292 \| 4.8578 \| 17.8171 \| \| 0.1332 \| 177.0 \| 139476 \| 2.7565 \| 4.7861 \| 17.8 \| \| 0.1291 \| 178.0 \| 140264 \| 2.7577 \| 4.8903 \| 17.7686 \| \| 0.1298 \| 179.0 \| 141052 \| 2.7474 \| 4.7653 \| 17.8171 \| \| 0.1268 \| 180.0 \| 141840 \| 2.7466 \| 4.7403 \| 17.8143 \| \| 0.123 \| 181.0 \| 142628 \| 2.7517 \| 4.7989 \| 17.8171 \| \| 0.1267 \| 182.0 \| 143416 \| 2.7634 \| 4.7267 \| 17.84 \| \| 0.1246 \| 183.0 \| 144204 \| 2.7620 \| 4.8103 \| 17.8343 \| \| 0.1221 \| 184.0 \| 144992 \| 2.7686 \| 4.968 \| 17.8429 \| \| 0.1202 \| 185.0 \| 145780 \| 2.7624 \| 4.806 \| 17.7914 \| \| 0.1222 \| 186.0 \| 146568 \| 2.7735 \| 4.8647 \| 17.82 \| \| 0.1187 \| 187.0 \| 147356 \| 2.7775 \| 4.5615 \| 17.8229 \| \| 0.1175 \| 188.0 \| 148144 \| 2.7703 \| 4.824 \| 17.82 \| \| 0.121 \| 189.0 \| 148932 \| 2.7824 \| 4.8669 \| 17.78 \| \| 0.114 \| 190.0 \| 149720 \| 2.7807 \| 4.8833 \| 17.8257 \| \| 0.1146 \| 191.0 \| 150508 \| 2.7869 \| 4.9505 \| 17.7857 \| \| 0.1133 \| 192.0 \| 151296 \| 2.7900 \| 4.9474 \| 17.7257 \| \| 0.1137 \| 193.0 \| 152084 \| 2.8008 \| 4.8476 \| 17.7371 \| \| 0.1098 \| 194.0 \| 152872 \| 2.7971 \| 4.736 \| 17.7543 \| \| 0.1072 \| 195.0 \| 153660 \| 2.7956 \| 4.7635 \| 17.8057 \| \| 0.1106 \| 196.0 \| 154448 \| 2.8019 \| 4.6805 \| 17.7657 \| \| 0.1077 \| 197.0 \| 155236 \| 2.8134 \| 4.6501 \| 17.8029 \| \| 0.1076 \| 198.0 \| 156024 \| 2.8222 \| 4.5361 \| 17.82 \| \| 0.1054 \| 199.0 \| 156812 \| 2.8173 \| 4.8964 \| 17.78 \| \| 0.1045 \| 200.0 \| 157600 \| 2.8248 \| 4.9418 \| 17.7771 \| \| 0.1083 \| 201.0 \| 158388 \| 2.8214 \| 4.8408 \| 17.7829 \| \| 0.1035 \| 202.0 \| 159176 \| 2.8277 \| 4.66 \| 17.8 \| \| 0.1033 \| 203.0 \| 159964 \| 2.8342 \| 4.616 \| 17.8114 \| \| 0.1013 \| 204.0 \| 160752 \| 2.8392 \| 4.7213 \| 17.8371 \| \| 0.1012 \| 205.0 \| 161540 \| 2.8313 \| 4.7918 \| 17.8 \| \| 0.1021 \| 206.0 \| 162328 \| 2.8372 \| 4.8182 \| 17.8371 \| \| 0.0979 \| 207.0 \| 163116 \| 2.8500 \| 4.759 \| 17.8657 \| \| 0.0985 \| 208.0 \| 163904 \| 2.8458 \| 4.6711 \| 17.8171 \| \| 0.1006 \| 209.0 \| 164692 \| 2.8468 \| 4.7997 \| 17.8286 \| \| 0.0994 \| 210.0 \| 165480 \| 2.8426 \| 4.7327 \| 17.8571 \| \| 0.0981 \| 211.0 \| 166268 \| 2.8565 \| 4.7288 \| 17.8457 \| \| 0.0985 \| 212.0 \| 167056 \| 2.8608 \| 4.8843 \| 17.8457 \| \| 0.0933 \| 213.0 \| 167844 \| 2.8656 \| 4.7052 \| 17.8143 \| \| 0.0963 \| 214.0 \| 168632 \| 2.8650 \| 4.8149 \| 17.7771 \| \| 0.092 \| 215.0 \| 169420 \| 2.8569 \| 4.6251 \| 17.8 \| \| 0.0958 \| 216.0 \| 170208 \| 2.8688 \| 4.7479 \| 17.7714 \| \| 0.094 \| 217.0 \| 170996 \| 2.8657 \| 4.7716 \| 17.8229 \| \| 0.0926 \| 218.0 \| 171784 \| 2.8741 \| 4.6749 \| 17.8143 \| \| 0.0924 \| 219.0 \| 172572 \| 2.8727 \| 4.8438 \| 17.82 \| \| 0.0932 \| 220.0 \| 173360 \| 2.8749 \| 4.6733 \| 17.84 \| \| 0.0899 \| 221.0 \| 174148 \| 2.8774 \| 4.6198 \| 17.8286 \| \| 0.0925 \| 222.0 \| 174936 \| 2.8796 \| 4.6945 \| 17.8286 \| \| 0.0904 \| 223.0 \| 175724 \| 2.8872 \| 4.6184 \| 17.82 \| \| 0.0886 \| 224.0 \| 176512 \| 2.8974 \| 4.74 \| 17.7743 \| \| 0.0898 \| 225.0 \| 177300 \| 2.8879 \| 4.5856 \| 17.8229 \| \| 0.0874 \| 226.0 \| 178088 \| 2.8880 \| 4.582 \| 17.8171 \| \| 0.0877 \| 227.0 \| 178876 \| 2.8941 \| 4.64 \| 17.8057 \| \| 0.0892 \| 228.0 \| 179664 \| 2.8975 \| 4.7271 \| 17.8114 \| \| 0.0857 \| 229.0 \| 180452 \| 2.8957 \| 4.6847 \| 17.7943 \| \| 0.088 \| 230.0 \| 181240 \| 2.8950 \| 4.7799 \| 17.8086 \| \| 0.0885 \| 231.0 \| 182028 \| 2.9061 \| 4.699 \| 17.7829 \| \| 0.0863 \| 232.0 \| 182816 \| 2.9085 \| 4.7863 \| 17.7771 \| \| 0.0853 \| 233.0 \| 183604 \| 2.9083 \| 4.7545 \| 17.7857 \| \| 0.0838 \| 234.0 \| 184392 \| 2.9067 \| 4.6354 \| 17.7829 \| \| 0.0835 \| 235.0 \| 185180 \| 2.9139 \| 4.5979 \| 17.8371 \| \| 0.0865 \| 236.0 \| 185968 \| 2.9094 \| 4.7646 \| 17.8314 \| \| 0.0853 \| 237.0 \| 186756 \| 2.9127 \| 4.6967 \| 17.7971 \| \| 0.082 \| 238.0 \| 187544 \| 2.9205 \| 4.7171 \| 17.8029 \| \| 0.0811 \| 239.0 \| 188332 \| 2.9204 \| 4.6172 \| 17.7971 \| \| 0.0837 \| 240.0 \| 189120 \| 2.9202 \| 4.6729 \| 17.8057 \| \| 0.0803 \| 241.0 \| 189908 \| 2.9190 \| 4.9057 \| 17.8143 \| \| 0.0813 \| 242.0 \| 190696 \| 2.9236 \| 4.7919 \| 17.8429 \| \| 0.0814 \| 243.0 \| 191484 \| 2.9307 \| 4.7492 \| 17.8286 \| \| 0.0822 \| 244.0 \| 192272 \| 2.9238 \| 4.7454 \| 17.8429 \| \| 0.0823 \| 245.0 \| 193060 \| 2.9269 \| 4.8462 \| 17.8257 \| \| 0.0803 \| 246.0 \| 193848 \| 2.9293 \| 4.738 \| 17.8286 \| \| 0.0806 \| 247.0 \| 194636 \| 2.9280 \| 4.8432 \| 17.78 \| \| 0.0757 \| 248.0 \| 195424 \| 2.9371 \| 4.8563 \| 17.8171 \| \| 0.0774 \| 249.0 \| 196212 \| 2.9330 \| 4.7717 \| 17.8057 \| \| 0.079 \| 250.0 \| 197000 \| 2.9373 \| 4.7938 \| 17.8371 \| \| 0.0784 \| 251.0 \| 197788 \| 2.9397 \| 4.8316 \| 17.82 \| \| 0.0801 \| 252.0 \| 198576 \| 2.9378 \| 4.9071 \| 17.8314 \| \| 0.0795 \| 253.0 \| 199364 \| 2.9366 \| 4.8581 \| 17.8343 \| \| 0.077 \| 254.0 \| 200152 \| 2.9372 \| 4.8495 \| 17.7971 \| \| 0.0787 \| 255.0 \| 200940 \| 2.9447 \| 4.8479 \| 17.8086 \| \| 0.077 \| 256.0 \| 201728 \| 2.9380 \| 4.8716 \| 17.84 \| \| 0.0765 \| 257.0 \| 202516 \| 2.9410 \| 4.8944 \| 17.7571 \| \| 0.0762 \| 258.0 \| 203304 \| 2.9423 \| 4.7536 \| 17.7971 \| \| 0.0772 \| 259.0 \| 204092 \| 2.9485 \| 4.8251 \| 17.8343 \| \| 0.0761 \| 260.0 \| 204880 \| 2.9401 \| 4.7726 \| 17.82 \| \| 0.0766 \| 261.0 \| 205668 \| 2.9427 \| 4.8626 \| 17.8286 \| \| 0.0766 \| 262.0 \| 206456 \| 2.9428 \| 5.0326 \| 17.8143 \| \| 0.074 \| 263.0 \| 207244 \| 2.9463 \| 5.0095 \| 17.8286 \| \| 0.0758 \| 264.0 \| 208032 \| 2.9497 \| 4.987 \| 17.8029 \| \| 0.0778 \| 265.0 \| 208820 \| 2.9534 \| 4.9829 \| 17.8086 \| \| 0.0748 \| 266.0 \| 209608 \| 2.9521 \| 4.9309 \| 17.8286 \| \| 0.0759 \| 267.0 \| 210396 \| 2.9519 \| 4.9294 \| 17.84 \| \| 0.0738 \| 268.0 \| 211184 \| 2.9521 \| 4.9953 \| 17.8486 \| \| 0.077 \| 269.0 \| 211972 \| 2.9521 \| 4.8414 \| 17.8486 \| \| 0.0759 \| 270.0 \| 212760 \| 2.9533 \| 4.8158 \| 17.8286 \| \| 0.0725 \| 271.0 \| 213548 \| 2.9534 \| 4.8427 \| 17.8457 \| \| 0.0749 \| 272.0 \| 214336 \| 2.9512 \| 4.8769 \| 17.8314 \| \| 0.0745 \| 273.0 \| 215124 \| 2.9520 \| 4.8782 \| 17.8257 \| \| 0.0723 \| 274.0 \| 215912 \| 2.9546 \| 4.8465 \| 17.8229 \| \| 0.0748 \| 275.0 \| 216700 \| 2.9567 \| 4.8704 \| 17.8343 \| \| 0.072 \| 276.0 \| 217488 \| 2.9569 \| 4.8633 \| 17.8371 \| \| 0.0747 \| 277.0 \| 218276 \| 2.9578 \| 4.8667 \| 17.8457 \| \| 0.0722 \| 278.0 \| 219064 \| 2.9566 \| 4.8686 \| 17.8371 \| \| 0.0733 \| 279.0 \| 219852 \| 2.9563 \| 4.846 \| 17.84 \| \| 0.0713 \| 280.0 \| 220640 \| 2.9566 \| 4.8513 \| 17.84 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.12.1+cu113 - Datasets 2.3.2 - Tokenizers 0.10.3
ShishckovA/results	ShishckovA	2022-11-29T07:33:29Z	105	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-29T07:31:07Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2721 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 0.505 \| 1.0 \| 26878 \| 0.4409 \| \| 0.4063 \| 2.0 \| 53756 \| 0.3390 \| \| 0.358 \| 3.0 \| 80634 \| 0.2967 \| \| 0.3383 \| 4.0 \| 107512 \| 0.2777 \| \| 0.3289 \| 5.0 \| 134390 \| 0.2721 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
mlxen/electra-contrastdata-squad	mlxen	2022-11-29T07:16:20Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "electra", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-11-28T07:16:40Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: electra-contrastdata-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # electra-contrastdata-squad This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
nagais/sd-class-butterflies-32	nagais	2022-11-29T07:06:12Z	32	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T06:51:12Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(nagais/sd-class-butterflies-32) image = pipeline().images[0] image ```
jl8771/sd-class-butterflies-32	jl8771	2022-11-29T05:41:50Z	37	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T05:41:45Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(jl8771/sd-class-butterflies-32) image = pipeline().images[0] image ```
manter/momoko	manter	2022-11-29T05:21:52Z	0	8	null	[ "doi:10.57967/hf/0147", "license:unknown", "region:us" ]	null	2022-11-29T03:32:48Z	--- license: unknown --- This was a stable diffusion based model that was based off of anythingv3 and momoko which I still don't know the orgin of. (personal story: How I fond this was from going to a outdated stable diffusion web ui link and hitting generate. It came out good so I googled it and found this.) Sorce: https://www.kaggle.com/code/inmine/novelai-with-webui-stable-diffusion-version/data, https://www.kaggle.com/datasets/inmine/momoko btw here is a prompt (prompt:Masterpiece, best quality,)(negitive prompt:lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewerdigits, cropped, worst quality, low quality, normal quality, ipeg artifacts, signature, watermark,username, blurry) That's what I found work's the best, The main thing it generates is woman so be warned.
Shubham09/whisper63filescheck	Shubham09	2022-11-29T05:12:22Z	77	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-29T05:07:16Z	--- tags: - generated_from_trainer metrics: - wer model-index: - name: whisper63filescheck results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper63filescheck This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0638 - Wer: 23.7647 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.1324 \| 14.29 \| 100 \| 1.0638 \| 23.7647 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
Urigavilan03/Tiempo	Urigavilan03	2022-11-29T05:12:14Z	0	0	null	[ "region:us" ]	null	2022-11-29T05:09:08Z	un reloj de bolsillo antiguo en medio de unas hojas escritas en cursiva desenfocada
laroy23/ddpm-butterflies-128	laroy23	2022-11-29T04:33:59Z	0	0	diffusers	[ "diffusers", "tensorboard", "en", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-11-28T13:56:37Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: ./cifar-10-batches-py metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `./cifar-10-batches-py` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/laroy23/ddpm-butterflies-128/tensorboard?#scalars)
elRivx/gAWoman	elRivx	2022-11-29T04:33:34Z	0	2	null	[ "stable-diffusion", "text-to-image", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2022-11-29T04:22:28Z	--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image --- # gAWoman This is my second Stable Diffusion custom model that bring to you a generic woman generated with non-licenced images. The magic word is: gAWoman If you enjoy my work, please consider supporting me: [![Buy me a coffee](https://badgen.net/badge/icon/buymeacoffee?icon=buymeacoffee&label)](https://www.buymeacoffee.com/elrivx) Examples: <img src=https://imgur.com/B5XkfuG.png width=30% height=30%> <img src=https://imgur.com/N8lNtZo.png width=30% height=30%> ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
NSandra/distilbert-base-uncased-finetuned-ner	NSandra	2022-11-29T04:09:17Z	125	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-29T03:55:13Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2393 - Precision: 1.0 - Recall: 1.0 - F1: 1.0 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---:\|:--------:\| \| No log \| 1.0 \| 1 \| 1.5491 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| No log \| 2.0 \| 2 \| 1.3278 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| No log \| 3.0 \| 3 \| 1.2393 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
nhanv/cv_parser	nhanv	2022-11-29T04:00:56Z	167	3	transformers	[ "transformers", "pytorch", "deberta-v2", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-29T03:23:32Z	--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: cv-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cv-ner This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0956 - Precision: 0.8906 - Recall: 0.9325 - F1: 0.9111 - Accuracy: 0.9851 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 91 \| 0.2049 \| 0.6618 \| 0.7362 \| 0.6970 \| 0.9534 \| \| 0.5036 \| 2.0 \| 182 \| 0.1156 \| 0.7873 \| 0.8630 \| 0.8234 \| 0.9722 \| \| 0.1442 \| 3.0 \| 273 \| 0.1078 \| 0.8262 \| 0.9039 \| 0.8633 \| 0.9771 \| \| 0.0757 \| 4.0 \| 364 \| 0.1179 \| 0.8652 \| 0.9059 \| 0.8851 \| 0.9780 \| \| 0.0526 \| 5.0 \| 455 \| 0.0907 \| 0.888 \| 0.9080 \| 0.8979 \| 0.9837 \| \| 0.0342 \| 6.0 \| 546 \| 0.0972 \| 0.8926 \| 0.9346 \| 0.9131 \| 0.9832 \| \| 0.0245 \| 7.0 \| 637 \| 0.1064 \| 0.8937 \| 0.9284 \| 0.9107 \| 0.9834 \| \| 0.0188 \| 8.0 \| 728 \| 0.0965 \| 0.8980 \| 0.9366 \| 0.9169 \| 0.9850 \| \| 0.0159 \| 9.0 \| 819 \| 0.0999 \| 0.91 \| 0.9305 \| 0.9201 \| 0.9846 \| \| 0.0141 \| 10.0 \| 910 \| 0.0956 \| 0.8906 \| 0.9325 \| 0.9111 \| 0.9851 \| ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
jeraldflowers/vit_model	jeraldflowers	2022-11-29T03:51:31Z	188	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:beans", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-11-27T05:06:17Z	--- license: apache-2.0 tags: - image-classification - generated_from_trainer datasets: - beans metrics: - accuracy widget: - src: https://huggingface.co/jeraldflowers/vit_model/blob/main/healthy.jpeg example_title: Healthy - src: https://huggingface.co/jeraldflowers/vit_model/blob/main/bean_rust.jpeg example_title: Bean Rust model-index: - name: vit_model results: - task: name: Image Classification type: image-classification dataset: name: beans type: beans config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 1.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0095 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1526 \| 3.85 \| 500 \| 0.0095 \| 1.0 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
UCSYNLP/MyanBERTa	UCSYNLP	2022-11-29T03:35:58Z	297	3	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "MyanBERTa", "Myanmar", "BERT", "RoBERTa", "my", "dataset:MyCorpus", "dataset:Web", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-07-25T06:57:10Z	--- language: my tags: - MyanBERTa - Myanmar - BERT - RoBERTa license: apache-2.0 datasets: - MyCorpus - Web --- ## Model description This model is a BERT based Myanmar pre-trained language model. MyanBERTa was pre-trained for 528K steps on a word segmented Myanmar dataset consisting of 5,992,299 sentences (136M words). As the tokenizer, byte-leve BPE tokenizer of 30,522 subword units which is learned after word segmentation is applied. Cite this work as: ``` Aye Mya Hlaing, Win Pa Pa, "MyanBERTa: A Pre-trained Language Model For Myanmar", In Proceedings of 2022 International Conference on Communication and Computer Research (ICCR2022), November 2022, Seoul, Republic of Korea ``` [Download Paper](https://journal-home.s3.ap-northeast-2.amazonaws.com/site/iccr2022/abs/QOHFI-0004.pdf)
jeraldflowers/distilroberts-base-mrpc-glue-jeraldflowers	jeraldflowers	2022-11-29T02:57:36Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T05:30:00Z	--- license: apache-2.0 tags: - text-classification - generated_from_trainer datasets: - glue metrics: - accuracy - f1 widget: - text: ["Yucaipa owned Dominick's before selling the chain to Safeway in 1998 for $ 2.5 billion.", "Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998."] example_title: Not Equivalent - text: ["Revenue in the first quarter of the year dropped 15 percent from the same period a year earlier.", "With the scandal hanging over Stewart's company revenue the first quarter of the year dropped 15 percent from the same period a year earlier."] example_title: Equivalent model-index: - name: distilroberts-base-mrpc-glue-jeraldflowers results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: train args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8431372549019608 - name: F1 type: f1 value: 0.8814814814814815 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilroberts-base-mrpc-glue-jeraldflowers This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets. It achieves the following results on the evaluation set: - Loss: 0.4990 - Accuracy: 0.8431 - F1: 0.8815 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5289 \| 1.09 \| 500 \| 0.5668 \| 0.8211 \| 0.8689 \| \| 0.3675 \| 2.18 \| 1000 \| 0.4990 \| 0.8431 \| 0.8815 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
neulab/omnitab-large-128shot-finetuned-wtq-128shot	neulab	2022-11-29T02:55:31Z	47	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:wikitablequestions", "arxiv:2207.03637", "autotrain_compatible", "endpoints_compatible", "region:us" ]	table-question-answering	2022-11-29T02:54:00Z	--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large-128shot-finetuned-wtq-128shot` (based on BART architecture) is initialized with `neulab/omnitab-large-128shot` and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) in the 128-shot setting. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-128shot-finetuned-wtq-128shot") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-128shot-finetuned-wtq-128shot") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```
neulab/omnitab-large-1024shot-finetuned-wtq-1024shot	neulab	2022-11-29T02:45:55Z	51	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:wikitablequestions", "arxiv:2207.03637", "autotrain_compatible", "endpoints_compatible", "region:us" ]	table-question-answering	2022-11-29T02:44:57Z	--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large-1024shot-finetuned-wtq-1024shot` (based on BART architecture) is initialized with `neulab/omnitab-large-1024shot` and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) in the 1024-shot setting. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-1024shot-finetuned-wtq-1024shot") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-1024shot-finetuned-wtq-1024shot") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```
neulab/omnitab-large-1024shot	neulab	2022-11-29T02:38:18Z	48	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:wikitablequestions", "arxiv:2207.03637", "autotrain_compatible", "endpoints_compatible", "region:us" ]	table-question-answering	2022-11-29T02:37:18Z	--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large-1024shot` (based on BART architecture) is initialized with `microsoft/tapex-large` and continuously pretrained on natural and synthetic data (SQL2NL model trained in the 1024-shot setting). ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-1024shot") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-1024shot") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```
SiddharthaM/hasoc19-xlm-roberta-base-sentiment-new	SiddharthaM	2022-11-29T02:13:32Z	114	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-29T00:44:19Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: hasoc19-xlm-roberta-base-sentiment-new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hasoc19-xlm-roberta-base-sentiment-new This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3840 - Accuracy: 0.8726 - Precision: 0.8724 - Recall: 0.8726 - F1: 0.8725 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.4786 \| 1.0 \| 537 \| 0.3999 \| 0.8381 \| 0.8391 \| 0.8381 \| 0.8363 \| \| 0.349 \| 2.0 \| 1074 \| 0.3443 \| 0.8606 \| 0.8603 \| 0.8606 \| 0.8603 \| \| 0.2927 \| 3.0 \| 1611 \| 0.3412 \| 0.8669 \| 0.8668 \| 0.8669 \| 0.8662 \| \| 0.2471 \| 4.0 \| 2148 \| 0.3408 \| 0.8705 \| 0.8708 \| 0.8705 \| 0.8706 \| \| 0.2195 \| 5.0 \| 2685 \| 0.3897 \| 0.8726 \| 0.8725 \| 0.8726 \| 0.8721 \| \| 0.1849 \| 6.0 \| 3222 \| 0.3840 \| 0.8726 \| 0.8724 \| 0.8726 \| 0.8725 \| ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1
neulab/omnitab-large-16shot-finetuned-wtq-16shot	neulab	2022-11-29T02:10:07Z	52	1	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:wikitablequestions", "arxiv:2207.03637", "autotrain_compatible", "endpoints_compatible", "region:us" ]	table-question-answering	2022-11-29T01:48:24Z	--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large-16shot-finetuned-wtq-16shot` (based on BART architecture) is initialized with `neulab/omnitab-large-16shot` and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) in the 16-shot setting. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-16shot-finetuned-wtq-16shot") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-16shot-finetuned-wtq-16shot") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```
romendiratta/fin-unsupersvised-mt5-4000	romendiratta	2022-11-29T02:07:11Z	4	0	transformers	[ "transformers", "jax", "tensorboard", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-29T01:55:24Z	This model contains MT5 which has been trained via masked language modeling on a financial dataset in an unsupervised manner. --- license: mit ---
alexziweiwang/retrain5_oneTimeTraining_MTL-1epoch	alexziweiwang	2022-11-29T02:00:29Z	31	0	transformers	[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2022-11-29T01:43:16Z	--- tags: - generated_from_trainer model-index: - name: retrain5_oneTimeTraining_MTL-1epoch results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # retrain5_oneTimeTraining_MTL-1epoch This model is a fine-tuned version of [alexziweiwang/exp21-uaspeech-foundation](https://huggingface.co/alexziweiwang/exp21-uaspeech-foundation) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 7.1861 - Acc: 0.285 - Wer: 1.1126 - Correct: 57 - Total: 200 - Strlen: 200 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Acc \| Wer \| Correct \| Total \| Strlen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----:\|:------:\|:-------:\|:-----:\|:------:\| \| No log \| 0.02 \| 5 \| 13.9337 \| 0.01 \| 1.2925 \| 2 \| 200 \| 200 \| \| 12.4373 \| 0.04 \| 10 \| 13.7513 \| 0.08 \| 1.5296 \| 16 \| 200 \| 200 \| \| 12.4373 \| 0.06 \| 15 \| 13.5517 \| 0.125 \| 2.1126 \| 25 \| 200 \| 200 \| \| 12.6667 \| 0.08 \| 20 \| 13.3400 \| 0.165 \| 2.5791 \| 33 \| 200 \| 200 \| \| 12.6667 \| 0.11 \| 25 \| 13.1141 \| 0.205 \| 3.6561 \| 41 \| 200 \| 200 \| \| 11.1856 \| 0.13 \| 30 \| 12.8805 \| 0.22 \| 2.7451 \| 44 \| 200 \| 200 \| \| 11.1856 \| 0.15 \| 35 \| 12.6423 \| 0.245 \| 2.5178 \| 49 \| 200 \| 200 \| \| 10.6635 \| 0.17 \| 40 \| 12.4028 \| 0.27 \| 2.4308 \| 54 \| 200 \| 200 \| \| 10.6635 \| 0.19 \| 45 \| 12.1660 \| 0.3 \| 2.1818 \| 60 \| 200 \| 200 \| \| 10.7952 \| 0.21 \| 50 \| 11.9291 \| 0.305 \| 1.9348 \| 61 \| 200 \| 200 \| \| 10.7952 \| 0.23 \| 55 \| 11.6945 \| 0.31 \| 1.6858 \| 62 \| 200 \| 200 \| \| 10.3867 \| 0.25 \| 60 \| 11.4608 \| 0.315 \| 1.5237 \| 63 \| 200 \| 200 \| \| 10.3867 \| 0.27 \| 65 \| 11.2313 \| 0.315 \| 1.3953 \| 63 \| 200 \| 200 \| \| 10.252 \| 0.3 \| 70 \| 11.0102 \| 0.315 \| 1.3162 \| 63 \| 200 \| 200 \| \| 10.252 \| 0.32 \| 75 \| 10.7918 \| 0.315 \| 1.2826 \| 63 \| 200 \| 200 \| \| 10.1788 \| 0.34 \| 80 \| 10.5736 \| 0.315 \| 1.2628 \| 63 \| 200 \| 200 \| \| 10.1788 \| 0.36 \| 85 \| 10.3607 \| 0.32 \| 1.2391 \| 64 \| 200 \| 200 \| \| 9.1361 \| 0.38 \| 90 \| 10.1527 \| 0.31 \| 1.2253 \| 62 \| 200 \| 200 \| \| 9.1361 \| 0.4 \| 95 \| 9.9507 \| 0.31 \| 1.2036 \| 62 \| 200 \| 200 \| \| 9.5447 \| 0.42 \| 100 \| 9.7553 \| 0.315 \| 1.2095 \| 63 \| 200 \| 200 \| \| 9.5447 \| 0.44 \| 105 \| 9.5599 \| 0.31 \| 1.2016 \| 62 \| 200 \| 200 \| \| 9.1579 \| 0.46 \| 110 \| 9.3711 \| 0.295 \| 1.1996 \| 59 \| 200 \| 200 \| \| 9.1579 \| 0.48 \| 115 \| 9.1892 \| 0.295 \| 1.1897 \| 59 \| 200 \| 200 \| \| 7.9217 \| 0.51 \| 120 \| 9.0143 \| 0.3 \| 1.1858 \| 60 \| 200 \| 200 \| \| 7.9217 \| 0.53 \| 125 \| 8.8493 \| 0.305 \| 1.1719 \| 61 \| 200 \| 200 \| \| 8.4439 \| 0.55 \| 130 \| 8.6946 \| 0.305 \| 1.1739 \| 61 \| 200 \| 200 \| \| 8.4439 \| 0.57 \| 135 \| 8.5492 \| 0.31 \| 1.1581 \| 62 \| 200 \| 200 \| \| 8.0639 \| 0.59 \| 140 \| 8.4153 \| 0.315 \| 1.1502 \| 63 \| 200 \| 200 \| \| 8.0639 \| 0.61 \| 145 \| 8.2872 \| 0.32 \| 1.1482 \| 64 \| 200 \| 200 \| \| 8.4173 \| 0.63 \| 150 \| 8.1649 \| 0.33 \| 1.1443 \| 66 \| 200 \| 200 \| \| 8.4173 \| 0.65 \| 155 \| 8.0500 \| 0.325 \| 1.1403 \| 65 \| 200 \| 200 \| \| 7.8991 \| 0.67 \| 160 \| 7.9422 \| 0.33 \| 1.1364 \| 66 \| 200 \| 200 \| \| 7.8991 \| 0.7 \| 165 \| 7.8410 \| 0.32 \| 1.1344 \| 64 \| 200 \| 200 \| \| 6.9206 \| 0.72 \| 170 \| 7.7469 \| 0.32 \| 1.1304 \| 64 \| 200 \| 200 \| \| 6.9206 \| 0.74 \| 175 \| 7.6601 \| 0.325 \| 1.1285 \| 65 \| 200 \| 200 \| \| 7.1911 \| 0.76 \| 180 \| 7.5832 \| 0.305 \| 1.1206 \| 61 \| 200 \| 200 \| \| 7.1911 \| 0.78 \| 185 \| 7.5163 \| 0.305 \| 1.1225 \| 61 \| 200 \| 200 \| \| 7.201 \| 0.8 \| 190 \| 7.4565 \| 0.305 \| 1.1245 \| 61 \| 200 \| 200 \| \| 7.201 \| 0.82 \| 195 \| 7.4049 \| 0.295 \| 1.1245 \| 59 \| 200 \| 200 \| \| 7.1507 \| 0.84 \| 200 \| 7.3568 \| 0.295 \| 1.1225 \| 59 \| 200 \| 200 \| \| 7.1507 \| 0.86 \| 205 \| 7.3139 \| 0.3 \| 1.1206 \| 60 \| 200 \| 200 \| \| 6.6223 \| 0.89 \| 210 \| 7.2774 \| 0.295 \| 1.1186 \| 59 \| 200 \| 200 \| \| 6.6223 \| 0.91 \| 215 \| 7.2469 \| 0.295 \| 1.1186 \| 59 \| 200 \| 200 \| \| 7.1645 \| 0.93 \| 220 \| 7.2220 \| 0.295 \| 1.1166 \| 59 \| 200 \| 200 \| \| 7.1645 \| 0.95 \| 225 \| 7.2041 \| 0.29 \| 1.1146 \| 58 \| 200 \| 200 \| \| 6.2562 \| 0.97 \| 230 \| 7.1921 \| 0.29 \| 1.1146 \| 58 \| 200 \| 200 \| \| 6.2562 \| 0.99 \| 235 \| 7.1861 \| 0.285 \| 1.1126 \| 57 \| 200 \| 200 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2
BunnyViking/bvSketchOutline	BunnyViking	2022-11-29T01:26:30Z	0	12	null	[ "license:mit", "region:us" ]	null	2022-11-28T02:53:16Z	--- license: mit --- Sketch Outline style - a scratchy concept-art like style to give the appearance of quickly rendered pencil and ink art. The model is trained on humans, some animals, some structures and a few vehicles but it is best at humans and monsters. NOTE - the model has been trained with some artistic nudes included and can generate unintended NSFW content on occasion. Custom style trained off SD 1.5 DDLM Token: bvSketchOutline Not using the token (or using prompts like 'stroke' or 'outline') or placing the token at the start or end of the prompt will have different interesting effect. Higher versions will improve the overall style at the cost of flexibility. The model will skew more toward humans at the higher versions. The higher versions will also create more monstrous animals. I recommend a confidence between 7.5 and 12.5 v2 2000 - some outline and flexible CFG 7.5 is fine 7.5 ![v2000-7-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620449747-631c8158aa346997917dcf5d.jpeg) 12.5 ![v2000-12-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620450973-631c8158aa346997917dcf5d.jpeg) v2 3000 - sketchy and flexible CFG 7.5 is fine 7.5 ![v3000-7-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620450689-631c8158aa346997917dcf5d.jpeg) 12.5 ![v3000-12-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620451447-631c8158aa346997917dcf5d.jpeg) v2 4000 - sketchy outline and extra outline strokes. recommend increasing CFG to 12.5 so less flexible 7.5 ![v4000-7-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620550007-631c8158aa346997917dcf5d.jpeg) 12.5 ![v4000-12-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620550245-631c8158aa346997917dcf5d.jpeg) v2 5000 - smoother outlines much less flexible, will start skewing strongly toward humans even at 7.5 CFG. At 12.5 CFG it will be sketchier with more outline strokes, almost like v2 2000 in look but at higher quality. 7.5 ![v5000-7-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620549994-631c8158aa346997917dcf5d.jpeg) 12.5 ![v5000-12-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620550011-631c8158aa346997917dcf5d.jpeg) v2 6000 - very sketchy and scratchy at 7.5 CFG, more inky, may lose detail. At 12.5 is quite inky in its outlines. 7.5 ![v6000-7-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620597579-631c8158aa346997917dcf5d.jpeg) 12.5 ![v6000-12-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620598073-631c8158aa346997917dcf5d.jpeg) v2 7000 - sketchy and many flowing outlines at 7.5 CFG. Can have compromised details. At 12.5 CFG the style becomes very inky and loses detail almost wet watercolour 7.5 ![v7000-7-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620597830-631c8158aa346997917dcf5d.jpeg) 12.5 ![v7000-12-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620598527-631c8158aa346997917dcf5d.jpeg)
alexziweiwang/retrain2_oneTimeTraining_MTL-1epoch	alexziweiwang	2022-11-29T01:04:58Z	31	0	transformers	[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2022-11-29T00:47:27Z	--- tags: - generated_from_trainer model-index: - name: retrain2_oneTimeTraining_MTL-1epoch results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # retrain2_oneTimeTraining_MTL-1epoch This model is a fine-tuned version of [alexziweiwang/exp21-uaspeech-foundation](https://huggingface.co/alexziweiwang/exp21-uaspeech-foundation) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.9312 - Acc: 0.265 - Wer: 1.0 - Correct: 53 - Total: 200 - Strlen: 200 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Acc \| Wer \| Correct \| Total \| Strlen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----:\|:------:\|:-------:\|:-----:\|:------:\| \| No log \| 0.02 \| 5 \| 13.6638 \| 0.005 \| 1.6126 \| 1 \| 200 \| 200 \| \| 12.2282 \| 0.04 \| 10 \| 13.4030 \| 0.005 \| 1.4743 \| 1 \| 200 \| 200 \| \| 12.2282 \| 0.06 \| 15 \| 13.1289 \| 0.005 \| 1.3953 \| 1 \| 200 \| 200 \| \| 12.3565 \| 0.08 \| 20 \| 12.8538 \| 0.005 \| 1.3043 \| 1 \| 200 \| 200 \| \| 12.3565 \| 0.11 \| 25 \| 12.5711 \| 0.005 \| 1.2095 \| 1 \| 200 \| 200 \| \| 10.7997 \| 0.13 \| 30 \| 12.2891 \| 0.005 \| 1.1462 \| 1 \| 200 \| 200 \| \| 10.7997 \| 0.15 \| 35 \| 12.0060 \| 0.005 \| 1.0909 \| 1 \| 200 \| 200 \| \| 10.1556 \| 0.17 \| 40 \| 11.7183 \| 0.005 \| 1.0632 \| 1 \| 200 \| 200 \| \| 10.1556 \| 0.19 \| 45 \| 11.4347 \| 0.01 \| 1.0395 \| 2 \| 200 \| 200 \| \| 10.3187 \| 0.21 \| 50 \| 11.1549 \| 0.01 \| 1.0178 \| 2 \| 200 \| 200 \| \| 10.3187 \| 0.23 \| 55 \| 10.8828 \| 0.01 \| 1.0099 \| 2 \| 200 \| 200 \| \| 9.8042 \| 0.25 \| 60 \| 10.6161 \| 0.01 \| 1.0040 \| 2 \| 200 \| 200 \| \| 9.8042 \| 0.27 \| 65 \| 10.3539 \| 0.01 \| 0.9980 \| 2 \| 200 \| 200 \| \| 9.6489 \| 0.3 \| 70 \| 10.0954 \| 0.015 \| 1.0 \| 3 \| 200 \| 200 \| \| 9.6489 \| 0.32 \| 75 \| 9.8456 \| 0.025 \| 1.0 \| 5 \| 200 \| 200 \| \| 9.6112 \| 0.34 \| 80 \| 9.5980 \| 0.045 \| 1.0 \| 9 \| 200 \| 200 \| \| 9.6112 \| 0.36 \| 85 \| 9.3535 \| 0.055 \| 1.0 \| 11 \| 200 \| 200 \| \| 8.4257 \| 0.38 \| 90 \| 9.1168 \| 0.085 \| 1.0 \| 17 \| 200 \| 200 \| \| 8.4257 \| 0.4 \| 95 \| 8.8920 \| 0.105 \| 1.0 \| 21 \| 200 \| 200 \| \| 8.7311 \| 0.42 \| 100 \| 8.6739 \| 0.11 \| 1.0 \| 22 \| 200 \| 200 \| \| 8.7311 \| 0.44 \| 105 \| 8.4607 \| 0.135 \| 1.0 \| 27 \| 200 \| 200 \| \| 8.3653 \| 0.46 \| 110 \| 8.2551 \| 0.165 \| 1.0 \| 33 \| 200 \| 200 \| \| 8.3653 \| 0.48 \| 115 \| 8.0573 \| 0.17 \| 1.0 \| 34 \| 200 \| 200 \| \| 7.1342 \| 0.51 \| 120 \| 7.8700 \| 0.175 \| 1.0 \| 35 \| 200 \| 200 \| \| 7.1342 \| 0.53 \| 125 \| 7.6908 \| 0.185 \| 1.0 \| 37 \| 200 \| 200 \| \| 7.5411 \| 0.55 \| 130 \| 7.5221 \| 0.205 \| 1.0 \| 41 \| 200 \| 200 \| \| 7.5411 \| 0.57 \| 135 \| 7.3628 \| 0.22 \| 1.0 \| 44 \| 200 \| 200 \| \| 7.2449 \| 0.59 \| 140 \| 7.2131 \| 0.23 \| 1.0 \| 46 \| 200 \| 200 \| \| 7.2449 \| 0.61 \| 145 \| 7.0735 \| 0.23 \| 1.0 \| 46 \| 200 \| 200 \| \| 7.5166 \| 0.63 \| 150 \| 6.9396 \| 0.25 \| 1.0 \| 50 \| 200 \| 200 \| \| 7.5166 \| 0.65 \| 155 \| 6.8186 \| 0.25 \| 1.0 \| 50 \| 200 \| 200 \| \| 7.0016 \| 0.67 \| 160 \| 6.7015 \| 0.25 \| 1.0 \| 50 \| 200 \| 200 \| \| 7.0016 \| 0.7 \| 165 \| 6.5904 \| 0.25 \| 1.0 \| 50 \| 200 \| 200 \| \| 6.0715 \| 0.72 \| 170 \| 6.4879 \| 0.255 \| 1.0 \| 51 \| 200 \| 200 \| \| 6.0715 \| 0.74 \| 175 \| 6.3980 \| 0.26 \| 1.0 \| 52 \| 200 \| 200 \| \| 6.312 \| 0.76 \| 180 \| 6.3198 \| 0.26 \| 1.0 \| 52 \| 200 \| 200 \| \| 6.312 \| 0.78 \| 185 \| 6.2532 \| 0.26 \| 1.0 \| 52 \| 200 \| 200 \| \| 6.3694 \| 0.8 \| 190 \| 6.1952 \| 0.26 \| 1.0 \| 52 \| 200 \| 200 \| \| 6.3694 \| 0.82 \| 195 \| 6.1453 \| 0.26 \| 1.0 \| 52 \| 200 \| 200 \| \| 6.2196 \| 0.84 \| 200 \| 6.0993 \| 0.26 \| 1.0 \| 52 \| 200 \| 200 \| \| 6.2196 \| 0.86 \| 205 \| 6.0556 \| 0.265 \| 1.0 \| 53 \| 200 \| 200 \| \| 5.7131 \| 0.89 \| 210 \| 6.0181 \| 0.265 \| 1.0 \| 53 \| 200 \| 200 \| \| 5.7131 \| 0.91 \| 215 \| 5.9873 \| 0.265 \| 1.0 \| 53 \| 200 \| 200 \| \| 6.1827 \| 0.93 \| 220 \| 5.9619 \| 0.265 \| 1.0 \| 53 \| 200 \| 200 \| \| 6.1827 \| 0.95 \| 225 \| 5.9460 \| 0.265 \| 1.0 \| 53 \| 200 \| 200 \| \| 5.3823 \| 0.97 \| 230 \| 5.9359 \| 0.265 \| 1.0 \| 53 \| 200 \| 200 \| \| 5.3823 \| 0.99 \| 235 \| 5.9312 \| 0.265 \| 1.0 \| 53 \| 200 \| 200 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2
dlwh/legal-xlm-base_128k	dlwh	2022-11-29T00:48:35Z	4	2	transformers	[ "transformers", "roberta", "fill-mask", "bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-29T00:41:54Z	--- license: apache-2.0 language: - bg - cs - da - de - el - en - es - et - fi - fr - ga - hr - hu - it - lt - lv - mt - nl - pl - pt - ro - sk - sl - sv dataset: - joelito/MultiLegalPile_Wikipedia_Filtered --- Huggingface thinks this is a model, but it's just a tokenizer. Trained on https://huggingface.co/datasets/joelito/MultiLegalPile_Wikipedia_Filtered
Serhio/sd-fine-tune-v2	Serhio	2022-11-28T23:43:18Z	34	0	diffusers	[ "diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-28T23:41:46Z	--- license: creativeml-openrail-m tags: - text-to-image --- ### sd-fine-tune-v2 on Stable Diffusion via Dreambooth #### model by Serhio This your the Stable Diffusion model fine-tuned the sd-fine-tune-v2 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: Bashkov Sergey You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
pig4431/TweetEval_BERT_5E	pig4431	2022-11-28T23:38:03Z	102	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T23:31:54Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - accuracy model-index: - name: TweetEval_BERT_5E results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: train args: sentiment metrics: - name: Accuracy type: accuracy value: 0.9266666666666666 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # TweetEval_BERT_5E This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.5419 - Accuracy: 0.9267 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6264 \| 0.04 \| 50 \| 0.5266 \| 0.74 \| \| 0.5054 \| 0.08 \| 100 \| 0.5959 \| 0.6333 \| \| 0.4732 \| 0.12 \| 150 \| 0.3524 \| 0.86 \| \| 0.3916 \| 0.16 \| 200 \| 0.3195 \| 0.8667 \| \| 0.3477 \| 0.2 \| 250 \| 0.2878 \| 0.8867 \| \| 0.3116 \| 0.24 \| 300 \| 0.2903 \| 0.92 \| \| 0.3039 \| 0.28 \| 350 \| 0.2488 \| 0.8933 \| \| 0.2633 \| 0.32 \| 400 \| 0.2530 \| 0.92 \| \| 0.2667 \| 0.37 \| 450 \| 0.2125 \| 0.9267 \| \| 0.2604 \| 0.41 \| 500 \| 0.2628 \| 0.8867 \| \| 0.278 \| 0.45 \| 550 \| 0.2322 \| 0.8867 \| \| 0.2625 \| 0.49 \| 600 \| 0.1903 \| 0.92 \| \| 0.2808 \| 0.53 \| 650 \| 0.2400 \| 0.8933 \| \| 0.2396 \| 0.57 \| 700 \| 0.2184 \| 0.9067 \| \| 0.2571 \| 0.61 \| 750 \| 0.1906 \| 0.9133 \| \| 0.2676 \| 0.65 \| 800 \| 0.2467 \| 0.9067 \| \| 0.2288 \| 0.69 \| 850 \| 0.2038 \| 0.9133 \| \| 0.2959 \| 0.73 \| 900 \| 0.1941 \| 0.9 \| \| 0.2619 \| 0.77 \| 950 \| 0.2100 \| 0.9333 \| \| 0.2504 \| 0.81 \| 1000 \| 0.1523 \| 0.9333 \| \| 0.2338 \| 0.85 \| 1050 \| 0.1429 \| 0.94 \| \| 0.2529 \| 0.89 \| 1100 \| 0.1269 \| 0.94 \| \| 0.2238 \| 0.93 \| 1150 \| 0.1722 \| 0.9333 \| \| 0.2295 \| 0.97 \| 1200 \| 0.1874 \| 0.94 \| \| 0.2089 \| 1.01 \| 1250 \| 0.2214 \| 0.9067 \| \| 0.1406 \| 1.06 \| 1300 \| 0.3410 \| 0.9133 \| \| 0.1587 \| 1.1 \| 1350 \| 0.3330 \| 0.9133 \| \| 0.1732 \| 1.14 \| 1400 \| 0.2716 \| 0.9133 \| \| 0.195 \| 1.18 \| 1450 \| 0.3726 \| 0.92 \| \| 0.1777 \| 1.22 \| 1500 \| 0.2430 \| 0.9267 \| \| 0.1433 \| 1.26 \| 1550 \| 0.3011 \| 0.9267 \| \| 0.1333 \| 1.3 \| 1600 \| 0.2489 \| 0.9333 \| \| 0.1516 \| 1.34 \| 1650 \| 0.3340 \| 0.9267 \| \| 0.1774 \| 1.38 \| 1700 \| 0.2497 \| 0.8933 \| \| 0.1608 \| 1.42 \| 1750 \| 0.3234 \| 0.9 \| \| 0.1534 \| 1.46 \| 1800 \| 0.3383 \| 0.9133 \| \| 0.1287 \| 1.5 \| 1850 \| 0.3134 \| 0.9133 \| \| 0.1422 \| 1.54 \| 1900 \| 0.3330 \| 0.9 \| \| 0.1578 \| 1.58 \| 1950 \| 0.3281 \| 0.9133 \| \| 0.1786 \| 1.62 \| 2000 \| 0.2939 \| 0.9267 \| \| 0.2019 \| 1.66 \| 2050 \| 0.3535 \| 0.9 \| \| 0.1995 \| 1.7 \| 2100 \| 0.3032 \| 0.9067 \| \| 0.159 \| 1.75 \| 2150 \| 0.2598 \| 0.9267 \| \| 0.1493 \| 1.79 \| 2200 \| 0.2391 \| 0.9267 \| \| 0.1748 \| 1.83 \| 2250 \| 0.2258 \| 0.92 \| \| 0.1783 \| 1.87 \| 2300 \| 0.2749 \| 0.9133 \| \| 0.1619 \| 1.91 \| 2350 \| 0.2699 \| 0.92 \| \| 0.1378 \| 1.95 \| 2400 \| 0.2776 \| 0.9067 \| \| 0.1529 \| 1.99 \| 2450 \| 0.2235 \| 0.9333 \| \| 0.1071 \| 2.03 \| 2500 \| 0.2841 \| 0.9267 \| \| 0.0812 \| 2.07 \| 2550 \| 0.3178 \| 0.9267 \| \| 0.0464 \| 2.11 \| 2600 \| 0.3567 \| 0.92 \| \| 0.1108 \| 2.15 \| 2650 \| 0.2723 \| 0.92 \| \| 0.0845 \| 2.19 \| 2700 \| 0.2774 \| 0.9267 \| \| 0.0795 \| 2.23 \| 2750 \| 0.3027 \| 0.9267 \| \| 0.0403 \| 2.27 \| 2800 \| 0.3566 \| 0.9267 \| \| 0.0664 \| 2.31 \| 2850 \| 0.4015 \| 0.92 \| \| 0.0659 \| 2.35 \| 2900 \| 0.4298 \| 0.9067 \| \| 0.1059 \| 2.39 \| 2950 \| 0.4028 \| 0.92 \| \| 0.105 \| 2.44 \| 3000 \| 0.3701 \| 0.92 \| \| 0.0808 \| 2.48 \| 3050 \| 0.3206 \| 0.9267 \| \| 0.0811 \| 2.52 \| 3100 \| 0.3644 \| 0.9133 \| \| 0.0458 \| 2.56 \| 3150 \| 0.3781 \| 0.9267 \| \| 0.0764 \| 2.6 \| 3200 \| 0.3749 \| 0.9267 \| \| 0.0567 \| 2.64 \| 3250 \| 0.3995 \| 0.92 \| \| 0.0971 \| 2.68 \| 3300 \| 0.3455 \| 0.92 \| \| 0.0579 \| 2.72 \| 3350 \| 0.4508 \| 0.92 \| \| 0.0853 \| 2.76 \| 3400 \| 0.4350 \| 0.92 \| \| 0.0577 \| 2.8 \| 3450 \| 0.3804 \| 0.9333 \| \| 0.0732 \| 2.84 \| 3500 \| 0.4387 \| 0.92 \| \| 0.0874 \| 2.88 \| 3550 \| 0.3885 \| 0.9333 \| \| 0.1031 \| 2.92 \| 3600 \| 0.3937 \| 0.92 \| \| 0.0335 \| 2.96 \| 3650 \| 0.4963 \| 0.8933 \| \| 0.0913 \| 3.0 \| 3700 \| 0.3827 \| 0.9333 \| \| 0.047 \| 3.04 \| 3750 \| 0.4136 \| 0.92 \| \| 0.0531 \| 3.08 \| 3800 \| 0.4362 \| 0.92 \| \| 0.0265 \| 3.12 \| 3850 \| 0.4857 \| 0.92 \| \| 0.038 \| 3.17 \| 3900 \| 0.4425 \| 0.92 \| \| 0.0294 \| 3.21 \| 3950 \| 0.4347 \| 0.92 \| \| 0.0367 \| 3.25 \| 4000 \| 0.4291 \| 0.9333 \| \| 0.0102 \| 3.29 \| 4050 \| 0.5178 \| 0.9267 \| \| 0.0311 \| 3.33 \| 4100 \| 0.4784 \| 0.9267 \| \| 0.0274 \| 3.37 \| 4150 \| 0.5421 \| 0.9267 \| \| 0.0275 \| 3.41 \| 4200 \| 0.5194 \| 0.92 \| \| 0.0795 \| 3.45 \| 4250 \| 0.4788 \| 0.92 \| \| 0.0413 \| 3.49 \| 4300 \| 0.4393 \| 0.9267 \| \| 0.0373 \| 3.53 \| 4350 \| 0.4965 \| 0.92 \| \| 0.0303 \| 3.57 \| 4400 \| 0.4284 \| 0.9267 \| \| 0.0248 \| 3.61 \| 4450 \| 0.4476 \| 0.9267 \| \| 0.0557 \| 3.65 \| 4500 \| 0.4690 \| 0.92 \| \| 0.0358 \| 3.69 \| 4550 \| 0.4774 \| 0.9133 \| \| 0.0194 \| 3.73 \| 4600 \| 0.4755 \| 0.92 \| \| 0.0473 \| 3.77 \| 4650 \| 0.4637 \| 0.92 \| \| 0.0133 \| 3.81 \| 4700 \| 0.4868 \| 0.92 \| \| 0.0204 \| 3.86 \| 4750 \| 0.4886 \| 0.9267 \| \| 0.0338 \| 3.9 \| 4800 \| 0.5101 \| 0.9267 \| \| 0.0424 \| 3.94 \| 4850 \| 0.4812 \| 0.9267 \| \| 0.0237 \| 3.98 \| 4900 \| 0.4837 \| 0.9267 \| \| 0.0372 \| 4.02 \| 4950 \| 0.5000 \| 0.9267 \| \| 0.0254 \| 4.06 \| 5000 \| 0.5210 \| 0.92 \| \| 0.024 \| 4.1 \| 5050 \| 0.5272 \| 0.92 \| \| 0.0117 \| 4.14 \| 5100 \| 0.5447 \| 0.92 \| \| 0.018 \| 4.18 \| 5150 \| 0.5353 \| 0.92 \| \| 0.0097 \| 4.22 \| 5200 \| 0.5415 \| 0.9267 \| \| 0.0151 \| 4.26 \| 5250 \| 0.5447 \| 0.9267 \| \| 0.0118 \| 4.3 \| 5300 \| 0.5285 \| 0.9267 \| \| 0.0004 \| 4.34 \| 5350 \| 0.5399 \| 0.9267 \| \| 0.0102 \| 4.38 \| 5400 \| 0.5552 \| 0.9267 \| \| 0.0012 \| 4.42 \| 5450 \| 0.5689 \| 0.92 \| \| 0.02 \| 4.46 \| 5500 \| 0.5619 \| 0.9267 \| \| 0.0056 \| 4.5 \| 5550 \| 0.5784 \| 0.92 \| \| 0.0271 \| 4.55 \| 5600 \| 0.5766 \| 0.92 \| \| 0.0191 \| 4.59 \| 5650 \| 0.5662 \| 0.92 \| \| 0.0311 \| 4.63 \| 5700 \| 0.5514 \| 0.9267 \| \| 0.0167 \| 4.67 \| 5750 \| 0.5510 \| 0.9267 \| \| 0.0293 \| 4.71 \| 5800 \| 0.5571 \| 0.9267 \| \| 0.0304 \| 4.75 \| 5850 \| 0.5494 \| 0.92 \| \| 0.0161 \| 4.79 \| 5900 \| 0.5469 \| 0.9267 \| \| 0.0017 \| 4.83 \| 5950 \| 0.5468 \| 0.9267 \| \| 0.0176 \| 4.87 \| 6000 \| 0.5426 \| 0.9267 \| \| 0.0094 \| 4.91 \| 6050 \| 0.5402 \| 0.9267 \| \| 0.0041 \| 4.95 \| 6100 \| 0.5416 \| 0.9267 \| \| 0.0281 \| 4.99 \| 6150 \| 0.5419 \| 0.9267 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.3.2 - Tokenizers 0.13.2
Pramodith/sd-class-butterflies-32	Pramodith	2022-11-28T23:19:08Z	38	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T23:18:35Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(Pramodith/sd-class-butterflies-32) image = pipeline().images[0] image ```
dogeplusplus/sd-class-butterflies-32	dogeplusplus	2022-11-28T23:02:51Z	35	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T23:02:05Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(dogeplusplus/sd-class-butterflies-32) image = pipeline().images[0] image ```
ali97/sd-class-butterflies-32	ali97	2022-11-28T22:31:50Z	37	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T22:31:00Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(ali97/sd-class-butterflies-32) image = pipeline().images[0] image ```
kanixwang/my-awesome-setfit-model	kanixwang	2022-11-28T22:19:56Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-11-28T22:02:13Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 40 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 40, "warmup_steps": 4, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
alryan1478/gpt-neo-125M-DOD-LOW	alryan1478	2022-11-28T22:19:47Z	103	0	transformers	[ "transformers", "pytorch", "gpt_neo", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-11-28T21:59:56Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: gpt-neo-125M-DOD-LOW results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt-neo-125M-DOD-LOW This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.0427 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 261 \| 6.4768 \| \| 6.8863 \| 2.0 \| 522 \| 6.1056 \| \| 6.8863 \| 3.0 \| 783 \| 6.0427 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2
futuredatascience/action-classifier-v1	futuredatascience	2022-11-28T22:17:56Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-11-28T22:17:44Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 105 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 1050, "warmup_steps": 105, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
ThomasSimonini/ML-Agents-SnowballFight-1vs1-model	ThomasSimonini	2022-11-28T22:07:31Z	6	0	ml-agents	[ "ml-agents", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Snowballfight-1vs1", "region:us" ]	reinforcement-learning	2022-11-28T21:26:07Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Snowballfight-1vs1 library_name: ml-agents ---
alryan1478/gpt-neo-125M-wikitext2	alryan1478	2022-11-28T21:57:47Z	4	0	transformers	[ "transformers", "pytorch", "gpt_neo", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-11-22T20:55:28Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: gpt-neo-125M-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt-neo-125M-wikitext2 This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.0325 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 259 \| 6.4308 \| \| 6.8563 \| 2.0 \| 518 \| 6.0898 \| \| 6.8563 \| 3.0 \| 777 \| 6.0325 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2
michaelmayo704/sd-class-butterflies-64	michaelmayo704	2022-11-28T21:39:43Z	34	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T21:38:51Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(michaelmayo704/sd-class-butterflies-64) image = pipeline().images[0] image ```
pig4431/YELP_DistilBERT_5E	pig4431	2022-11-28T21:37:46Z	107	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:yelp_review_full", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T21:21:40Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - yelp_review_full metrics: - accuracy model-index: - name: YELP_DistilBERT_5E results: - task: name: Text Classification type: text-classification dataset: name: yelp_review_full type: yelp_review_full config: yelp_review_full split: train args: yelp_review_full metrics: - name: Accuracy type: accuracy value: 0.9666666666666667 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # YELP_DistilBERT_5E This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 0.1557 - Accuracy: 0.9667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6211 \| 0.03 \| 50 \| 0.3873 \| 0.8933 \| \| 0.3252 \| 0.06 \| 100 \| 0.2181 \| 0.92 \| \| 0.2241 \| 0.1 \| 150 \| 0.1850 \| 0.94 \| \| 0.2645 \| 0.13 \| 200 \| 0.1514 \| 0.9467 \| \| 0.2094 \| 0.16 \| 250 \| 0.1850 \| 0.92 \| \| 0.2693 \| 0.19 \| 300 \| 0.1504 \| 0.9467 \| \| 0.2524 \| 0.22 \| 350 \| 0.1479 \| 0.96 \| \| 0.2538 \| 0.26 \| 400 \| 0.1375 \| 0.94 \| \| 0.1937 \| 0.29 \| 450 \| 0.1204 \| 0.9467 \| \| 0.1692 \| 0.32 \| 500 \| 0.1396 \| 0.9533 \| \| 0.1987 \| 0.35 \| 550 \| 0.1151 \| 0.94 \| \| 0.207 \| 0.38 \| 600 \| 0.1705 \| 0.94 \| \| 0.2135 \| 0.42 \| 650 \| 0.1189 \| 0.9467 \| \| 0.1847 \| 0.45 \| 700 \| 0.1315 \| 0.9533 \| \| 0.169 \| 0.48 \| 750 \| 0.1407 \| 0.9533 \| \| 0.1767 \| 0.51 \| 800 \| 0.1675 \| 0.9333 \| \| 0.1899 \| 0.54 \| 850 \| 0.0913 \| 0.9467 \| \| 0.1641 \| 0.58 \| 900 \| 0.0954 \| 0.96 \| \| 0.1765 \| 0.61 \| 950 \| 0.1237 \| 0.9467 \| \| 0.1663 \| 0.64 \| 1000 \| 0.1029 \| 0.9533 \| \| 0.1238 \| 0.67 \| 1050 \| 0.1267 \| 0.96 \| \| 0.2087 \| 0.7 \| 1100 \| 0.1111 \| 0.96 \| \| 0.1354 \| 0.74 \| 1150 \| 0.0916 \| 0.9667 \| \| 0.1937 \| 0.77 \| 1200 \| 0.1059 \| 0.96 \| \| 0.2216 \| 0.8 \| 1250 \| 0.1049 \| 0.9467 \| \| 0.1788 \| 0.83 \| 1300 \| 0.1472 \| 0.94 \| \| 0.2138 \| 0.86 \| 1350 \| 0.1234 \| 0.9467 \| \| 0.1555 \| 0.9 \| 1400 \| 0.1386 \| 0.94 \| \| 0.1583 \| 0.93 \| 1450 \| 0.1642 \| 0.9467 \| \| 0.1525 \| 0.96 \| 1500 \| 0.1571 \| 0.94 \| \| 0.2049 \| 0.99 \| 1550 \| 0.1257 \| 0.9333 \| \| 0.1266 \| 1.02 \| 1600 \| 0.1677 \| 0.94 \| \| 0.1282 \| 1.06 \| 1650 \| 0.1307 \| 0.9533 \| \| 0.1007 \| 1.09 \| 1700 \| 0.1375 \| 0.9533 \| \| 0.0991 \| 1.12 \| 1750 \| 0.1513 \| 0.9533 \| \| 0.1211 \| 1.15 \| 1800 \| 0.1229 \| 0.9667 \| \| 0.1833 \| 1.18 \| 1850 \| 0.1105 \| 0.9733 \| \| 0.1596 \| 1.22 \| 1900 \| 0.1279 \| 0.9533 \| \| 0.1172 \| 1.25 \| 1950 \| 0.1124 \| 0.96 \| \| 0.1137 \| 1.28 \| 2000 \| 0.1407 \| 0.9467 \| \| 0.1135 \| 1.31 \| 2050 \| 0.1377 \| 0.96 \| \| 0.096 \| 1.34 \| 2100 \| 0.1022 \| 0.9667 \| \| 0.1203 \| 1.38 \| 2150 \| 0.1719 \| 0.9467 \| \| 0.1289 \| 1.41 \| 2200 \| 0.1254 \| 0.9667 \| \| 0.1392 \| 1.44 \| 2250 \| 0.1086 \| 0.9667 \| \| 0.1319 \| 1.47 \| 2300 \| 0.1511 \| 0.9467 \| \| 0.1161 \| 1.5 \| 2350 \| 0.1758 \| 0.9467 \| \| 0.1402 \| 1.54 \| 2400 \| 0.1369 \| 0.96 \| \| 0.1433 \| 1.57 \| 2450 \| 0.1495 \| 0.9667 \| \| 0.1882 \| 1.6 \| 2500 \| 0.1186 \| 0.9467 \| \| 0.1474 \| 1.63 \| 2550 \| 0.1249 \| 0.9533 \| \| 0.0937 \| 1.66 \| 2600 \| 0.1390 \| 0.96 \| \| 0.1231 \| 1.7 \| 2650 \| 0.1467 \| 0.96 \| \| 0.1485 \| 1.73 \| 2700 \| 0.1602 \| 0.9533 \| \| 0.1683 \| 1.76 \| 2750 \| 0.1884 \| 0.9533 \| \| 0.1141 \| 1.79 \| 2800 \| 0.1634 \| 0.96 \| \| 0.1351 \| 1.82 \| 2850 \| 0.1212 \| 0.9733 \| \| 0.1298 \| 1.86 \| 2900 \| 0.1224 \| 0.96 \| \| 0.1616 \| 1.89 \| 2950 \| 0.1241 \| 0.96 \| \| 0.1159 \| 1.92 \| 3000 \| 0.1532 \| 0.9533 \| \| 0.1101 \| 1.95 \| 3050 \| 0.1105 \| 0.96 \| \| 0.0779 \| 1.98 \| 3100 \| 0.1334 \| 0.9533 \| \| 0.1427 \| 2.02 \| 3150 \| 0.1026 \| 0.9733 \| \| 0.0673 \| 2.05 \| 3200 \| 0.1231 \| 0.96 \| \| 0.0901 \| 2.08 \| 3250 \| 0.1077 \| 0.9733 \| \| 0.0532 \| 2.11 \| 3300 \| 0.1385 \| 0.9467 \| \| 0.0984 \| 2.14 \| 3350 \| 0.1432 \| 0.9467 \| \| 0.1006 \| 2.18 \| 3400 \| 0.1183 \| 0.9667 \| \| 0.067 \| 2.21 \| 3450 \| 0.1533 \| 0.9533 \| \| 0.0901 \| 2.24 \| 3500 \| 0.1314 \| 0.9733 \| \| 0.0644 \| 2.27 \| 3550 \| 0.1354 \| 0.9667 \| \| 0.076 \| 2.3 \| 3600 \| 0.1548 \| 0.96 \| \| 0.0932 \| 2.34 \| 3650 \| 0.1624 \| 0.9667 \| \| 0.0777 \| 2.37 \| 3700 \| 0.1878 \| 0.9533 \| \| 0.106 \| 2.4 \| 3750 \| 0.1721 \| 0.96 \| \| 0.0621 \| 2.43 \| 3800 \| 0.1470 \| 0.9667 \| \| 0.0919 \| 2.46 \| 3850 \| 0.1478 \| 0.96 \| \| 0.091 \| 2.5 \| 3900 \| 0.1371 \| 0.9667 \| \| 0.0912 \| 2.53 \| 3950 \| 0.1467 \| 0.9667 \| \| 0.0775 \| 2.56 \| 4000 \| 0.1289 \| 0.9733 \| \| 0.1053 \| 2.59 \| 4050 \| 0.1107 \| 0.9733 \| \| 0.063 \| 2.62 \| 4100 \| 0.1031 \| 0.9733 \| \| 0.0859 \| 2.66 \| 4150 \| 0.0953 \| 0.98 \| \| 0.084 \| 2.69 \| 4200 \| 0.1216 \| 0.9733 \| \| 0.1215 \| 2.72 \| 4250 \| 0.1025 \| 0.9733 \| \| 0.0675 \| 2.75 \| 4300 \| 0.0992 \| 0.9667 \| \| 0.0608 \| 2.78 \| 4350 \| 0.1288 \| 0.96 \| \| 0.0965 \| 2.82 \| 4400 \| 0.1179 \| 0.9667 \| \| 0.061 \| 2.85 \| 4450 \| 0.1178 \| 0.9733 \| \| 0.0821 \| 2.88 \| 4500 \| 0.1188 \| 0.9733 \| \| 0.0802 \| 2.91 \| 4550 \| 0.1423 \| 0.9667 \| \| 0.0901 \| 2.94 \| 4600 \| 0.1367 \| 0.96 \| \| 0.1069 \| 2.98 \| 4650 \| 0.1118 \| 0.9733 \| \| 0.0653 \| 3.01 \| 4700 \| 0.1359 \| 0.9533 \| \| 0.0577 \| 3.04 \| 4750 \| 0.1046 \| 0.9667 \| \| 0.0467 \| 3.07 \| 4800 \| 0.1366 \| 0.96 \| \| 0.041 \| 3.1 \| 4850 \| 0.1276 \| 0.9667 \| \| 0.0585 \| 3.13 \| 4900 \| 0.1426 \| 0.9667 \| \| 0.0635 \| 3.17 \| 4950 \| 0.1571 \| 0.96 \| \| 0.0395 \| 3.2 \| 5000 \| 0.1527 \| 0.96 \| \| 0.034 \| 3.23 \| 5050 \| 0.1323 \| 0.9667 \| \| 0.0405 \| 3.26 \| 5100 \| 0.1377 \| 0.96 \| \| 0.0306 \| 3.29 \| 5150 \| 0.1526 \| 0.9667 \| \| 0.0471 \| 3.33 \| 5200 \| 0.1419 \| 0.9667 \| \| 0.0646 \| 3.36 \| 5250 \| 0.1459 \| 0.9667 \| \| 0.0508 \| 3.39 \| 5300 \| 0.1312 \| 0.9667 \| \| 0.0593 \| 3.42 \| 5350 \| 0.1483 \| 0.96 \| \| 0.05 \| 3.45 \| 5400 \| 0.1076 \| 0.9733 \| \| 0.0559 \| 3.49 \| 5450 \| 0.1412 \| 0.9667 \| \| 0.0614 \| 3.52 \| 5500 \| 0.1597 \| 0.9667 \| \| 0.0691 \| 3.55 \| 5550 \| 0.1656 \| 0.96 \| \| 0.0472 \| 3.58 \| 5600 \| 0.1556 \| 0.9667 \| \| 0.055 \| 3.61 \| 5650 \| 0.1347 \| 0.9667 \| \| 0.0564 \| 3.65 \| 5700 \| 0.1424 \| 0.96 \| \| 0.0567 \| 3.68 \| 5750 \| 0.1448 \| 0.9733 \| \| 0.0645 \| 3.71 \| 5800 \| 0.1290 \| 0.9667 \| \| 0.0361 \| 3.74 \| 5850 \| 0.1367 \| 0.9667 \| \| 0.0546 \| 3.77 \| 5900 \| 0.1406 \| 0.9667 \| \| 0.043 \| 3.81 \| 5950 \| 0.1337 \| 0.96 \| \| 0.0148 \| 3.84 \| 6000 \| 0.1475 \| 0.9533 \| \| 0.0922 \| 3.87 \| 6050 \| 0.1318 \| 0.9733 \| \| 0.0671 \| 3.9 \| 6100 \| 0.1446 \| 0.9733 \| \| 0.0295 \| 3.93 \| 6150 \| 0.1217 \| 0.9733 \| \| 0.0503 \| 3.97 \| 6200 \| 0.1133 \| 0.9733 \| \| 0.0457 \| 4.0 \| 6250 \| 0.1145 \| 0.9733 \| \| 0.0487 \| 4.03 \| 6300 \| 0.1119 \| 0.9733 \| \| 0.0491 \| 4.06 \| 6350 \| 0.1274 \| 0.9667 \| \| 0.0417 \| 4.09 \| 6400 \| 0.1377 \| 0.9733 \| \| 0.0595 \| 4.13 \| 6450 \| 0.1271 \| 0.9733 \| \| 0.035 \| 4.16 \| 6500 \| 0.1183 \| 0.9733 \| \| 0.0482 \| 4.19 \| 6550 \| 0.1153 \| 0.9733 \| \| 0.0196 \| 4.22 \| 6600 \| 0.1388 \| 0.9733 \| \| 0.028 \| 4.25 \| 6650 \| 0.1310 \| 0.9733 \| \| 0.0193 \| 4.29 \| 6700 \| 0.1460 \| 0.9667 \| \| 0.0233 \| 4.32 \| 6750 \| 0.1233 \| 0.9733 \| \| 0.0316 \| 4.35 \| 6800 \| 0.1220 \| 0.9667 \| \| 0.0132 \| 4.38 \| 6850 \| 0.1350 \| 0.9533 \| \| 0.0415 \| 4.41 \| 6900 \| 0.1547 \| 0.9667 \| \| 0.0157 \| 4.45 \| 6950 \| 0.1562 \| 0.9667 \| \| 0.0186 \| 4.48 \| 7000 \| 0.1424 \| 0.9667 \| \| 0.0012 \| 4.51 \| 7050 \| 0.1421 \| 0.9667 \| \| 0.0223 \| 4.54 \| 7100 \| 0.1475 \| 0.9733 \| \| 0.0455 \| 4.57 \| 7150 \| 0.1457 \| 0.96 \| \| 0.0571 \| 4.61 \| 7200 \| 0.1559 \| 0.9667 \| \| 0.0305 \| 4.64 \| 7250 \| 0.1614 \| 0.9667 \| \| 0.0457 \| 4.67 \| 7300 \| 0.1691 \| 0.9667 \| \| 0.022 \| 4.7 \| 7350 \| 0.1622 \| 0.9667 \| \| 0.0338 \| 4.73 \| 7400 \| 0.1560 \| 0.9667 \| \| 0.0365 \| 4.77 \| 7450 \| 0.1553 \| 0.9667 \| \| 0.025 \| 4.8 \| 7500 \| 0.1512 \| 0.9667 \| \| 0.0441 \| 4.83 \| 7550 \| 0.1550 \| 0.9667 \| \| 0.0363 \| 4.86 \| 7600 \| 0.1564 \| 0.9667 \| \| 0.0188 \| 4.89 \| 7650 \| 0.1553 \| 0.9667 \| \| 0.0427 \| 4.93 \| 7700 \| 0.1572 \| 0.9733 \| \| 0.0362 \| 4.96 \| 7750 \| 0.1568 \| 0.9667 \| \| 0.0115 \| 4.99 \| 7800 \| 0.1557 \| 0.9667 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.3.2 - Tokenizers 0.13.2
rlarios/distilbert-base-uncased-finetuned-emotion	rlarios	2022-11-28T21:34:34Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-25T20:15:59Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9325 - name: F1 type: f1 value: 0.9322428116765227 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2225 - Accuracy: 0.9325 - F1: 0.9322 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8372 \| 1.0 \| 250 \| 0.3225 \| 0.9045 \| 0.9017 \| \| 0.2534 \| 2.0 \| 500 \| 0.2225 \| 0.9325 \| 0.9322 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cpu - Datasets 2.6.1 - Tokenizers 0.13.1
pig4431/TUF_ALBERT_5E	pig4431	2022-11-28T21:34:30Z	105	0	transformers	[ "transformers", "pytorch", "albert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T21:32:18Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: TUF_ALBERT_5E results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # TUF_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2389 - Accuracy: 0.9533 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5099 \| 0.1 \| 50 \| 0.3861 \| 0.8533 \| \| 0.2985 \| 0.2 \| 100 \| 0.2961 \| 0.8933 \| \| 0.2972 \| 0.3 \| 150 \| 0.2335 \| 0.9333 \| \| 0.2835 \| 0.4 \| 200 \| 0.1872 \| 0.94 \| \| 0.26 \| 0.5 \| 250 \| 0.4147 \| 0.9133 \| \| 0.2986 \| 0.59 \| 300 \| 0.2080 \| 0.9267 \| \| 0.2554 \| 0.69 \| 350 \| 0.3984 \| 0.9133 \| \| 0.2306 \| 0.79 \| 400 \| 0.2136 \| 0.9333 \| \| 0.2218 \| 0.89 \| 450 \| 0.4455 \| 0.8867 \| \| 0.2113 \| 0.99 \| 500 \| 0.2205 \| 0.94 \| \| 0.2541 \| 1.09 \| 550 \| 0.1705 \| 0.9333 \| \| 0.1947 \| 1.19 \| 600 \| 0.3264 \| 0.8933 \| \| 0.2409 \| 1.29 \| 650 \| 0.2084 \| 0.92 \| \| 0.1968 \| 1.39 \| 700 \| 0.2550 \| 0.9267 \| \| 0.172 \| 1.49 \| 750 \| 0.2238 \| 0.9467 \| \| 0.1478 \| 1.58 \| 800 \| 0.2501 \| 0.9533 \| \| 0.2199 \| 1.68 \| 850 \| 0.2618 \| 0.9133 \| \| 0.1792 \| 1.78 \| 900 \| 0.2109 \| 0.9267 \| \| 0.1831 \| 1.88 \| 950 \| 0.2641 \| 0.92 \| \| 0.1534 \| 1.98 \| 1000 \| 0.1924 \| 0.94 \| \| 0.1208 \| 2.08 \| 1050 \| 0.2990 \| 0.9333 \| \| 0.1118 \| 2.18 \| 1100 \| 0.4952 \| 0.9 \| \| 0.158 \| 2.28 \| 1150 \| 0.1706 \| 0.9533 \| \| 0.1163 \| 2.38 \| 1200 \| 0.1238 \| 0.9733 \| \| 0.1738 \| 2.48 \| 1250 \| 0.1989 \| 0.9467 \| \| 0.1305 \| 2.57 \| 1300 \| 0.4354 \| 0.9067 \| \| 0.1668 \| 2.67 \| 1350 \| 0.1276 \| 0.9667 \| \| 0.1195 \| 2.77 \| 1400 \| 0.2170 \| 0.9533 \| \| 0.1057 \| 2.87 \| 1450 \| 0.2882 \| 0.9333 \| \| 0.1172 \| 2.97 \| 1500 \| 0.1435 \| 0.9667 \| \| 0.0893 \| 3.07 \| 1550 \| 0.1754 \| 0.96 \| \| 0.0582 \| 3.17 \| 1600 \| 0.1858 \| 0.96 \| \| 0.0887 \| 3.27 \| 1650 \| 0.4954 \| 0.92 \| \| 0.1166 \| 3.37 \| 1700 \| 0.2356 \| 0.9467 \| \| 0.0518 \| 3.47 \| 1750 \| 0.1910 \| 0.96 \| \| 0.0741 \| 3.56 \| 1800 \| 0.1328 \| 0.9733 \| \| 0.072 \| 3.66 \| 1850 \| 0.2769 \| 0.9467 \| \| 0.0534 \| 3.76 \| 1900 \| 0.3501 \| 0.94 \| \| 0.0776 \| 3.86 \| 1950 \| 0.3171 \| 0.94 \| \| 0.0537 \| 3.96 \| 2000 \| 0.2138 \| 0.9533 \| \| 0.0683 \| 4.06 \| 2050 \| 0.2934 \| 0.94 \| \| 0.015 \| 4.16 \| 2100 \| 0.2233 \| 0.9533 \| \| 0.0236 \| 4.26 \| 2150 \| 0.2673 \| 0.9533 \| \| 0.0357 \| 4.36 \| 2200 \| 0.2279 \| 0.96 \| \| 0.0298 \| 4.46 \| 2250 \| 0.3017 \| 0.9467 \| \| 0.0357 \| 4.55 \| 2300 \| 0.2910 \| 0.9467 \| \| 0.0208 \| 4.65 \| 2350 \| 0.2498 \| 0.9533 \| \| 0.0345 \| 4.75 \| 2400 \| 0.2259 \| 0.9667 \| \| 0.0174 \| 4.85 \| 2450 \| 0.2274 \| 0.9667 \| \| 0.0393 \| 4.95 \| 2500 \| 0.2389 \| 0.9533 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2
anikethjr/PromoGen_K562_2080Ti_restart	anikethjr	2022-11-28T21:24:36Z	91	0	transformers	[ "transformers", "pytorch", "tensorboard", "prophetnet", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-11-27T05:27:24Z	--- tags: - generated_from_trainer model-index: - name: PromoGen_K562_2080Ti_restart results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # PromoGen_K562_2080Ti_restart This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4624 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 0.7676 \| 0.49 \| 2500 \| 0.7383 \| \| 0.7121 \| 0.97 \| 5000 \| 0.6867 \| \| 0.6914 \| 1.46 \| 7500 \| 0.6705 \| \| 0.6837 \| 1.95 \| 10000 \| 0.6622 \| \| 0.6778 \| 2.44 \| 12500 \| 0.6558 \| \| 0.6748 \| 2.92 \| 15000 \| 0.6517 \| \| 0.6676 \| 3.41 \| 17500 \| 0.6433 \| \| 0.6593 \| 3.9 \| 20000 \| 0.6358 \| \| 0.6584 \| 4.38 \| 22500 \| 0.6320 \| \| 0.6557 \| 4.87 \| 25000 \| 0.6301 \| \| 0.6523 \| 5.36 \| 27500 \| 0.6257 \| \| 0.6478 \| 5.84 \| 30000 \| 0.6236 \| \| 0.6393 \| 6.33 \| 32500 \| 0.6145 \| \| 0.6039 \| 6.82 \| 35000 \| 0.5658 \| \| 0.5616 \| 7.31 \| 37500 \| 0.5376 \| \| 0.5518 \| 7.79 \| 40000 \| 0.5310 \| \| 0.5509 \| 8.28 \| 42500 \| 0.5273 \| \| 0.5487 \| 8.77 \| 45000 \| 0.5261 \| \| 0.5479 \| 9.25 \| 47500 \| 0.5249 \| \| 0.546 \| 9.74 \| 50000 \| 0.5242 \| \| 0.5447 \| 10.23 \| 52500 \| 0.5229 \| \| 0.5439 \| 10.71 \| 55000 \| 0.5220 \| \| 0.5433 \| 11.2 \| 57500 \| 0.5209 \| \| 0.5394 \| 11.69 \| 60000 \| 0.5162 \| \| 0.5153 \| 12.18 \| 62500 \| 0.4944 \| \| 0.5137 \| 12.66 \| 65000 \| 0.4932 \| \| 0.514 \| 13.15 \| 67500 \| 0.4924 \| \| 0.5131 \| 13.64 \| 70000 \| 0.4919 \| \| 0.5104 \| 14.12 \| 72500 \| 0.4914 \| \| 0.5122 \| 14.61 \| 75000 \| 0.4906 \| \| 0.5089 \| 15.1 \| 77500 \| 0.4901 \| \| 0.5076 \| 15.59 \| 80000 \| 0.4891 \| \| 0.4986 \| 16.07 \| 82500 \| 0.4721 \| \| 0.4875 \| 16.56 \| 85000 \| 0.4672 \| \| 0.4887 \| 17.05 \| 87500 \| 0.4669 \| \| 0.4839 \| 17.53 \| 90000 \| 0.4661 \| \| 0.4849 \| 18.02 \| 92500 \| 0.4654 \| \| 0.4848 \| 18.51 \| 95000 \| 0.4649 \| \| 0.4831 \| 18.99 \| 97500 \| 0.4646 \| \| 0.4816 \| 19.48 \| 100000 \| 0.4644 \| \| 0.4808 \| 19.97 \| 102500 \| 0.4637 \| \| 0.4812 \| 20.46 \| 105000 \| 0.4634 \| \| 0.4813 \| 20.94 \| 107500 \| 0.4633 \| \| 0.4818 \| 21.43 \| 110000 \| 0.4631 \| \| 0.4813 \| 21.92 \| 112500 \| 0.4629 \| \| 0.4782 \| 22.4 \| 115000 \| 0.4628 \| \| 0.4804 \| 22.89 \| 117500 \| 0.4626 \| \| 0.4815 \| 23.38 \| 120000 \| 0.4625 \| \| 0.4812 \| 23.87 \| 122500 \| 0.4625 \| \| 0.4785 \| 24.35 \| 125000 \| 0.4624 \| \| 0.4795 \| 24.84 \| 127500 \| 0.4624 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.0 - Tokenizers 0.13.0.dev0
Inayat/Fine_tune_whisper_small	Inayat	2022-11-28T21:14:32Z	79	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-14T19:18:30Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: Fine_tune_whisper_small results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Fine_tune_whisper_small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8238 - Wer: 42.9362 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 900 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.2994 \| 3.92 \| 200 \| 0.6607 \| 44.0797 \| \| 0.0201 \| 7.84 \| 400 \| 0.7371 \| 42.6042 \| \| 0.002 \| 11.76 \| 600 \| 0.8027 \| 42.5304 \| \| 0.0011 \| 15.69 \| 800 \| 0.8238 \| 42.9362 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
pig4431/TweetEval_DistilBERT_5E	pig4431	2022-11-28T21:09:36Z	103	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T21:03:38Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - accuracy model-index: - name: TweetEval_DistilBERT_5E results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: train args: sentiment metrics: - name: Accuracy type: accuracy value: 0.9133333333333333 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # TweetEval_DistilBERT_5E This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.4043 - Accuracy: 0.9133 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5747 \| 0.04 \| 50 \| 0.4843 \| 0.7333 \| \| 0.4336 \| 0.08 \| 100 \| 0.2888 \| 0.8667 \| \| 0.3437 \| 0.12 \| 150 \| 0.2895 \| 0.8667 \| \| 0.3375 \| 0.16 \| 200 \| 0.2864 \| 0.8733 \| \| 0.3072 \| 0.2 \| 250 \| 0.2577 \| 0.8867 \| \| 0.3019 \| 0.24 \| 300 \| 0.2574 \| 0.8933 \| \| 0.2662 \| 0.28 \| 350 \| 0.2621 \| 0.8867 \| \| 0.283 \| 0.32 \| 400 \| 0.2340 \| 0.92 \| \| 0.2949 \| 0.37 \| 450 \| 0.2482 \| 0.8933 \| \| 0.3066 \| 0.41 \| 500 \| 0.2537 \| 0.9 \| \| 0.2457 \| 0.45 \| 550 \| 0.2473 \| 0.9 \| \| 0.295 \| 0.49 \| 600 \| 0.2177 \| 0.9133 \| \| 0.2862 \| 0.53 \| 650 \| 0.2215 \| 0.9133 \| \| 0.2603 \| 0.57 \| 700 \| 0.2272 \| 0.9133 \| \| 0.2976 \| 0.61 \| 750 \| 0.2298 \| 0.9067 \| \| 0.2823 \| 0.65 \| 800 \| 0.2451 \| 0.8933 \| \| 0.2583 \| 0.69 \| 850 \| 0.2645 \| 0.8933 \| \| 0.2694 \| 0.73 \| 900 \| 0.2352 \| 0.9 \| \| 0.2433 \| 0.77 \| 950 \| 0.2322 \| 0.9133 \| \| 0.2598 \| 0.81 \| 1000 \| 0.2300 \| 0.9 \| \| 0.2701 \| 0.85 \| 1050 \| 0.2162 \| 0.9 \| \| 0.2227 \| 0.89 \| 1100 \| 0.2135 \| 0.8933 \| \| 0.2045 \| 0.93 \| 1150 \| 0.2233 \| 0.9133 \| \| 0.2821 \| 0.97 \| 1200 \| 0.2194 \| 0.9 \| \| 0.2342 \| 1.01 \| 1250 \| 0.2488 \| 0.88 \| \| 0.2028 \| 1.06 \| 1300 \| 0.2451 \| 0.8867 \| \| 0.1509 \| 1.1 \| 1350 \| 0.3174 \| 0.88 \| \| 0.1888 \| 1.14 \| 1400 \| 0.2537 \| 0.9133 \| \| 0.1825 \| 1.18 \| 1450 \| 0.2559 \| 0.9067 \| \| 0.1721 \| 1.22 \| 1500 \| 0.2511 \| 0.92 \| \| 0.2137 \| 1.26 \| 1550 \| 0.2963 \| 0.9133 \| \| 0.2153 \| 1.3 \| 1600 \| 0.2210 \| 0.92 \| \| 0.1989 \| 1.34 \| 1650 \| 0.2231 \| 0.9133 \| \| 0.2155 \| 1.38 \| 1700 \| 0.1991 \| 0.9133 \| \| 0.1912 \| 1.42 \| 1750 \| 0.2146 \| 0.92 \| \| 0.1623 \| 1.46 \| 1800 \| 0.2721 \| 0.9 \| \| 0.2236 \| 1.5 \| 1850 \| 0.2301 \| 0.9267 \| \| 0.1907 \| 1.54 \| 1900 \| 0.1988 \| 0.92 \| \| 0.1286 \| 1.58 \| 1950 \| 0.2326 \| 0.9 \| \| 0.2147 \| 1.62 \| 2000 \| 0.2432 \| 0.9267 \| \| 0.2018 \| 1.66 \| 2050 \| 0.2162 \| 0.9067 \| \| 0.2073 \| 1.7 \| 2100 \| 0.2153 \| 0.9133 \| \| 0.1498 \| 1.75 \| 2150 \| 0.2335 \| 0.92 \| \| 0.1812 \| 1.79 \| 2200 \| 0.2275 \| 0.9267 \| \| 0.1482 \| 1.83 \| 2250 \| 0.2734 \| 0.9 \| \| 0.2233 \| 1.87 \| 2300 \| 0.2454 \| 0.9 \| \| 0.1673 \| 1.91 \| 2350 \| 0.2394 \| 0.92 \| \| 0.1555 \| 1.95 \| 2400 \| 0.2725 \| 0.92 \| \| 0.2082 \| 1.99 \| 2450 \| 0.2684 \| 0.9133 \| \| 0.1545 \| 2.03 \| 2500 \| 0.3049 \| 0.9067 \| \| 0.1384 \| 2.07 \| 2550 \| 0.2960 \| 0.9133 \| \| 0.1201 \| 2.11 \| 2600 \| 0.3259 \| 0.9 \| \| 0.1348 \| 2.15 \| 2650 \| 0.3091 \| 0.9133 \| \| 0.1046 \| 2.19 \| 2700 \| 0.2916 \| 0.9267 \| \| 0.1506 \| 2.23 \| 2750 \| 0.2910 \| 0.9133 \| \| 0.1481 \| 2.27 \| 2800 \| 0.2855 \| 0.9067 \| \| 0.1318 \| 2.31 \| 2850 \| 0.3075 \| 0.9 \| \| 0.1204 \| 2.35 \| 2900 \| 0.3169 \| 0.8933 \| \| 0.1669 \| 2.39 \| 2950 \| 0.3050 \| 0.9067 \| \| 0.1725 \| 2.44 \| 3000 \| 0.2970 \| 0.9133 \| \| 0.1305 \| 2.48 \| 3050 \| 0.3065 \| 0.9 \| \| 0.1508 \| 2.52 \| 3100 \| 0.3079 \| 0.9133 \| \| 0.184 \| 2.56 \| 3150 \| 0.3482 \| 0.9067 \| \| 0.1263 \| 2.6 \| 3200 \| 0.3310 \| 0.9 \| \| 0.1282 \| 2.64 \| 3250 \| 0.3520 \| 0.8933 \| \| 0.1217 \| 2.68 \| 3300 \| 0.3158 \| 0.9067 \| \| 0.1203 \| 2.72 \| 3350 \| 0.3351 \| 0.92 \| \| 0.1068 \| 2.76 \| 3400 \| 0.3239 \| 0.92 \| \| 0.1517 \| 2.8 \| 3450 \| 0.3247 \| 0.92 \| \| 0.113 \| 2.84 \| 3500 \| 0.3269 \| 0.9133 \| \| 0.1276 \| 2.88 \| 3550 \| 0.3162 \| 0.92 \| \| 0.1548 \| 2.92 \| 3600 \| 0.3196 \| 0.9133 \| \| 0.1305 \| 2.96 \| 3650 \| 0.3163 \| 0.92 \| \| 0.149 \| 3.0 \| 3700 \| 0.3013 \| 0.92 \| \| 0.0816 \| 3.04 \| 3750 \| 0.3097 \| 0.9267 \| \| 0.0884 \| 3.08 \| 3800 \| 0.3028 \| 0.92 \| \| 0.0727 \| 3.12 \| 3850 \| 0.3487 \| 0.9133 \| \| 0.1018 \| 3.17 \| 3900 \| 0.3447 \| 0.92 \| \| 0.1266 \| 3.21 \| 3950 \| 0.3589 \| 0.9133 \| \| 0.1216 \| 3.25 \| 4000 \| 0.3464 \| 0.92 \| \| 0.091 \| 3.29 \| 4050 \| 0.3454 \| 0.92 \| \| 0.0829 \| 3.33 \| 4100 \| 0.3450 \| 0.92 \| \| 0.1084 \| 3.37 \| 4150 \| 0.3670 \| 0.92 \| \| 0.0754 \| 3.41 \| 4200 \| 0.3661 \| 0.92 \| \| 0.094 \| 3.45 \| 4250 \| 0.3588 \| 0.9067 \| \| 0.0641 \| 3.49 \| 4300 \| 0.3936 \| 0.92 \| \| 0.1138 \| 3.53 \| 4350 \| 0.3616 \| 0.92 \| \| 0.0744 \| 3.57 \| 4400 \| 0.3562 \| 0.92 \| \| 0.0697 \| 3.61 \| 4450 \| 0.3532 \| 0.9267 \| \| 0.1083 \| 3.65 \| 4500 \| 0.3451 \| 0.9267 \| \| 0.0701 \| 3.69 \| 4550 \| 0.3307 \| 0.92 \| \| 0.0849 \| 3.73 \| 4600 \| 0.3797 \| 0.92 \| \| 0.09 \| 3.77 \| 4650 \| 0.3746 \| 0.9267 \| \| 0.0799 \| 3.81 \| 4700 \| 0.3799 \| 0.92 \| \| 0.0589 \| 3.86 \| 4750 \| 0.3805 \| 0.92 \| \| 0.0578 \| 3.9 \| 4800 \| 0.3910 \| 0.9133 \| \| 0.0816 \| 3.94 \| 4850 \| 0.3856 \| 0.9133 \| \| 0.1366 \| 3.98 \| 4900 \| 0.3707 \| 0.92 \| \| 0.0846 \| 4.02 \| 4950 \| 0.3802 \| 0.92 \| \| 0.0401 \| 4.06 \| 5000 \| 0.3842 \| 0.92 \| \| 0.0851 \| 4.1 \| 5050 \| 0.3773 \| 0.9267 \| \| 0.0514 \| 4.14 \| 5100 \| 0.3922 \| 0.9133 \| \| 0.0909 \| 4.18 \| 5150 \| 0.3893 \| 0.92 \| \| 0.0764 \| 4.22 \| 5200 \| 0.3818 \| 0.9133 \| \| 0.1208 \| 4.26 \| 5250 \| 0.4096 \| 0.92 \| \| 0.0689 \| 4.3 \| 5300 \| 0.3940 \| 0.9133 \| \| 0.0524 \| 4.34 \| 5350 \| 0.4020 \| 0.9133 \| \| 0.0733 \| 4.38 \| 5400 \| 0.4002 \| 0.9133 \| \| 0.0699 \| 4.42 \| 5450 \| 0.4013 \| 0.9133 \| \| 0.0712 \| 4.46 \| 5500 \| 0.4037 \| 0.9067 \| \| 0.0557 \| 4.5 \| 5550 \| 0.4121 \| 0.92 \| \| 0.0679 \| 4.55 \| 5600 \| 0.4067 \| 0.9133 \| \| 0.0651 \| 4.59 \| 5650 \| 0.4194 \| 0.9133 \| \| 0.0607 \| 4.63 \| 5700 \| 0.4007 \| 0.9133 \| \| 0.0676 \| 4.67 \| 5750 \| 0.4013 \| 0.9133 \| \| 0.0303 \| 4.71 \| 5800 \| 0.3984 \| 0.9133 \| \| 0.0674 \| 4.75 \| 5850 \| 0.4037 \| 0.9133 \| \| 0.0842 \| 4.79 \| 5900 \| 0.4072 \| 0.9133 \| \| 0.0516 \| 4.83 \| 5950 \| 0.4096 \| 0.9133 \| \| 0.0556 \| 4.87 \| 6000 \| 0.4111 \| 0.92 \| \| 0.0277 \| 4.91 \| 6050 \| 0.4079 \| 0.9133 \| \| 0.0629 \| 4.95 \| 6100 \| 0.4053 \| 0.9133 \| \| 0.0426 \| 4.99 \| 6150 \| 0.4043 \| 0.9133 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.3.2 - Tokenizers 0.13.2
futuredatascience/to-classifier-v1	futuredatascience	2022-11-28T20:53:10Z	2	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-11-28T20:52:58Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 53 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 530, "warmup_steps": 53, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
futuredatascience/from-classifier-v1	futuredatascience	2022-11-28T20:07:27Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-11-28T20:07:15Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 53 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 530, "warmup_steps": 53, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
reubenjohn/stack-overflow-open-status-classifier-pt	reubenjohn	2022-11-28T20:01:21Z	4	0	transformers	[ "transformers", "pytorch", "albert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-16T03:44:14Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: stack-overflow-open-status-classifier-pt results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # stack-overflow-open-status-classifier-pt This model is a fine-tuned version of [reubenjohn/stack-overflow-open-status-classifier-pt](https://huggingface.co/reubenjohn/stack-overflow-open-status-classifier-pt) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.9448 - eval_runtime: 3.554 - eval_samples_per_second: 28.137 - eval_steps_per_second: 0.563 - epoch: 0.01 - step: 60 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 1 ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
motmono/a2c-AntBulletEnv-v0	motmono	2022-11-28T19:58:24Z	2	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-28T19:57:12Z	--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1539.68 +/- 213.96 name: mean_reward verified: false --- # A2C Agent playing AntBulletEnv-v0 This is a trained model of a A2C agent playing AntBulletEnv-v0 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
pig4431/TUF_roBERTa_5E	pig4431	2022-11-28T19:55:07Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T19:48:29Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: TUF_roBERTa_5E results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # TUF_roBERTa_5E This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2136 - Accuracy: 0.9667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4665 \| 0.1 \| 50 \| 0.2587 \| 0.9333 \| \| 0.245 \| 0.2 \| 100 \| 0.1355 \| 0.96 \| \| 0.2079 \| 0.3 \| 150 \| 0.1454 \| 0.9533 \| \| 0.2098 \| 0.4 \| 200 \| 0.1809 \| 0.9533 \| \| 0.1637 \| 0.5 \| 250 \| 0.2299 \| 0.94 \| \| 0.1869 \| 0.59 \| 300 \| 0.1324 \| 0.9667 \| \| 0.2202 \| 0.69 \| 350 \| 0.1786 \| 0.9467 \| \| 0.2084 \| 0.79 \| 400 \| 0.1541 \| 0.9533 \| \| 0.148 \| 0.89 \| 450 \| 0.1790 \| 0.9533 \| \| 0.1945 \| 0.99 \| 500 \| 0.1168 \| 0.9667 \| \| 0.1648 \| 1.09 \| 550 \| 0.1153 \| 0.96 \| \| 0.1099 \| 1.19 \| 600 \| 0.1239 \| 0.96 \| \| 0.1238 \| 1.29 \| 650 \| 0.1486 \| 0.9533 \| \| 0.1067 \| 1.39 \| 700 \| 0.1195 \| 0.96 \| \| 0.1324 \| 1.49 \| 750 \| 0.1134 \| 0.96 \| \| 0.1128 \| 1.58 \| 800 \| 0.1180 \| 0.9667 \| \| 0.1406 \| 1.68 \| 850 \| 0.2081 \| 0.9533 \| \| 0.1516 \| 1.78 \| 900 \| 0.1987 \| 0.9533 \| \| 0.1537 \| 1.88 \| 950 \| 0.1644 \| 0.96 \| \| 0.0957 \| 1.98 \| 1000 \| 0.1660 \| 0.96 \| \| 0.0699 \| 2.08 \| 1050 \| 0.2057 \| 0.9533 \| \| 0.1007 \| 2.18 \| 1100 \| 0.2336 \| 0.9533 \| \| 0.0677 \| 2.28 \| 1150 \| 0.2399 \| 0.9467 \| \| 0.059 \| 2.38 \| 1200 \| 0.2331 \| 0.96 \| \| 0.1051 \| 2.48 \| 1250 \| 0.1974 \| 0.9533 \| \| 0.0778 \| 2.57 \| 1300 \| 0.2857 \| 0.9467 \| \| 0.1099 \| 2.67 \| 1350 \| 0.2641 \| 0.9533 \| \| 0.0747 \| 2.77 \| 1400 \| 0.2219 \| 0.9533 \| \| 0.0874 \| 2.87 \| 1450 \| 0.2780 \| 0.9533 \| \| 0.0675 \| 2.97 \| 1500 \| 0.1993 \| 0.96 \| \| 0.052 \| 3.07 \| 1550 \| 0.1918 \| 0.96 \| \| 0.0214 \| 3.17 \| 1600 \| 0.2410 \| 0.96 \| \| 0.0512 \| 3.27 \| 1650 \| 0.2353 \| 0.96 \| \| 0.0548 \| 3.37 \| 1700 \| 0.2722 \| 0.9533 \| \| 0.0554 \| 3.47 \| 1750 \| 0.1593 \| 0.9733 \| \| 0.0742 \| 3.56 \| 1800 \| 0.2568 \| 0.96 \| \| 0.064 \| 3.66 \| 1850 \| 0.2358 \| 0.96 \| \| 0.052 \| 3.76 \| 1900 \| 0.2161 \| 0.9667 \| \| 0.0349 \| 3.86 \| 1950 \| 0.2497 \| 0.96 \| \| 0.0868 \| 3.96 \| 2000 \| 0.1834 \| 0.9667 \| \| 0.0445 \| 4.06 \| 2050 \| 0.2441 \| 0.9533 \| \| 0.0388 \| 4.16 \| 2100 \| 0.2136 \| 0.9667 \| \| 0.0484 \| 4.26 \| 2150 \| 0.2114 \| 0.9667 \| \| 0.0263 \| 4.36 \| 2200 \| 0.2325 \| 0.96 \| \| 0.0409 \| 4.46 \| 2250 \| 0.2454 \| 0.9533 \| \| 0.0324 \| 4.55 \| 2300 \| 0.2105 \| 0.9667 \| \| 0.0295 \| 4.65 \| 2350 \| 0.2118 \| 0.9667 \| \| 0.0372 \| 4.75 \| 2400 \| 0.2005 \| 0.9667 \| \| 0.0294 \| 4.85 \| 2450 \| 0.2057 \| 0.9667 \| \| 0.0354 \| 4.95 \| 2500 \| 0.2136 \| 0.9667 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2
altsoph/xlmr-AER	altsoph	2022-11-28T19:22:35Z	115	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "nlp", "roberta", "xlmr", "classifier", "aer", "narrative", "entity recognition", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-27T22:41:15Z	--- language: - en thumbnail: https://raw.githubusercontent.com/altsoph/misc/main/imgs/aer_logo.png tags: - nlp - roberta - xlmr - classifier - aer - narrative - entity recognition license: mit --- An XLM-Roberta based language model fine-tuned for AER (Actionable Entities Recognition) -- recognition of entities that protagonists could interact with for further plot development. We used 5K+ locations from 1K interactive text fiction games and extracted textual descriptions of locations and lists of actionable entities in them. The resulting [BAER dataset is available here](https://github.com/altsoph/BAER). Then we used it to train this model. The example of usage: ```py from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline MODEL_NAME = "altsoph/xlmr-AER" text = """This bedroom is extremely spare, with dirty laundry scattered haphazardly all over the floor. Cleaner clothing can be found in the dresser. A bathroom lies to the south, while a door to the east leads to the living room.""" model = AutoModelForTokenClassification.from_pretrained(MODEL_NAME) tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) pipe = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="simple", ignore_labels=['O','PAD']) entities = pipe(text) print(entities) ``` If you use the model, please cite the following: ``` @inproceedings{Tikhonov-etal-2022-AER, title = "Actionable Entities Recognition Benchmark for Interactive Fiction", author = "Alexey Tikhonov and Ivan P. Yamshchikov", year = "2022", } ```
essayproj/roberta-base-essay	essayproj	2022-11-28T19:08:54Z	59	0	transformers	[ "transformers", "tf", "roberta", "feature-extraction", "generated_from_keras_callback", "license:mit", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-28T19:08:03Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: roberta-base-essay results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-essay This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Tokenizers 0.13.2
Dagar/t5-small-science-papers-NIPS	Dagar	2022-11-28T18:21:27Z	107	1	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-28T18:00:28Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-science-papers-NIPS results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-science-papers-NIPS This model is a fine-tuned version of [Dagar/t5-small-science-papers](https://huggingface.co/Dagar/t5-small-science-papers) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.7566 - Rouge1: 15.7066 - Rouge2: 2.5654 - Rougel: 11.4679 - Rougelsum: 14.4017 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 318 \| 5.1856 \| 13.7172 \| 2.0644 \| 10.2189 \| 12.838 \| 19.0 \| \| 5.4522 \| 2.0 \| 636 \| 5.0383 \| 15.6211 \| 2.1808 \| 11.3561 \| 14.3054 \| 19.0 \| \| 5.4522 \| 3.0 \| 954 \| 4.9486 \| 15.1659 \| 2.3308 \| 11.1052 \| 13.9456 \| 19.0 \| \| 5.1254 \| 4.0 \| 1272 \| 4.8851 \| 15.716 \| 2.4099 \| 11.4954 \| 14.5099 \| 19.0 \| \| 4.9794 \| 5.0 \| 1590 \| 4.8456 \| 15.5507 \| 2.4267 \| 11.3867 \| 14.3237 \| 19.0 \| \| 4.9794 \| 6.0 \| 1908 \| 4.8073 \| 15.8406 \| 2.4254 \| 11.6878 \| 14.6154 \| 19.0 \| \| 4.8823 \| 7.0 \| 2226 \| 4.7872 \| 15.5554 \| 2.4637 \| 11.3401 \| 14.3183 \| 19.0 \| \| 4.8338 \| 8.0 \| 2544 \| 4.7680 \| 15.4783 \| 2.4888 \| 11.3364 \| 14.2031 \| 19.0 \| \| 4.8338 \| 9.0 \| 2862 \| 4.7621 \| 15.958 \| 2.5662 \| 11.6139 \| 14.6576 \| 19.0 \| \| 4.7838 \| 10.0 \| 3180 \| 4.7566 \| 15.7066 \| 2.5654 \| 11.4679 \| 14.4017 \| 19.0 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

lyhhhhhh/mt5-small-finetuned-test-class3

lyhhhhhh

2022-11-29T23:53:45Z

60

0

transformers

[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-29T23:52:43Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: mt5-small-finetuned-test-class3 results: [] ---  # mt5-small-finetuned-test-class3 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.2262 - Validation Loss: 1.8557 - Epoch: 7 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 64112, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.0384 | 2.3228 | 0 | | 2.7913 | 2.1021 | 1 | | 2.5264 | 1.9837 | 2 | | 2.4013 | 1.9247 | 3 | | 2.3268 | 1.8783 | 4 | | 2.2781 | 1.8712 | 5 | | 2.2462 | 1.8563 | 6 | | 2.2262 | 1.8557 | 7 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

ririying/my-finetuned-mt5-class0

ririying

2022-11-29T23:52:59Z

62

0

transformers

[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-29T23:52:19Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: my-finetuned-mt5-class0 results: [] ---  # my-finetuned-mt5-class0 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.0505 - Validation Loss: 1.7733 - Epoch: 7 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 107192, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.5536 | 2.1181 | 0 | | 2.4769 | 1.9296 | 1 | | 2.2865 | 1.8569 | 2 | | 2.1928 | 1.8241 | 3 | | 2.1344 | 1.8022 | 4 | | 2.0953 | 1.7880 | 5 | | 2.0671 | 1.7811 | 6 | | 2.0505 | 1.7733 | 7 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

huggingtweets/billym2k-elonmusk-lexfridman

huggingtweets

2022-11-29T23:52:17Z

117

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-29T23:29:00Z

--- language: en thumbnail: http://www.huggingtweets.com/billym2k-elonmusk-lexfridman/1669765849257/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/956331551435960322/OaqR8pAB_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1521369379941715968/bg0KgPWm_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Lex Fridman & Shibetoshi Nakamoto</div> <div style="text-align: center; font-size: 14px;">@billym2k-elonmusk-lexfridman</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Elon Musk & Lex Fridman & Shibetoshi Nakamoto. | Data | Elon Musk | Lex Fridman | Shibetoshi Nakamoto | | --- | --- | --- | --- | | Tweets downloaded | 3198 | 2411 | 341 | | Retweets | 127 | 253 | 1 | | Short tweets | 965 | 49 | 49 | | Tweets kept | 2106 | 2109 | 291 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2nokzkg2/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @billym2k-elonmusk-lexfridman's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1cnzg4dt) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1cnzg4dt/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/billym2k-elonmusk-lexfridman') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

lyhhhhhh/mt5-small-finetuned-test-class2

lyhhhhhh

2022-11-29T23:51:06Z

59

0

transformers

[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-29T23:50:04Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: mt5-small-finetuned-test-class2 results: [] ---  # mt5-small-finetuned-test-class2 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.3668 - Validation Loss: 1.9101 - Epoch: 7 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 44464, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.4693 | 2.3874 | 0 | | 3.0670 | 2.1557 | 1 | | 2.7416 | 2.0547 | 2 | | 2.5824 | 2.0089 | 3 | | 2.4922 | 1.9654 | 4 | | 2.4299 | 1.9344 | 5 | | 2.3906 | 1.9255 | 6 | | 2.3668 | 1.9101 | 7 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

SirVeggie/cutesexyrobutts

SirVeggie

2022-11-29T23:42:19Z

0

16

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2022-10-23T21:37:56Z

--- license: creativeml-openrail-m --- # Cutesexyrobutts stable diffusion model Original artist: Cutesexyrobutts\ Patreon: https://www.patreon.com/cutesexyrobutts ## Basic explanation Token and Class words are what guide the AI to produce images similar to the trained style/object/character. Include any mix of these words in the prompt to produce verying results, or exclude them to have a less pronounced effect. There is usually at least a slight stylistic effect even without the words, but it is recommended to include at least one. Adding token word/phrase class word/phrase at the start of the prompt in that order produces results most similar to the trained concept, but they can be included elsewhere as well. Some models produce better results when not including all token/class words. 3k models are are more flexible, while 5k models produce images closer to the trained concept. I recommend 2k/3k models for normal use, and 5k/6k models for model merging and use without token/class words. However it can be also very prompt specific. I highly recommend self-experimentation. These models are subject to the same legal concerns as their base models. ## Comparison Epoch 5 version was earlier in the waifu diffusion 1.3 training process, so it is easier to produce more varied, non anime, results. Robutts-any is the newest and best model. ## robutts-any ``` token: m_robutts class: illustration style base: anything v3 ``` ## robutts ``` token: § class: robutts base: waifu diffusion 1.3 ``` ## robutts_e5 ``` token: § class: robutts base: waifu diffusion 1.3-e5 ```

lct-rug-2022/edos-2023-baseline-microsoft-deberta-v3-base-label_vector

lct-rug-2022

2022-11-29T23:41:42Z

108

0

transformers

[ "transformers", "pytorch", "deberta-v2", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-29T11:22:40Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: edos-2023-baseline-microsoft-deberta-v3-base-label_vector results: [] ---  # edos-2023-baseline-microsoft-deberta-v3-base-label_vector This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5524 - F1: 0.3162 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.1209 | 1.18 | 100 | 1.9990 | 0.0801 | | 1.7997 | 2.35 | 200 | 1.7293 | 0.1349 | | 1.5749 | 3.53 | 300 | 1.6080 | 0.2431 | | 1.3674 | 4.71 | 400 | 1.5411 | 0.2793 | | 1.2214 | 5.88 | 500 | 1.5285 | 0.2980 | | 1.0752 | 7.06 | 600 | 1.5165 | 0.3054 | | 0.9899 | 8.24 | 700 | 1.5210 | 0.3186 | | 0.8733 | 9.41 | 800 | 1.5385 | 0.3134 | | 0.8578 | 10.59 | 900 | 1.5524 | 0.3162 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

davidaponte/sd-class-butterflies-32

davidaponte

2022-11-29T23:24:52Z

31

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T23:23:17Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. It was trained using a Tesla T4 GPU on a Google Colab Notebook. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(davidaponte/sd-class-butterflies-32) image = pipeline().images[0] image ```

lct-rug-2022/edos-2023-baseline-albert-base-v2-label_vector

lct-rug-2022

2022-11-29T22:57:00Z

113

0

transformers

[ "transformers", "pytorch", "albert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-28T21:58:16Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: edos-2023-baseline-albert-base-v2-label_vector results: [] ---  # edos-2023-baseline-albert-base-v2-label_vector This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8762 - F1: 0.1946 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.1002 | 1.18 | 100 | 1.9982 | 0.1023 | | 1.7832 | 2.35 | 200 | 1.8435 | 0.1310 | | 1.57 | 3.53 | 300 | 1.8097 | 0.1552 | | 1.3719 | 4.71 | 400 | 1.8216 | 0.1631 | | 1.2072 | 5.88 | 500 | 1.8138 | 0.1811 | | 1.0186 | 7.06 | 600 | 1.8762 | 0.1946 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

fathyshalab/all-roberta-large-v1-banking-18-16-5

fathyshalab

2022-11-29T22:47:43Z

115

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-28T22:20:41Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-18-16-5 results: [] ---  # all-roberta-large-v1-banking-18-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7470 - Accuracy: 0.0756 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.8182 | 1.0 | 1 | 2.7709 | 0.0356 | | 2.6751 | 2.0 | 2 | 2.7579 | 0.0578 | | 2.5239 | 3.0 | 3 | 2.7509 | 0.0622 | | 2.4346 | 4.0 | 4 | 2.7470 | 0.0756 | | 2.4099 | 5.0 | 5 | 2.7452 | 0.0756 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1

Euchale/ArcaneInkpunk2

Euchale

2022-11-29T22:28:05Z

0

null

[ "region:us" ]

null

2022-11-29T21:19:34Z

50/50 Merge of Arcane V3 and Inkpunk V2

dn-gh/ddpm-apes-128

dn-gh

2022-11-29T21:55:07Z

0

1

diffusers

[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

null

2022-11-14T21:53:21Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] --- # ddpm-apes-128 ![example image](example.png) ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python from diffusers import DDPMPipeline import torch model_id = "dn-gh/ddpm-apes-128" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id).to(device) # run pipeline in inference image = ddpm().images[0] # save image image.save("generated_image.png") ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data This model is trained on 4866 images generated with [ykilcher/apes](https://huggingface.co/ykilcher/apes) for 30 epochs. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/dn-gh/ddpm-apes-128/tensorboard?#scalars)

ririying/mt5-small-finetuned-test

ririying

2022-11-29T21:41:01Z

4

0

transformers

[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-29T18:37:20Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ririying/mt5-small-finetuned-test results: [] ---  # ririying/mt5-small-finetuned-test This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.0505 - Validation Loss: 1.7733 - Epoch: 7 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 107192, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.5536 | 2.1181 | 0 | | 2.4769 | 1.9296 | 1 | | 2.2865 | 1.8569 | 2 | | 2.1928 | 1.8241 | 3 | | 2.1344 | 1.8022 | 4 | | 2.0953 | 1.7880 | 5 | | 2.0671 | 1.7811 | 6 | | 2.0505 | 1.7733 | 7 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

lct-rug-2022/edos-2023-baseline-roberta-base-label_category

lct-rug-2022

2022-11-29T20:46:52Z

26

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-28T20:24:29Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: edos-2023-baseline-roberta-base-label_category results: [] ---  # edos-2023-baseline-roberta-base-label_category This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0133 - F1: 0.5792 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.169 | 1.18 | 100 | 1.0580 | 0.2159 | | 0.9143 | 2.35 | 200 | 0.9283 | 0.5405 | | 0.7535 | 3.53 | 300 | 0.9387 | 0.5665 | | 0.6085 | 4.71 | 400 | 0.9574 | 0.5664 | | 0.53 | 5.88 | 500 | 1.0133 | 0.5792 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

AndrewChar/model-QA-5-epoch-RU

AndrewChar

2022-11-29T19:36:19Z

34

17

transformers

[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "ru", "dataset:sberquad", "endpoints_compatible", "region:us" ]

question-answering

2022-03-02T23:29:04Z

--- tags: - generated_from_keras_callback language: ru datasets: - sberquad model-index: - name: model-QA-5-epoch-RU results: [] ---  # model-QA-5-epoch-RU This model is a fine-tuned version of [AndrewChar/diplom-prod-epoch-4-datast-sber-QA](https://huggingface.co/AndrewChar/diplom-prod-epoch-4-datast-sber-QA) on sberquad dataset. It achieves the following results on the evaluation set: - Train Loss: 1.1991 - Validation Loss: 0.0 - Epoch: 5 ## Model description Модель отвечающая на вопрос по контектсу это дипломная работа ## Intended uses & limitations Контекст должен содержать не более 512 токенов ## Training and evaluation data DataSet SberSQuAD {'exact_match': 54.586, 'f1': 73.644} ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_re': 2e-06 'decay_steps': 2986, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.1991 | | 5 | ### Framework versions - Transformers 4.15.0 - TensorFlow 2.7.0 - Datasets 1.17.0 - Tokenizers 0.10.3

Alred/bart-base-finetuned-summarization-cnn-ver1.2

Alred

2022-11-29T19:26:01Z

8

0

transformers

[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "summarization", "generated_from_trainer", "dataset:cnn_dailymail", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2022-11-29T18:10:27Z

--- license: apache-2.0 tags: - summarization - generated_from_trainer datasets: - cnn_dailymail model-index: - name: bart-base-finetuned-summarization-cnn-ver1.2 results: [] ---  # bart-base-finetuned-summarization-cnn-ver1.2 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 2.2476 - Bertscore-mean-precision: 0.8904 - Bertscore-mean-recall: 0.8611 - Bertscore-mean-f1: 0.8753 - Bertscore-median-precision: 0.8891 - Bertscore-median-recall: 0.8600 - Bertscore-median-f1: 0.8741 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bertscore-mean-precision | Bertscore-mean-recall | Bertscore-mean-f1 | Bertscore-median-precision | Bertscore-median-recall | Bertscore-median-f1 | |:-------------:|:-----:|:-----:|:---------------:|:------------------------:|:---------------------:|:-----------------:|:--------------------------:|:-----------------------:|:-------------------:| | 2.3305 | 1.0 | 5742 | 2.2125 | 0.8845 | 0.8587 | 0.8713 | 0.8840 | 0.8577 | 0.8706 | | 1.7751 | 2.0 | 11484 | 2.2028 | 0.8910 | 0.8616 | 0.8759 | 0.8903 | 0.8603 | 0.8744 | | 1.4564 | 3.0 | 17226 | 2.2476 | 0.8904 | 0.8611 | 0.8753 | 0.8891 | 0.8600 | 0.8741 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

lct-rug-2022/edos-2023-baseline-bert-base-uncased-label_category

lct-rug-2022

2022-11-29T19:24:32Z

12

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-28T12:51:45Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: edos-2023-baseline-bert-base-uncased-label_category results: [] ---  # edos-2023-baseline-bert-base-uncased-label_category This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0354 - F1: 0.5675 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1743 | 1.18 | 100 | 1.1120 | 0.1949 | | 1.0197 | 2.35 | 200 | 1.0548 | 0.3307 | | 0.8872 | 3.53 | 300 | 0.9621 | 0.4795 | | 0.7117 | 4.71 | 400 | 0.9876 | 0.4947 | | 0.6173 | 5.88 | 500 | 0.9615 | 0.5447 | | 0.5015 | 7.06 | 600 | 0.9973 | 0.5512 | | 0.4076 | 8.24 | 700 | 1.0052 | 0.5620 | | 0.3381 | 9.41 | 800 | 1.0354 | 0.5675 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

Tirendaz/sentiment-model-on-imdb-dataset

Tirendaz

2022-11-29T19:16:53Z

13

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-29T17:43:04Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: sentiment-model-on-imdb-dataset results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.85 - name: F1 type: f1 value: 0.8543689320388349 ---  # sentiment-model-on-imdb-dataset This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3694 - Accuracy: 0.85 - F1: 0.8544 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

ikanher/sd-class-butterflies-32

ikanher

2022-11-29T19:07:48Z

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T19:07:21Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(ikanher/sd-class-butterflies-32) image = pipeline().images[0] image ```

renesteeman/whisper-base-dutch-25

renesteeman

2022-11-29T19:02:44Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "nl", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-29T10:35:49Z

--- language: - nl license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Base Dutch 25 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: nl, split: test' metrics: - name: Wer type: wer value: 29.948494805079477 ---  # Whisper Base Dutch 25 This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4919 - Wer: 29.9485 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3704 | 0.78 | 500 | 0.5438 | 33.9890 | | 0.2356 | 1.56 | 1000 | 0.5059 | 31.3516 | | 0.1335 | 2.34 | 1500 | 0.4953 | 30.5745 | | 0.0998 | 3.12 | 2000 | 0.4919 | 29.9485 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

pcuenq/Paella

pcuenq

2022-11-29T18:51:19Z

0

6

null

[ "text-to-image", "endpoints-template", "en", "arxiv:2211.07292", "license:mit", "endpoints_compatible", "region:us" ]

text-to-image

2022-11-29T15:36:58Z

--- title: Paella emoji: 🥘 language: - en tags: - text-to-image - endpoints-template license: mit --- Paella is a novel text-to-image model that uses a compressed quantized latent space, based on a f8 VQGAN, and a masked training objective to achieve fast generation in ~10 inference steps. * [Paper](https://arxiv.org/abs/2211.07292) * [Official implementation](https://github.com/dome272/Paella) Biases and content acknowledgment Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on 600 million images from the improved <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B aesthetic</a> dataset, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes.

Guizmus/SD_PoW_Collection

Guizmus

2022-11-29T18:47:05Z

0

13

EveryDream

[ "EveryDream", "diffusers", "stable-diffusion", "text-to-image", "image-to-image", "en", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2022-11-09T22:34:09Z

--- language: - en license: creativeml-openrail-m thumbnail: "https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/showcase.jpg" tags: - stable-diffusion - text-to-image - image-to-image library_name: "EveryDream" inference: false --- ![PoW](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/images/showcase_PoW_neverendingloop.jpg) # Intro This is a collection of models related to the "Picture of the Week" contest on Stable Diffusion discord. I try to make a model out of all the submission for people to continue enjoy the theme after the even, and see a little of their designs in other people's creations. The token stays "PoW Style" and I balance the learning on the low side, so that it doesn't just replicate creations. I also make smaller quality models to help make pictures for the contest itself, based on the theme. # 29 novembre 2022, "The Stable Kitchen" ## Theme : Burgers and Fries Welcome to the VERY FIRST edition of the most Stable Kitchen in the universe! On today’s menu will be Sandwiches & Frie. Since you’re here for the first time, I will explain how it works! You can generate your orders and we will make them for you. Take a seat, flip through the menu, bring all of your favorite ingredients~ * The sandwich with the most cheddar? 5 beef burgers? An infinite fries generator? * Serve us your best sandwich and fries combo! Not even the sky's the limit my friend, You want it? You have it! As long as it's delicious, of course! We’ll see you on the chopping block for this week’s Stable Kitchen! ![PoW](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/291122/images/theme.png) ## Models ### Burgy ![Burgy](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/291122/images/showcase_burgy.jpg) * Burgers, burgers burgers * training: 40 pictures, 6 epochs of 40 repeats, batch size 6, LR1e-6, EveryDream * balance : Strong, burgers * **Activation token :** `Burgy` * [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/291122/ckpts/Burgy.ckpt) * [Dataset](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/291122/dataset_Burgy.zip) # 22 novembre 2022, "Imaginary Friend" ## Theme : Imaginary Friend Do you remember putting your hands into what seemed as if it were just plain air and giggling like a child? Having conversations with someone who “wasn’t there”? Nowadays the term “Imaginary Friend” isn’t as frequently used as it used to be, right? Let’s bring it back. * Can you build your Imaginary Friends actualized? * What traits do you recall of them? Are they still young? Have they grown up now? Do they resemble you, or a creature that isn’t human? * Where would you find this Imaginary Friend? Where do they reside? What do they stand for? Our prompt for this event was created by @Andrekerygma "a boy drinking tea with a cute monster on the bedroom, disney infinity character design, pixar, artstation, vinyl, toy, figurine, 3 d model, cinema 4 d, substance 3 d painter, vray, unreal engine 5, octane render, cinematic" ![PoW](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/221122/images/theme.png) ## Models ### PoW ArtStyle 22-11-22 ![PoW ArtStyle](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/221122/images/showcase_pow_imaginary_friend.jpg) * based on all the submissions to the PoW * training: 73 pictures, 6000 steps on batch 6, 1e-6 polynomial LR. * balance : a little lighter on the style than last week, still manages to reproduce most participants * **Activation token :** `PoW ArtStyle` * Other noticable tokens : Your Discord username, if you participated. Also TMNT,NikeAir Shoes and Sid, Ice Age movie * [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/221122/ckpts/PoWArtStyle_ImaginaryFriend.ckpt) * [Dataset](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/221122/PoW_221122_dataset.zip) ### CharacterChan Style ![CharacterChan Style](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/images/showcase_CharacterChanStyle-v1.jpg) * based on the "Character" dreamer community of the Stable Diffusion Discord * training: 50 pictures, 160 total repeat, LR1e-6 * balance : correct, but some sub concepts have overtrain a little, like the clown. * **Activation token :** `CharacterChan Style` * [CKPT](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/ckpt/CharacterChanStyle-v1.ckpt) * [Dataset](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/datasets/CharacterChanStyle-v1.zip) * [Model page](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection#characterchan-style) ### CreatureChan Style ![CreatureChan Style](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/images/showcase_CreatureChanStyle-v1.jpg) * based on the "Creature" dreamer community of the Stable Diffusion Discord * training: 50 pictures, 160 total repeat, LR1e-6 * balance : good * **Activation token :** `CreatureChan Style` * [CKPT](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/ckpt/CreatureChanStyle-v1.ckpt) * [Dataset](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/datasets/CreatureChanStyle-v1.zip) * [Model page](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection#creaturechan-style) # 14 novembre 2022, "The Never-Ending Loop" ## Theme : The Never-Ending Loop It is a passed-down proverb that lines represent the flow of time itself. They converge and take shape. They twist, tangle, sometimes unravel, break, and then connect again. * Without words, how are we able to accurately represent this flow of time with only lines? geometrically, intricately, asymmetricaly, seamlessly, ornately... * Think of a never-ending pattern, texture, or shape– looping on and on for what feels infinite. * Just how detailed are you able to get with your patterns? Our prompt for this event was created by @Asukii ! "the fractal flow of time stretches towards the horizon, surreal fractal intertwined looping pathways, dramatic cinematic perspective, detailed delicate intricate ornate linework, geometric abstract masterwork digital art, quantum wavetracing, ink drawing, optical illusion" ![PoW](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/images/theme1.png) ![PoW](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/images/theme2.png) ## Models ### PoW Style 14-11-22 ![PoW Style](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/images/showcase_PoW_neverendingloop.jpg) * based on all the submissions to the PoW * training: 101 pictures, 9000 steps on batch 6, 1e-6 polynomial LR. * balance : a little strong on the style but it made it possible to differentiate each participants * **Activation token :** `PoW Style` * Other noticable tokens : Your Discord username, if you participated. Also Rick Roll and "fullbody shot" * [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/ckpts/PoWStyle_NeverEndingLoop.ckpt) * [Diffusers : Guizmus/SD_PoW_Collection/141122/diffusers](https://huggingface.co/Guizmus/SD_PoW_Collection/tree/main/141122/diffusers/) * [Dataset](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/PoW_141122_2_dataset.zip) ### Fractime Style ![Fractime Style](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/images/showcase_FractimeStyle.jpg) * based on the suggested prompt and theme * training: 50 pictures, 1750 steps on batch 6, 1e-6 polynomial LR. * balance : correct, but the style doesn't apply to every subject * **Activation token :** `Fractime Style` * Other noticable tokens : intricate, nebula, illusion, person, road, tree, boat * [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/ckpts/FractimeStyle.ckpt) * [Dataset](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/141122/PoW_141122_1_dataset.zip) # 09 novembre 2022, "Abstralities" ## Theme : Abstract Realities Glitch, warp, static, shape, flicker, break, bend, mend Have you ever felt your reality shift out from under your feet? Our perception falters and repairs itself in the blink of an eye. Just how much do our brains influence what we perceive? How much control do we have over molding these realities? With the introduction of AI and its rapid pace taking the world by storm, we are seeing single-handedly just how these realities can bring worlds into fruition. * Can you show us your altered reality? * Are these realities truly broken, or only bent? Our example prompt for this event was created by @Aether ! "household objects floating in space, bedroom, furniture, home living, warped reality, cosmic horror, nightmare, retrofuturism, surrealism, abstract, illustrations by alan nasmith" ![PoW](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/images/AETHER.png) ![PoW](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/images/aether2.png) ## Models ### PoW Style 09-11-22 ![PoW Style](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/images/showcase_pow_final.jpg) * Main model based on all the results from the PoW * training: 51 pictures, 3000 steps on 1e-6 polynomial LR. * balanced on the light side, add attention/weight on the activation token * **Activation token :** `PoW Style` * [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/ckpts/PoWStyle_Abstralities.ckpt) * [Diffusers : Guizmus/SD_PoW_Collection/091122/diffusers](https://huggingface.co/Guizmus/SD_PoW_Collection/tree/main/091122/diffusers/) * [Dataset](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/dataset.zip) ### Bendstract Style ![Bendstract Style](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/images/showcase_bendstract.jpg) * based on the suggested prompt * training: 100 pictures, 7500 steps on 1e-6 polynomial LR. overtrained * **Activation token :** `Bendstract Style` * [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/ckpts/Bendstract-v1.ckpt) ### endingReality Style ![BendingReality Style](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/images/showcase_bendingreality.jpg) * based on the suggested prompt * training: 68 pictures, 6000 steps on 1e-6 polynomial LR. overtrained * **Activation token :** `BendingReality Style` * [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/ckpts/BendingReality_Style-v1.ckpt) ### PoW Style mid-submissions 09-11-22 ![PoW Style](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/images/showcase_pow_midrun.jpg) * based on the first few submissions * training: 24 pictures, 2400 steps on 1e-6 polynomial LR. a little too trained * **Activation token :** `PoW Style` * [CKPT](https://huggingface.co/Guizmus/SD_PoW_Collection/resolve/main/091122/ckpts/PoWStyle_midrun.ckpt) # License These models are open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

epsil/sd-class-butterflies-64

epsil

2022-11-29T18:13:23Z

5

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T18:13:12Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(epsil/sd-class-butterflies-64) image = pipeline().images[0] image ```

epsil/sd-class-butterflies-32

epsil

2022-11-29T17:42:54Z

6

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T17:42:32Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(epsil/sd-class-butterflies-32) image = pipeline().images[0] image ```

ser-mei/borges-gpt-collab

ser-mei

2022-11-29T17:14:30Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-06T20:48:40Z

--- license: mit tags: - generated_from_trainer model-index: - name: borges-gpt-collab results: [] ---  # borges-gpt-collab This model is a fine-tuned version of [DeepESP/gpt2-spanish](https://huggingface.co/DeepESP/gpt2-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 8.3468 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 70 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 11.2135 | 0.96 | 7 | 10.2022 | | 10.3195 | 1.96 | 14 | 9.6343 | | 9.9127 | 2.96 | 21 | 9.4637 | | 9.7295 | 3.96 | 28 | 9.2993 | | 9.527 | 4.96 | 35 | 9.0962 | | 9.2648 | 5.96 | 42 | 8.8294 | | 8.9309 | 6.96 | 49 | 8.5103 | | 8.5639 | 7.96 | 56 | 8.1858 | | 8.2034 | 8.96 | 63 | 7.8816 | | 7.8665 | 9.96 | 70 | 7.6303 | | 7.5715 | 10.96 | 77 | 7.4307 | | 7.3259 | 11.96 | 84 | 7.2632 | | 7.136 | 12.96 | 91 | 7.1494 | | 6.9558 | 13.96 | 98 | 7.0957 | | 6.8068 | 14.96 | 105 | 7.0199 | | 6.6656 | 15.96 | 112 | 6.9554 | | 6.5264 | 16.96 | 119 | 6.9324 | | 6.3843 | 17.96 | 126 | 6.8940 | | 6.2204 | 18.96 | 133 | 6.8799 | | 6.0915 | 19.96 | 140 | 6.8788 | | 5.9532 | 20.96 | 147 | 6.8719 | | 5.8169 | 21.96 | 154 | 6.8647 | | 5.6531 | 22.96 | 161 | 6.8865 | | 5.5125 | 23.96 | 168 | 6.8940 | | 5.3666 | 24.96 | 175 | 6.9248 | | 5.2377 | 25.96 | 182 | 6.9421 | | 5.1115 | 26.96 | 189 | 6.9631 | | 4.9639 | 27.96 | 196 | 7.0135 | | 4.824 | 28.96 | 203 | 7.0352 | | 4.6886 | 29.96 | 210 | 7.0729 | | 4.5538 | 30.96 | 217 | 7.1385 | | 4.4126 | 31.96 | 224 | 7.1561 | | 4.2486 | 32.96 | 231 | 7.1792 | | 4.0955 | 33.96 | 238 | 7.2767 | | 3.9333 | 34.96 | 245 | 7.2815 | | 3.7914 | 35.96 | 252 | 7.3463 | | 3.618 | 36.96 | 259 | 7.3864 | | 3.4453 | 37.96 | 266 | 7.4394 | | 3.2795 | 38.96 | 273 | 7.4730 | | 3.0994 | 39.96 | 280 | 7.4880 | | 2.9143 | 40.96 | 287 | 7.5567 | | 2.741 | 41.96 | 294 | 7.5451 | | 2.5698 | 42.96 | 301 | 7.5966 | | 2.3855 | 43.96 | 308 | 7.6898 | | 2.2059 | 44.96 | 315 | 7.6957 | | 2.0634 | 45.96 | 322 | 7.7503 | | 1.8719 | 46.96 | 329 | 7.8369 | | 1.7059 | 47.96 | 336 | 7.8411 | | 1.54 | 48.96 | 343 | 7.8316 | | 1.3768 | 49.96 | 350 | 7.8630 | | 1.2177 | 50.96 | 357 | 7.9360 | | 1.0663 | 51.96 | 364 | 7.9886 | | 0.9569 | 52.96 | 371 | 8.0187 | | 0.8281 | 53.96 | 378 | 8.0274 | | 0.7074 | 54.96 | 385 | 8.1010 | | 0.6095 | 55.96 | 392 | 8.1594 | | 0.5262 | 56.96 | 399 | 8.1010 | | 0.4678 | 57.96 | 406 | 8.1440 | | 0.4105 | 58.96 | 413 | 8.1638 | | 0.3766 | 59.96 | 420 | 8.1534 | | 0.3425 | 60.96 | 427 | 8.1980 | | 0.321 | 61.96 | 434 | 8.2184 | | 0.3061 | 62.96 | 441 | 8.2499 | | 0.2852 | 63.96 | 448 | 8.1690 | | 0.2698 | 64.96 | 455 | 8.2160 | | 0.2628 | 65.96 | 462 | 8.2616 | | 0.2619 | 66.96 | 469 | 8.2948 | | 0.2544 | 67.96 | 476 | 8.3553 | | 0.2414 | 68.96 | 483 | 8.3712 | | 0.2177 | 69.96 | 490 | 8.3468 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+rocm5.2 - Datasets 2.6.1 - Tokenizers 0.13.2

SALT-NLP/FLANG-DistilBERT

SALT-NLP

2022-11-29T17:07:13Z

14

0

transformers

[ "transformers", "pytorch", "distilbert", "fill-mask", "Financial Language Modelling", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-24T05:43:00Z

--- language: "en" tags: - Financial Language Modelling widget: - text: "Stocks rallied and the British pound [MASK]." --- ## Dataset Summary - **Homepage:** https://salt-nlp.github.io/FLANG/ - **Models:** https://huggingface.co/SALT-NLP/FLANG-BERT - **Repository:** https://github.com/SALT-NLP/FLANG ## FLANG FLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferential masking to build more robust representations for the domain. The models in the set are:\ [FLANG-BERT](https://huggingface.co/SALT-NLP/FLANG-BERT)\ [FLANG-SpanBERT](https://huggingface.co/SALT-NLP/FLANG-SpanBERT)\ [FLANG-DistilBERT](https://huggingface.co/SALT-NLP/FLANG-DistilBERT)\ [FLANG-Roberta](https://huggingface.co/SALT-NLP/FLANG-Roberta)\ [FLANG-ELECTRA](https://huggingface.co/SALT-NLP/FLANG-ELECTRA) ## FLANG-DistilBERT FLANG-DistilBERT is a pre-trained language model which uses financial keywords and phrases for preferential masking of domain specific terms. It is built by further training the DistilBERT language model in the finance domain with improved performance over previous models due to the use of domain knowledge and vocabulary. ## FLUE FLUE (Financial Language Understanding Evaluation) is a comprehensive and heterogeneous benchmark that has been built from 5 diverse financial domain specific datasets. Sentiment Classification: [Financial PhraseBank](https://huggingface.co/datasets/financial_phrasebank)\ Sentiment Analysis, Question Answering: [FiQA 2018](https://huggingface.co/datasets/SALT-NLP/FLUE-FiQA)\ New Headlines Classification: [Headlines](https://www.kaggle.com/datasets/daittan/gold-commodity-news-and-dimensions)\ Named Entity Recognition: [NER](https://paperswithcode.com/dataset/fin)\ Structure Boundary Detection: [FinSBD3](https://sites.google.com/nlg.csie.ntu.edu.tw/finweb2021/shared-task-finsbd-3) ## Citation Please cite the work with the following citation: ```bibtex @INPROCEEDINGS{shah-etal-2022-flang, author = {Shah, Raj Sanjay and Chawla, Kunal and Eidnani, Dheeraj and Shah, Agam and Du, Wendi and Chava, Sudheer and Raman, Natraj and Smiley, Charese and Chen, Jiaao and Yang, Diyi }, title = {When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain}, booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2022}, publisher = {Association for Computational Linguistics} } ``` ## Contact information Please contact Raj Sanjay Shah (rajsanjayshah[at]gatech[dot]edu) or Sudheer Chava (schava6[at]gatech[dot]edu) or Diyi Yang (diyiy[at]stanford[dot]edu) about any FLANG-DistilBERT related issues and questions. --- license: afl-3.0 ---

SALT-NLP/FLANG-SpanBERT

SALT-NLP

2022-11-29T17:06:55Z

30

0

transformers

[ "transformers", "pytorch", "bert", "fill-mask", "Financial Language Modelling", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-24T05:41:56Z

--- language: "en" tags: - Financial Language Modelling widget: - text: "Stocks rallied and the British pound [MASK]." --- ## Dataset Summary - **Homepage:** https://salt-nlp.github.io/FLANG/ - **Models:** https://huggingface.co/SALT-NLP/FLANG-BERT - **Repository:** https://github.com/SALT-NLP/FLANG ## FLANG FLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferential masking to build more robust representations for the domain. The models in the set are:\ [FLANG-BERT](https://huggingface.co/SALT-NLP/FLANG-BERT)\ [FLANG-SpanBERT](https://huggingface.co/SALT-NLP/FLANG-SpanBERT)\ [FLANG-DistilBERT](https://huggingface.co/SALT-NLP/FLANG-DistilBERT)\ [FLANG-Roberta](https://huggingface.co/SALT-NLP/FLANG-Roberta)\ [FLANG-ELECTRA](https://huggingface.co/SALT-NLP/FLANG-ELECTRA) ## FLANG-SpanBERT FLANG-SpanBERT is a pre-trained language model which uses financial keywords and phrases for preferential masking of domain specific terms. It is built by further training the SpanBERT language model in the finance domain with improved performance over previous models due to the use of domain knowledge and vocabulary. ## FLUE FLUE (Financial Language Understanding Evaluation) is a comprehensive and heterogeneous benchmark that has been built from 5 diverse financial domain specific datasets. Sentiment Classification: [Financial PhraseBank](https://huggingface.co/datasets/financial_phrasebank)\ Sentiment Analysis, Question Answering: [FiQA 2018](https://huggingface.co/datasets/SALT-NLP/FLUE-FiQA)\ New Headlines Classification: [Headlines](https://www.kaggle.com/datasets/daittan/gold-commodity-news-and-dimensions)\ Named Entity Recognition: [NER](https://paperswithcode.com/dataset/fin)\ Structure Boundary Detection: [FinSBD3](https://sites.google.com/nlg.csie.ntu.edu.tw/finweb2021/shared-task-finsbd-3) ## Citation Please cite the model with the following citation: ```bibtex @INPROCEEDINGS{shah-etal-2022-flang, author = {Shah, Raj Sanjay and Chawla, Kunal and Eidnani, Dheeraj and Shah, Agam and Du, Wendi and Chava, Sudheer and Raman, Natraj and Smiley, Charese and Chen, Jiaao and Yang, Diyi }, title = {When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain}, booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2022}, publisher = {Association for Computational Linguistics} } ``` ## Contact information Please contact Raj Sanjay Shah (rajsanjayshah[at]gatech[dot]edu) or Sudheer Chava (schava6[at]gatech[dot]edu) or Diyi Yang (diyiy[at]stanford[dot]edu) about any FLANG-SpanBERT related issues and questions. --- license: afl-3.0 ---

SALT-NLP/FLANG-BERT

SALT-NLP

2022-11-29T17:06:37Z

83

4

transformers

[ "transformers", "pytorch", "bert", "fill-mask", "Financial Language Modelling", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-24T02:37:04Z

--- language: "en" tags: - Financial Language Modelling widget: - text: "Stocks rallied and the British pound [MASK]." --- ## Dataset Summary - **Homepage:** https://salt-nlp.github.io/FLANG/ - **Models:** https://huggingface.co/SALT-NLP/FLANG-BERT - **Repository:** https://github.com/SALT-NLP/FLANG ## FLANG FLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferential masking to build more robust representations for the domain. The models in the set are:\ [FLANG-BERT](https://huggingface.co/SALT-NLP/FLANG-BERT)\ [FLANG-SpanBERT](https://huggingface.co/SALT-NLP/FLANG-SpanBERT)\ [FLANG-DistilBERT](https://huggingface.co/SALT-NLP/FLANG-DistilBERT)\ [FLANG-Roberta](https://huggingface.co/SALT-NLP/FLANG-Roberta)\ [FLANG-ELECTRA](https://huggingface.co/SALT-NLP/FLANG-ELECTRA) ## FLANG-BERT FLANG-BERT is a pre-trained language model which uses financial keywords and phrases for preferential masking of domain specific terms. It is built by further training the BERT language model in the finance domain with improved performance over previous models due to the use of domain knowledge and vocabulary. ## FLUE FLUE (Financial Language Understanding Evaluation) is a comprehensive and heterogeneous benchmark that has been built from 5 diverse financial domain specific datasets. Sentiment Classification: [Financial PhraseBank](https://huggingface.co/datasets/financial_phrasebank)\ Sentiment Analysis, Question Answering: [FiQA 2018](https://huggingface.co/datasets/SALT-NLP/FLUE-FiQA)\ New Headlines Classification: [Headlines](https://www.kaggle.com/datasets/daittan/gold-commodity-news-and-dimensions)\ Named Entity Recognition: [NER](https://paperswithcode.com/dataset/fin)\ Structure Boundary Detection: [FinSBD3](https://sites.google.com/nlg.csie.ntu.edu.tw/finweb2021/shared-task-finsbd-3) ## Citation Please cite the model with the following citation: ```bibtex @INPROCEEDINGS{shah-etal-2022-flang, author = {Shah, Raj Sanjay and Chawla, Kunal and Eidnani, Dheeraj and Shah, Agam and Du, Wendi and Chava, Sudheer and Raman, Natraj and Smiley, Charese and Chen, Jiaao and Yang, Diyi }, title = {When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain}, booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2022}, publisher = {Association for Computational Linguistics} } ``` ## Contact information Please contact Raj Sanjay Shah (rajsanjayshah[at]gatech[dot]edu) or Sudheer Chava (schava6[at]gatech[dot]edu) or Diyi Yang (diyiy[at]stanford[dot]edu) about any FLANG-BERT related issues and questions. --- license: afl-3.0 ---

BKick/whisper-small_test3

BKick

2022-11-29T16:20:02Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-28T20:38:48Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: whisper-small_test3 results: [] ---  # whisper-small_test3 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2153 - eval_wer: 13.6949 - eval_runtime: 1589.8456 - eval_samples_per_second: 2.734 - eval_steps_per_second: 0.342 - epoch: 0.53 - step: 300 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - training_steps: 1000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

Alred/bart-base-finetuned-summarization-cnn-ver1.1

Alred

2022-11-29T16:17:56Z

9

0

transformers

[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "summarization", "generated_from_trainer", "dataset:cnn_dailymail", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2022-11-29T15:02:40Z

--- license: apache-2.0 tags: - summarization - generated_from_trainer datasets: - cnn_dailymail model-index: - name: bart-base-finetuned-summarization-cnn-ver1.1 results: [] ---  # bart-base-finetuned-summarization-cnn-ver1.1 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 2.3824 - Bertscore-mean-precision: 0.8904 - Bertscore-mean-recall: 0.8610 - Bertscore-mean-f1: 0.8753 - Bertscore-median-precision: 0.8893 - Bertscore-median-recall: 0.8606 - Bertscore-median-f1: 0.8744 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bertscore-mean-precision | Bertscore-mean-recall | Bertscore-mean-f1 | Bertscore-median-precision | Bertscore-median-recall | Bertscore-median-f1 | |:-------------:|:-----:|:-----:|:---------------:|:------------------------:|:---------------------:|:-----------------:|:--------------------------:|:-----------------------:|:-------------------:| | 2.4217 | 1.0 | 5742 | 2.3095 | 0.8824 | 0.8582 | 0.8700 | 0.8822 | 0.8559 | 0.8696 | | 1.7335 | 2.0 | 11484 | 2.2855 | 0.8907 | 0.8610 | 0.8754 | 0.8907 | 0.8600 | 0.8746 | | 1.3013 | 3.0 | 17226 | 2.3824 | 0.8904 | 0.8610 | 0.8753 | 0.8893 | 0.8606 | 0.8744 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

dulleto/sfe

dulleto

2022-11-29T16:11:29Z

0

null

[ "license:bigscience-openrail-m", "region:us" ]

null

2022-11-29T16:11:29Z

--- license: bigscience-openrail-m ---

kejian/debug-pt-conditional

kejian

2022-11-29T15:03:05Z

1

0

transformers

[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2022-11-29T14:52:56Z

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: debug-pt-conditional results: [] ---  # debug-pt-conditional This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0008 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 128, 'prefix': '<|aligned|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 128, 'prefix': '<|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'debug-pt-conditional', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 8, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 10, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/3my099dp

MatiasTamayo/videomae-base-finetuned-ucf101-subset

MatiasTamayo

2022-11-29T14:58:07Z

1

0

transformers

[ "transformers", "pytorch", "tensorboard", "videomae", "video-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

video-classification

2022-11-29T14:52:15Z

--- license: cc-by-nc-4.0 tags: - generated_from_trainer model-index: - name: videomae-base-finetuned-ucf101-subset results: [] ---  # videomae-base-finetuned-ucf101-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 148 ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

KPEKEP/rugpt_chitchat

KPEKEP

2022-11-29T14:48:36Z

42

1

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "PyTorch", "Transformers", "ru", "license:unlicense", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-29T14:48:34Z

--- pipeline_tag: text-generation tags: - PyTorch - Transformers - gpt2 license: unlicense language: ru widget: - text: >- - У Джульетты было 7 пончиков, а потом она 3 съела. Сколько у нее осталось пончиков? - - text: >- - Поглажено 4 манула. Осталось погладить 6. Сколько всего манулов надо погладить? - - text: '- Для начала скажи, чему равно пятью девять? -' - text: '- ты чё такой борзый? -' - text: '- Привет! Как ваше ничего? -' duplicated_from: inkoziev/rugpt_chitchat --- ## Russian Chit-chat, Deductive and Common Sense reasoning model Модель является ядром прототипа [диалоговой системы](https://github.com/Koziev/chatbot) с двумя основными функциями. Первая функция - **генерация реплик чит-чата**. В качестве затравки подается история диалога (предшествующие несколько реплик, от 1 до 10). ``` - Привет, как дела? - Привет, так себе. - <<< эту реплику ожидаем от модели >>> ``` Вторая функция модели - вывод ответа на заданный вопрос, опираясь на дополнительные факты или на "здравый смысл". Предполагается, что релевантные факты извлекаются из стороннего хранилища (базы знаний) с помощью другой модели, например [sbert_pq](https://huggingface.co/inkoziev/sbert_pq). Используя указанный факт(ы) и текст вопроса, модель построит грамматичный и максимально краткий ответ, как это сделал бы человек в подобной коммуникативной ситуации. Релевантные факты следует указывать перед текстом заданного вопроса так, будто сам собеседник сказал их: ``` - Сегодня 15 сентября. Какой сейчас у нас месяц? - Сентябрь ``` Модель не ожидает, что все найденные и добавленные в контекст диалога факты действительно имеют отношение к заданному вопросу. Поэтому модель, извлекающая из базы знаний информацию, может жертвовать точностью в пользу полноте и добавлять что-то лишнее. Модель читчата в этом случае сама выберет среди добавленных в контекст фактов необходимую фактуру и проигнорирует лишнее. Текущая версия модели допускает до 5 фактов перед вопросом. Например: ``` - Стасу 16 лет. Стас живет в Подольске. У Стаса нет своей машины. Где живет Стас? - в Подольске ``` В некоторых случаях модель может выполнять **силлогический вывод** ответа, опираясь на 2 предпосылки, связанные друг с другом. Выводимое из двух предпосылок следствие не фигурирует явно, а *как бы* используется для вывода ответа: ``` - Смертен ли Аристофан, если он был греческим философом, а все философы смертны? - Да ``` Как можно видеть из приведенных примеров, формат подаваемой на вход модели фактической информации для выполнения вывода предельно естественный и свободный. Кроме логического вывода, модель также умеет решать простые арифметические задачи в рамках 1-2 классов начальной школы, с двумя числовыми аргументами: ``` - Чему равно 2+8? - 10 ``` ### Варианты модели и метрики Выложенная на данный момент модель имеет 760 млн. параметров, т.е. уровня sberbank-ai/rugpt3large_based_on_gpt2. Далее приводится результат замера точности решения арифметических задач на отложенном тестовом наборе сэмплов: | base model | arith. accuracy | | --------------------------------------- | --------------- | | sberbank-ai/rugpt3large_based_on_gpt2 | 0.91 | | sberbank-ai/rugpt3medium_based_on_gpt2 | 0.70 | | sberbank-ai/rugpt3small_based_on_gpt2 | 0.58 | | tinkoff-ai/ruDialoGPT-small | 0.44 | | tinkoff-ai/ruDialoGPT-medium | 0.69 | Цифра 0.91 в столбце "arith. accuracy" означает, что 91% тестовых задач решено полностью верно. Любое отклонение сгенерированного ответа от эталонного рассматривается как ошибка. Например, выдача ответа "120" вместо "119" тоже фиксируется как ошибка. ### Пример использования ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM device = "cuda" if torch.cuda.is_available() else "cpu" model_name = "inkoziev/rugpt_chitchat" tokenizer = AutoTokenizer.from_pretrained(model_name) tokenizer.add_special_tokens({'bos_token': '<s>', 'eos_token': '</s>', 'pad_token': '<pad>'}) model = AutoModelForCausalLM.from_pretrained(model_name) model.to(device) model.eval() # На вход модели подаем последние 2-3 реплики диалога. Каждая реплика на отдельной строке, начинается с символа "-" input_text = """<s>- Привет! Что делаешь? - Привет :) В такси еду -""" encoded_prompt = tokenizer.encode(input_text, add_special_tokens=False, return_tensors="pt").to(device) output_sequences = model.generate(input_ids=encoded_prompt, max_length=100, num_return_sequences=1, pad_token_id=tokenizer.pad_token_id) text = tokenizer.decode(output_sequences[0].tolist(), clean_up_tokenization_spaces=True)[len(input_text)+1:] text = text[: text.find('</s>')] print(text) ``` ### Контакты Если у Вас есть какие-то вопросы по использованию этой модели, или предложения по ее улучшению - пишите мне mentalcomputing@gmail.com ### Citation: ``` @MISC{rugpt_chitchat, author = {Ilya Koziev}, title = {Russian Chit-chat with Common sence Reasoning}, url = {https://huggingface.co/inkoziev/rugpt_chitchat}, year = 2022 } ```

mzhou08/t5-base-finetuned-qg-medium-hard-qns

mzhou08

2022-11-29T14:09:43Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-29T13:37:37Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-base-finetuned-qg-medium-hard-qns results: [] ---  # t5-base-finetuned-qg-medium-hard-qns This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5919 - Rouge1: 38.6117 - Rouge2: 21.3082 - Rougel: 35.7294 - Rougelsum: 35.4192 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | No log | 1.0 | 73 | 1.8640 | 31.2085 | 11.6418 | 26.1137 | 26.2911 | | No log | 2.0 | 146 | 1.6488 | 29.6798 | 10.9223 | 26.7442 | 26.9736 | | No log | 3.0 | 219 | 1.6045 | 33.6703 | 11.7038 | 30.167 | 29.9192 | | No log | 4.0 | 292 | 1.5812 | 36.6758 | 17.748 | 33.739 | 33.4974 | | No log | 5.0 | 365 | 1.5879 | 33.3704 | 16.4099 | 31.7658 | 31.3874 | | No log | 6.0 | 438 | 1.5786 | 34.1216 | 14.9588 | 30.9584 | 30.9277 | | 1.7533 | 7.0 | 511 | 1.5804 | 34.8267 | 15.7046 | 32.0877 | 31.9317 | | 1.7533 | 8.0 | 584 | 1.5861 | 33.2539 | 12.728 | 30.551 | 30.2299 | | 1.7533 | 9.0 | 657 | 1.5911 | 38.4406 | 20.5922 | 36.4267 | 36.0426 | | 1.7533 | 10.0 | 730 | 1.5827 | 33.3421 | 16.0455 | 29.974 | 29.5357 | | 1.7533 | 11.0 | 803 | 1.5834 | 42.3363 | 24.6712 | 40.4291 | 40.0842 | | 1.7533 | 12.0 | 876 | 1.5889 | 33.268 | 15.5319 | 30.6942 | 30.4347 | | 1.7533 | 13.0 | 949 | 1.5911 | 42.1265 | 23.1983 | 39.5768 | 39.2304 | | 1.2341 | 14.0 | 1022 | 1.5926 | 35.0279 | 15.825 | 32.0736 | 32.0093 | | 1.2341 | 15.0 | 1095 | 1.5912 | 38.362 | 17.6108 | 35.3148 | 35.0558 | | 1.2341 | 16.0 | 1168 | 1.5919 | 38.6117 | 21.3082 | 35.7294 | 35.4192 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

Samael98/roberta-base-bne-finetuned-amazon_reviews_multi

Samael98

2022-11-29T14:08:39Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-29T13:46:19Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: roberta-base-bne-finetuned-amazon_reviews_multi results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: train args: es metrics: - name: Accuracy type: accuracy value: 0.93375 ---  # roberta-base-bne-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2313 - Accuracy: 0.9337 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1864 | 1.0 | 1250 | 0.2209 | 0.9317 | | 0.1063 | 2.0 | 2500 | 0.2313 | 0.9337 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

oskrmiguel/roberta-base-bne-clasificacion-de-texto-supervisado

oskrmiguel

2022-11-29T14:05:15Z

108

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-29T13:42:24Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: roberta-base-bne-clasificacion-de-texto-supervisado results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: train args: es metrics: - name: Accuracy type: accuracy value: 0.9335 ---  # roberta-base-bne-clasificacion-de-texto-supervisado This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2263 - Accuracy: 0.9335 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1934 | 1.0 | 1250 | 0.1700 | 0.9327 | | 0.1031 | 2.0 | 2500 | 0.2263 | 0.9335 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

yesyesjaewook/jets-jaewook-ukten-ko

yesyesjaewook

2022-11-29T14:02:17Z

6

0

espnet

[ "espnet", "audio", "text-to-speech", "ko", "dataset:Jaewook-Ukten", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]

text-to-speech

2022-11-27T13:55:18Z

--- tags: - espnet - audio - text-to-speech language: ko datasets: - Jaewook-Ukten license: cc-by-4.0 --- ## ESPnet2 TTS model ### yesyesjaewook/jets-jaewook-ukten-ko This model was trained by yesyesjaewook using jaewook_ukten recipe in [espnet](https://github.com/espnet/espnet/). ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

thliang01/sd-class-butterflies-32

thliang01

2022-11-29T13:42:36Z

35

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T13:42:21Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(thliang01/sd-class-butterflies-32) image = pipeline().images[0] image ```

kaizerkam/sd-class-comics-64

kaizerkam

2022-11-29T13:26:50Z

30

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T13:25:39Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of comic scenes. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(kaizerkam/sd-class-comics-64) image = pipeline().images[0] image ```

shivammehta25/sd-class-butterflies-32

shivammehta25

2022-11-29T12:46:27Z

35

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T12:46:12Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(shivammehta007/sd-class-butterflies-32) image = pipeline().images[0] image ```

pig4431/rtm_roBERTa_5E

pig4431

2022-11-29T12:34:52Z

104

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:rotten_tomatoes", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-29T11:02:18Z

--- license: mit tags: - generated_from_trainer datasets: - rotten_tomatoes metrics: - accuracy model-index: - name: rtm_roBERTa_5E results: - task: name: Text Classification type: text-classification dataset: name: rotten_tomatoes type: rotten_tomatoes config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8666666666666667 ---  # rtm_roBERTa_5E This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the rotten_tomatoes dataset. It achieves the following results on the evaluation set: - Loss: 0.6545 - Accuracy: 0.8667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6955 | 0.09 | 50 | 0.6752 | 0.7867 | | 0.5362 | 0.19 | 100 | 0.4314 | 0.8333 | | 0.4065 | 0.28 | 150 | 0.4476 | 0.8533 | | 0.3563 | 0.37 | 200 | 0.3454 | 0.8467 | | 0.3729 | 0.47 | 250 | 0.3421 | 0.86 | | 0.3355 | 0.56 | 300 | 0.3253 | 0.8467 | | 0.338 | 0.66 | 350 | 0.3859 | 0.8733 | | 0.2875 | 0.75 | 400 | 0.3537 | 0.8533 | | 0.3477 | 0.84 | 450 | 0.3636 | 0.8467 | | 0.3259 | 0.94 | 500 | 0.3115 | 0.88 | | 0.3204 | 1.03 | 550 | 0.4295 | 0.8333 | | 0.2673 | 1.12 | 600 | 0.3369 | 0.88 | | 0.2479 | 1.22 | 650 | 0.3620 | 0.8667 | | 0.2821 | 1.31 | 700 | 0.3582 | 0.8733 | | 0.2355 | 1.4 | 750 | 0.3130 | 0.8867 | | 0.2357 | 1.5 | 800 | 0.3229 | 0.86 | | 0.2725 | 1.59 | 850 | 0.3035 | 0.88 | | 0.2425 | 1.69 | 900 | 0.3146 | 0.8533 | | 0.1977 | 1.78 | 950 | 0.4079 | 0.86 | | 0.2557 | 1.87 | 1000 | 0.4132 | 0.8733 | | 0.2395 | 1.97 | 1050 | 0.3336 | 0.86 | | 0.1951 | 2.06 | 1100 | 0.5068 | 0.84 | | 0.1631 | 2.15 | 1150 | 0.5209 | 0.8867 | | 0.2192 | 2.25 | 1200 | 0.4766 | 0.8733 | | 0.1725 | 2.34 | 1250 | 0.3962 | 0.8667 | | 0.2215 | 2.43 | 1300 | 0.4133 | 0.8867 | | 0.1602 | 2.53 | 1350 | 0.5564 | 0.8533 | | 0.1986 | 2.62 | 1400 | 0.5826 | 0.86 | | 0.1972 | 2.72 | 1450 | 0.5412 | 0.8667 | | 0.2299 | 2.81 | 1500 | 0.4636 | 0.8733 | | 0.2028 | 2.9 | 1550 | 0.5096 | 0.8667 | | 0.2591 | 3.0 | 1600 | 0.3790 | 0.8467 | | 0.1197 | 3.09 | 1650 | 0.5704 | 0.8467 | | 0.174 | 3.18 | 1700 | 0.5904 | 0.8467 | | 0.1499 | 3.28 | 1750 | 0.6066 | 0.86 | | 0.1687 | 3.37 | 1800 | 0.6353 | 0.8533 | | 0.1463 | 3.46 | 1850 | 0.6434 | 0.8467 | | 0.1373 | 3.56 | 1900 | 0.6507 | 0.8533 | | 0.1339 | 3.65 | 1950 | 0.6014 | 0.86 | | 0.1488 | 3.75 | 2000 | 0.7245 | 0.84 | | 0.1725 | 3.84 | 2050 | 0.6214 | 0.86 | | 0.1443 | 3.93 | 2100 | 0.6446 | 0.8533 | | 0.1619 | 4.03 | 2150 | 0.6223 | 0.8533 | | 0.1153 | 4.12 | 2200 | 0.6579 | 0.8333 | | 0.1159 | 4.21 | 2250 | 0.6760 | 0.8667 | | 0.0948 | 4.31 | 2300 | 0.7172 | 0.8467 | | 0.1373 | 4.4 | 2350 | 0.7346 | 0.8467 | | 0.1463 | 4.49 | 2400 | 0.6453 | 0.8533 | | 0.0758 | 4.59 | 2450 | 0.6579 | 0.86 | | 0.16 | 4.68 | 2500 | 0.6556 | 0.8667 | | 0.112 | 4.78 | 2550 | 0.6490 | 0.88 | | 0.1151 | 4.87 | 2600 | 0.6525 | 0.8667 | | 0.2152 | 4.96 | 2650 | 0.6545 | 0.8667 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2

AlekseyKorshuk/125m-dalio-book-handwritten-io-constant-1e-6-v2

AlekseyKorshuk

2022-11-29T12:29:49Z

125

0

transformers

[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "dataset:AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2", "license:other", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-29T10:31:18Z

--- license: other tags: - generated_from_trainer datasets: - AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 metrics: - accuracy model-index: - name: 125m-dalio-book-handwritten-io-constant-1e-6-v2 results: - task: name: Causal Language Modeling type: text-generation dataset: name: AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 type: AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 metrics: - name: Accuracy type: accuracy value: 0.23359387091781458 ---  # 125m-dalio-book-handwritten-io-constant-1e-6-v2 This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 dataset. It achieves the following results on the evaluation set: - Loss: 3.0859 - Accuracy: 0.2336 - Perplexity: 21.8880 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Perplexity | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:| | 3.3352 | 0.01 | 1 | 3.1738 | 0.2305 | 23.8988 | | 3.3091 | 0.03 | 2 | 3.1738 | 0.2305 | 23.8988 | | 3.3347 | 0.04 | 3 | 3.1738 | 0.2305 | 23.8988 | | 3.1445 | 0.05 | 4 | 3.1738 | 0.2305 | 23.8988 | | 2.8918 | 0.07 | 5 | 3.1738 | 0.2305 | 23.8988 | | 3.2068 | 0.08 | 6 | 3.1738 | 0.2305 | 23.8988 | | 3.6245 | 0.09 | 7 | 3.1719 | 0.2305 | 23.8522 | | 3.2256 | 0.11 | 8 | 3.1719 | 0.2305 | 23.8522 | | 2.9991 | 0.12 | 9 | 3.1699 | 0.2305 | 23.8056 | | 3.3257 | 0.13 | 10 | 3.1680 | 0.2306 | 23.7592 | | 3.1199 | 0.15 | 11 | 3.1660 | 0.2306 | 23.7128 | | 3.3735 | 0.16 | 12 | 3.1660 | 0.2306 | 23.7128 | | 3.0051 | 0.17 | 13 | 3.1641 | 0.2307 | 23.6665 | | 3.2695 | 0.19 | 14 | 3.1621 | 0.2308 | 23.6204 | | 3.2004 | 0.2 | 15 | 3.1602 | 0.2309 | 23.5743 | | 3.2075 | 0.21 | 16 | 3.1582 | 0.2308 | 23.5283 | | 3.321 | 0.23 | 17 | 3.1562 | 0.2308 | 23.4824 | | 3.4026 | 0.24 | 18 | 3.1543 | 0.2309 | 23.4366 | | 3.0383 | 0.25 | 19 | 3.1523 | 0.2309 | 23.3908 | | 3.166 | 0.27 | 20 | 3.1504 | 0.2309 | 23.3452 | | 3.144 | 0.28 | 21 | 3.1484 | 0.2310 | 23.2996 | | 3.1624 | 0.29 | 22 | 3.1484 | 0.2310 | 23.2996 | | 3.0332 | 0.31 | 23 | 3.1465 | 0.2310 | 23.2542 | | 3.3745 | 0.32 | 24 | 3.1445 | 0.2311 | 23.2088 | | 3.0823 | 0.33 | 25 | 3.1426 | 0.2312 | 23.1635 | | 3.6021 | 0.35 | 26 | 3.1406 | 0.2312 | 23.1183 | | 3.1125 | 0.36 | 27 | 3.1387 | 0.2313 | 23.0732 | | 3.1406 | 0.37 | 28 | 3.1387 | 0.2314 | 23.0732 | | 3.1736 | 0.39 | 29 | 3.1367 | 0.2314 | 23.0282 | | 3.1104 | 0.4 | 30 | 3.1348 | 0.2315 | 22.9832 | | 3.1301 | 0.41 | 31 | 3.1328 | 0.2316 | 22.9384 | | 3.3376 | 0.43 | 32 | 3.1309 | 0.2315 | 22.8936 | | 3.218 | 0.44 | 33 | 3.1309 | 0.2316 | 22.8936 | | 3.0786 | 0.45 | 34 | 3.1289 | 0.2316 | 22.8490 | | 3.0125 | 0.47 | 35 | 3.1270 | 0.2317 | 22.8044 | | 3.2634 | 0.48 | 36 | 3.1270 | 0.2317 | 22.8044 | | 2.9888 | 0.49 | 37 | 3.125 | 0.2318 | 22.7599 | | 3.1624 | 0.51 | 38 | 3.1230 | 0.2318 | 22.7155 | | 2.9807 | 0.52 | 39 | 3.1211 | 0.2319 | 22.6712 | | 3.446 | 0.53 | 40 | 3.1211 | 0.2319 | 22.6712 | | 3.1338 | 0.55 | 41 | 3.1191 | 0.2320 | 22.6269 | | 3.1841 | 0.56 | 42 | 3.1191 | 0.2320 | 22.6269 | | 3.1079 | 0.57 | 43 | 3.1172 | 0.2320 | 22.5828 | | 3.0918 | 0.59 | 44 | 3.1152 | 0.2321 | 22.5387 | | 3.0302 | 0.6 | 45 | 3.1152 | 0.2322 | 22.5387 | | 3.1123 | 0.61 | 46 | 3.1133 | 0.2323 | 22.4947 | | 2.9985 | 0.63 | 47 | 3.1113 | 0.2324 | 22.4508 | | 3.3816 | 0.64 | 48 | 3.1113 | 0.2324 | 22.4508 | | 3.0813 | 0.65 | 49 | 3.1094 | 0.2324 | 22.4070 | | 3.2024 | 0.67 | 50 | 3.1094 | 0.2325 | 22.4070 | | 3.0178 | 0.68 | 51 | 3.1074 | 0.2325 | 22.3633 | | 3.1646 | 0.69 | 52 | 3.1074 | 0.2326 | 22.3633 | | 3.0046 | 0.71 | 53 | 3.1055 | 0.2327 | 22.3197 | | 3.0266 | 0.72 | 54 | 3.1055 | 0.2327 | 22.3197 | | 3.3857 | 0.73 | 55 | 3.1035 | 0.2327 | 22.2761 | | 3.064 | 0.75 | 56 | 3.1035 | 0.2328 | 22.2761 | | 3.176 | 0.76 | 57 | 3.1016 | 0.2328 | 22.2327 | | 3.1851 | 0.77 | 58 | 3.1016 | 0.2329 | 22.2327 | | 3.0811 | 0.79 | 59 | 3.0996 | 0.2329 | 22.1893 | | 3.0205 | 0.8 | 60 | 3.0996 | 0.2330 | 22.1893 | | 3.26 | 0.81 | 61 | 3.0977 | 0.2330 | 22.1460 | | 3.2922 | 0.83 | 62 | 3.0977 | 0.2331 | 22.1460 | | 3.5349 | 0.84 | 63 | 3.0957 | 0.2331 | 22.1028 | | 3.3525 | 0.85 | 64 | 3.0957 | 0.2331 | 22.1028 | | 3.135 | 0.87 | 65 | 3.0938 | 0.2331 | 22.0596 | | 3.1707 | 0.88 | 66 | 3.0938 | 0.2332 | 22.0596 | | 3.0127 | 0.89 | 67 | 3.0918 | 0.2332 | 22.0166 | | 3.0952 | 0.91 | 68 | 3.0918 | 0.2332 | 22.0166 | | 3.1023 | 0.92 | 69 | 3.0898 | 0.2334 | 21.9736 | | 3.3821 | 0.93 | 70 | 3.0898 | 0.2334 | 21.9736 | | 3.1118 | 0.95 | 71 | 3.0879 | 0.2334 | 21.9308 | | 3.1143 | 0.96 | 72 | 3.0879 | 0.2335 | 21.9308 | | 3.1118 | 0.97 | 73 | 3.0879 | 0.2335 | 21.9308 | | 3.0596 | 0.99 | 74 | 3.0859 | 0.2336 | 21.8880 | | 3.1033 | 1.0 | 75 | 3.0859 | 0.2336 | 21.8880 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

nlp-tlp/mwo-ner

nlp-tlp

2022-11-29T12:00:39Z

4

3

flair

[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "en", "dataset:mwo_ner", "region:us" ]

token-classification

2022-11-29T11:58:19Z

--- tags: - flair - token-classification - sequence-tagger-model language: en datasets: - mwo_ner widget: - text: "replace seal on pump" --- ## MWO NER Test A flair-based NER model for MWOs. There are three classes: `Item`, `Activity`, and `Observation`.

clp/segformer-b0-scene-parse-150

clp

2022-11-29T11:47:29Z

46

0

transformers

[ "transformers", "pytorch", "tensorboard", "segformer", "generated_from_trainer", "dataset:scene_parse_150", "license:other", "endpoints_compatible", "region:us" ]

null

2022-11-28T15:53:44Z

--- license: other tags: - generated_from_trainer datasets: - scene_parse_150 model-index: - name: segformer-b0-scene-parse-150 results: [] ---  # segformer-b0-scene-parse-150 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset. It achieves the following results on the evaluation set: - Loss: 2.3118 - Mean Iou: 0.0859 - Mean Accuracy: 0.1493 - Overall Accuracy: 0.5430 - Per Category Iou: [0.4898642376841085, 0.502026813829342, 0.9487341030299479, 0.44331193050176815, 0.28502594514455154, 0.5132976114794296, 0.8390207156308851, 0.0, 0.30530825819472024, 0.0, 0.06594624784212842, 0.0, 0.03397963180571876, 0.0, 0.0007459827819109256, 0.0, nan, 0.04554975143210437, 0.0, 0.07792795056021705, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] - Per Category Accuracy: [0.8215553632658342, 0.819071257846768, 0.9731147245348802, 0.8672811704363634, 0.9004683840749415, 0.594073476114797, 0.9732440887086908, 0.0, 0.40956851614311834, 0.0, 0.5229850345614389, 0.0, 0.034648027958062905, nan, 0.0007464041475862904, 0.0, nan, 0.0476077438413251, 0.0, 0.5009150608246313, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | 4.2849 | 1.0 | 20 | 4.2070 | 0.0194 | 0.0679 | 0.3746 | [0.3949829725229674, 0.4135772915291814, 0.0, 0.26980840849544657, 0.1282559786684443, 0.15076540186066723, 0.00908901592761032, 0.0, 0.013565775517419566, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0003970617431010522, 0.008885447023579041, 0.0, 0.0, 0.0, 0.005040122024006897, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.002655312914892643, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan] | [0.9524952286989713, 0.755535418800725, 0.0, 0.48244304323326054, 0.9011709601873537, 0.16045676614279827, 0.011822582618269517, 0.0, 0.013613165579542043, 0.0, 0.0, 0.0, 0.0, nan, 0.0004034617013979948, 0.07362999240057418, nan, 0.0, 0.0, 0.04090860157175153, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0034295175023651846, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | | 3.7699 | 2.0 | 40 | 3.9727 | 0.0380 | 0.1002 | 0.4224 | [0.43442101571739283, 0.35923049538654755, 0.6190543160517142, 0.3355717837774341, 0.10588647687723779, 0.31387526278906797, 0.038367652125468235, 0.0, 0.04485789722234148, 0.0, 0.07154189015637985, 0.0, 0.0, 0.0, 0.004233122680308957, 0.0003664849512116909, 0.0, 0.0, 0.0, 0.0284352014981458, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0007874615794955165, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.9416870188264217, 0.6175399889685604, 0.926106819752938, 0.704992945957396, 0.9903981264637002, 0.35779874372493126, 0.04573495744569302, 0.0, 0.045626483993340794, 0.0, 0.12710556338500772, 0.0, 0.0, nan, 0.004256520949748845, 0.0013510090348729208, nan, 0.0, 0.0, 0.24846592744105933, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0009165089877010407, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | | 3.7161 | 3.0 | 60 | 3.6079 | 0.0535 | 0.1182 | 0.4695 | [0.46147636301683304, 0.42190388170055454, 0.5905298672673311, 0.34255470866251286, 0.10127362853686882, 0.2992112324204293, 0.616176968407381, 0.0, 0.031915928772988225, 0.0, 0.05093061049274829, 0.0, 0.0, 0.0, 0.006854943777434808, 0.021351935880581825, nan, 0.0002887139107611549, 0.0, 0.052485352485352486, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.9419053665006146, 0.7625719013474116, 0.9179916443749813, 0.6481040461001222, 1.0, 0.32631618778537375, 0.779355481379214, 0.0, 0.03222619469992631, 0.0, 0.11146902892423327, 0.0, 0.0, nan, 0.006899195093905711, 0.08502913113231444, nan, 0.00029013029487788154, 0.0, 0.2989557541177737, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | | 3.8074 | 4.0 | 80 | 3.4130 | 0.0568 | 0.1189 | 0.4832 | [0.475136642179407, 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0.9489863037726748, 0.453687342170703, 0.26604904256784684, 0.5300531090789863, 0.8582729222561327, 0.0, 0.3025795526473707, 0.0, 0.057744191168373524, 0.0, 0.061194895591647334, 0.0, 0.0, 0.0, nan, 0.0461902785576524, 0.0, 0.07613617021276596, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.8062831564986738, 0.785829853176792, 0.9756695019686814, 0.871238015325576, 0.9045667447306791, 0.6185178645005592, 0.9755596721483372, 0.0, 0.40182855271417267, 0.0, 0.5630494597678009, 0.0, 0.06320519221168247, nan, 0.0, 0.0, nan, 0.04990241071899562, 0.0, 0.48153730218538054, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | | 1.6687 | 48.0 | 960 | 2.2997 | 0.0839 | 0.1493 | 0.5419 | [0.4884039519989931, 0.5023855202911851, 0.9487134477260426, 0.4387315758616788, 0.2578810853950519, 0.5168101008425782, 0.8481733943976995, 0.0, 0.30145030552941604, 0.0, 0.06553419599907698, 0.0, 0.04092146189735614, 0.0, 6.051315152493142e-05, 0.0, nan, 0.03923789388905668, 0.0, 0.08363174912213608, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.8159248237044705, 0.8095238095238095, 0.974647590995161, 0.8772267535362759, 0.90807962529274, 0.602675680902769, 0.9741965362366963, 0.0, 0.4074124614502879, 0.0, 0.49553721226763303, 0.0, 0.04203694458312531, nan, 6.051925520969922e-05, 0.0, nan, 0.04111937542860157, 0.0, 0.5179244267413069, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | | 1.5107 | 49.0 | 980 | 2.3102 | 0.0833 | 0.1492 | 0.5368 | [0.48352497928524046, 0.48689002364782025, 0.9489120566960494, 0.44220181960314686, 0.2353657811850003, 0.5099299471402703, 0.8485805611101012, 0.0, 0.292445292371914, 0.0, 0.0640117658387975, 0.0, 0.062292844609085594, 0.0, 0.0001815211472136504, 0.0, nan, 0.040059637287969886, 0.0, 0.08392424840753396, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.8282109238532703, 0.777104509757571, 0.9738961858675723, 0.8748329035097461, 0.9275175644028103, 0.5860809094960605, 0.9789500358260079, 0.0, 0.39157228241587294, 0.0, 0.4907053217904839, 0.0, 0.06380429355966051, nan, 0.00018155776562909766, 0.0, nan, 0.043229413936804344, 0.0, 0.5262138012703197, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | | 1.3671 | 50.0 | 1000 | 2.3118 | 0.0859 | 0.1493 | 0.5430 | [0.4898642376841085, 0.502026813829342, 0.9487341030299479, 0.44331193050176815, 0.28502594514455154, 0.5132976114794296, 0.8390207156308851, 0.0, 0.30530825819472024, 0.0, 0.06594624784212842, 0.0, 0.03397963180571876, 0.0, 0.0007459827819109256, 0.0, nan, 0.04554975143210437, 0.0, 0.07792795056021705, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.8215553632658342, 0.819071257846768, 0.9731147245348802, 0.8672811704363634, 0.9004683840749415, 0.594073476114797, 0.9732440887086908, 0.0, 0.40956851614311834, 0.0, 0.5229850345614389, 0.0, 0.034648027958062905, nan, 0.0007464041475862904, 0.0, nan, 0.0476077438413251, 0.0, 0.5009150608246313, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

autoevaluate/binary-classification-not-evaluated

autoevaluate

2022-11-29T11:07:52Z

110

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-29T11:01:03Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue ---  # binary-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3009 - Accuracy: 0.8968 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.175 | 1.0 | 4210 | 0.3009 | 0.8968 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

supermy/poetry

supermy

2022-11-29T10:53:20Z

115

2

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "zh", "dataset:poetry", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-29T00:56:18Z

--- language: zh datasets: poetry inference: parameters: max_length: 108 num_return_sequences: 1 do_sample: True widget: - text: "物换星移几度秋" example_title: "滕王阁1" - text: "秋水共长天一色" example_title: "滕王阁 2" - text: "萍水相逢，尽是他乡之客。" example_title: "滕王阁 3" --- # 古诗词 ## Model description 古诗词AI生成 ## How to use 使用 pipeline 调用模型: ```python from transformers import AutoTokenizer, GPT2LMHeadModel, TextGenerationPipeline model_checkpoint = "supermy/poetry" tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) model = GPT2LMHeadModel.from_pretrained(model_checkpoint) text_generator = TextGenerationPipeline(model, tokenizer) text_generator.model.config.pad_token_id = text_generator.model.config.eos_token_id print(text_generator("举头望明月，", max_length=100, do_sample=True)) print(text_generator("物换星移几度秋，", max_length=100, do_sample=True)) >>> print(text_generator("举头望明月，", max_length=100, do_sample=True)) [{'generated_text': '举头望明月，何以喻无言。顾影若为舞，啸风清独伤。四时别有意，千古得从容。赏音我非此，何如鸥鹭群。崎山有佳色，落落样相宜。不嫌雪霜温，宁受四时肥。老态如偷面，冬心似相知。春风不可恃，触动春何为。岁晚忽然老，花前岁月深。可笑一场梦，婵娟乍自心。列名多岁月，森列尽林峦。试问影非笑'}] >>> print(text_generator("物换星移几度秋，", max_length=100, do_sample=True)) [{'generated_text': '物换星移几度秋，消长随时向一丘。渔者下逢勾漏令，漏声高出景阳丘。天津大尹昔从游，大尹来时春复秋。旗鼓日严宣使从，联镳歌笑又风流。冈峦比并瑶溪水，叠嶂高盘黼黻洲。花木芳菲三月天，莺花暖翠几流年。一从别后多携手，肠断酒阑怀凛然。北阙人称似梦中，西山别样梦魂香。多君观国亲圭璧，能预陇西称巨良。刷羽刷羽'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("supermy/poetry") model = AutoModelForCausalLM.from_pretrained("supermy/poetry") ``` ## Training data 非常全的古诗词数据，收录了从先秦到现代的共计85万余首古诗词。 ## 统计信息 | 朝代 | 诗词数 | 作者数 | |-----------------------|--------|--------| | 宋 | 287114 | 9446 | | 明 | 236957 | 4439 | | 清 | 90089 | 8872 | | 唐 | 49195 | 2736 | | 元 | 37375 | 1209 | | 近现代 | 28419 | 790 | | 当代 | 28219 | 177 | | 明末清初 | 17700 | 176 | | 元末明初 | 15736 | 79 | | 清末民国初 | 15367 | 99 | | 清末近现代初 | 12464 | 48 | | 宋末元初 | 12058 | 41 | | 南北朝 | 4586 | 434 | | 近现代末当代初 | 3426 | 23 | | 魏晋 | 3020 | 251 | | 金末元初 | 3019 | 17 | | 金 | 2741 | 253 | | 民国末当代初 | 1948 | 9 | | 隋 | 1170 | 84 | | 唐末宋初 | 1118 | 44 | | 先秦 | 570 | 8 | | 隋末唐初 | 472 | 40 | | 汉 | 363 | 83 | | 宋末金初 | 234 | 9 | | 辽 | 22 | 7 | | 秦 | 2 | 2 | | 魏晋末南北朝初 | 1 | 1 | | 总和 | 853385 | 29377 | ``` ``` ## Training procedure 模型：[GPT2](https://huggingface.co/gpt2) 训练环境：英伟达16G显卡 bpe分词："vocab_size"=50000 ``` ***** Running training ***** Num examples = 16431 Num Epochs = 680 Instantaneous batch size per device = 24 Total train batch size (w. parallel, distributed & accumulation) = 192 Gradient Accumulation steps = 8 Total optimization steps = 57800 Number of trainable parameters = 124242432 GPT-2 size: 124.2M parameters 0%| | 0/57800 [00:00<?, ?it/s]You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding. 9%|▊ | 5000/57800 [6:58:57<72:53:18, 4.97s/it]***** Running Evaluation ***** Num examples = 1755 Batch size = 24 {'loss': 3.1345, 'learning_rate': 0.0004939065828881268, 'epoch': 58.82} 9%|▊ | 5000/57800 [6:59:14<72:53:18, Saving model checkpoint to poetry-trainer/checkpoint-5000 Configuration saved in poetry-trainer/checkpoint-5000/config.json Model weights saved in poetry-trainer/checkpoint-5000/pytorch_model.bin tokenizer config file saved in poetry-trainer/checkpoint-5000/tokenizer_config.json Special tokens file saved in poetry-trainer/checkpoint-5000/special_tokens_map.json 17%|█▋ | 10000/57800 [13:55:32<65:40:41, 4.95s/it]***** Running Evaluation ***** Num examples = 1755 Batch size = 24 {'eval_loss': 11.14090633392334, 'eval_runtime': 16.8326, 'eval_samples_per_second': 104.262, 'eval_steps_per_second': 4.396, 'epoch': 58.82} {'loss': 0.2511, 'learning_rate': 0.00046966687938531824, 'epoch': 117.64} 17%|█▋ | 10000/57800 [13:55:48<65:40:41Saving model checkpoint to poetry-trainer/checkpoint-10000 .......... 95%|█████████▌| 55000/57800 [76:06:46<3:59:33, 5.13s/it]***** Running Evaluation ***** Num examples = 1755 Batch size = 24 {'eval_loss': 14.860174179077148, 'eval_runtime': 16.7826, 'eval_samples_per_second': 104.572, 'eval_steps_per_second': 4.409, 'epoch': 588.23} {'loss': 0.0083, 'learning_rate': 3.0262183266589473e-06, 'epoch': 647.06} 95%|█████████▌| 55000/57800 [76:07:03<3:59:33,Saving model checkpoint to poetry-trainer/checkpoint-55000 {'eval_loss': 14.830656051635742, 'eval_runtime': 16.7365, 'eval_samples_per_second': 104.86, 'eval_steps_per_second': 4.421, 'epoch': 647.06} {'train_runtime': 287920.5857, 'train_samples_per_second': 38.806, 'train_steps_per_second': 0.201, 'train_loss': 0.33751299874592816, 'epoch': 679.99} 100%|██████████| 57800/57800 [79:58:40<00:00, 4.93s/it] ``` ``` ### entry and citation info ``` ```

huggingtweets/mullen_usa-nasdaq

huggingtweets

2022-11-29T10:30:31Z

117

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-29T10:24:49Z

--- language: en thumbnail: http://www.huggingtweets.com/mullen_usa-nasdaq/1669717561312/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1521140484512620544/Ev6EIPlD_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1433904015834705921/tRPvxdFF_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Nasdaq & Mullen Automotive</div> <div style="text-align: center; font-size: 14px;">@mullen_usa-nasdaq</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Nasdaq & Mullen Automotive. | Data | Nasdaq | Mullen Automotive | | --- | --- | --- | | Tweets downloaded | 3250 | 963 | | Retweets | 663 | 188 | | Short tweets | 31 | 121 | | Tweets kept | 2556 | 654 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/352xmu00/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mullen_usa-nasdaq's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/x3hx0rfr) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/x3hx0rfr/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/mullen_usa-nasdaq') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

LuisQ/LuisQ_sd-class-butterflies-32

LuisQ

2022-11-29T10:18:33Z

35

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T16:17:40Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(LuisQ/LuisQ_sd-class-butterflies-32) image = pipeline().images[0] image ```

SayaEndo/distilbert-base-uncased-finetuned-squad-d5716d28

SayaEndo

2022-11-29T08:56:00Z

107

0

transformers

[ "transformers", "pytorch", "distilbert", "fill-mask", "question-answering", "en", "dataset:squad", "arxiv:1910.01108", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

question-answering

2022-11-29T08:44:02Z

--- language: - en thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg tags: - question-answering license: apache-2.0 datasets: - squad metrics: - squad --- # DistilBERT with a second step of distillation ## Model description This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation. In this version, the following pre-trained models were used: * Student: `distilbert-base-uncased` * Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1` ## Training data This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows: ```python from datasets import load_dataset squad = load_dataset('squad') ``` ## Training procedure ## Eval results | | Exact Match | F1 | |------------------|-------------|------| | DistilBERT paper | 79.1 | 86.9 | | Ours | 78.4 | 86.5 | The scores were calculated using the `squad` metric from `datasets`. ### BibTeX entry and citation info ```bibtex @misc{sanh2020distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, year={2020}, eprint={1910.01108}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

premsuresh/bart-finetuned-mathqa-moh

premsuresh

2022-11-29T08:42:54Z

172

0

transformers

[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-29T08:24:19Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-finetuned-mathqa-moh results: [] ---  # bart-finetuned-mathqa-moh This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

thivy/t5-base-finetuned-en-to-no

thivy

2022-11-29T08:21:44Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:opus_books", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-22T16:16:18Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - opus_books metrics: - bleu model-index: - name: t5-base-finetuned-en-to-no results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus_books type: opus_books args: en-no metrics: - name: Bleu type: bleu value: 4.8513 ---  # t5-base-finetuned-en-to-no This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the opus_books dataset. It achieves the following results on the evaluation set: - Loss: 2.9566 - Bleu: 4.8513 - Gen Len: 17.84 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 280 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:------:|:-------:| | 3.3949 | 1.0 | 788 | 2.7553 | 0.9274 | 18.1314 | | 2.8659 | 2.0 | 1576 | 2.5367 | 1.2755 | 18.1543 | | 2.7244 | 3.0 | 2364 | 2.3900 | 1.6351 | 18.0343 | | 2.5228 | 4.0 | 3152 | 2.2902 | 1.7125 | 18.0543 | | 2.4201 | 5.0 | 3940 | 2.2039 | 1.7217 | 18.0914 | | 2.3168 | 6.0 | 4728 | 2.1429 | 2.0474 | 18.08 | | 2.1856 | 7.0 | 5516 | 2.0772 | 2.228 | 18.0686 | | 2.12 | 8.0 | 6304 | 2.0333 | 2.1694 | 17.98 | | 2.0519 | 9.0 | 7092 | 1.9931 | 2.257 | 17.9914 | | 1.9856 | 10.0 | 7880 | 1.9540 | 2.489 | 18.04 | | 1.9164 | 11.0 | 8668 | 1.9266 | 2.5762 | 17.9629 | | 1.8864 | 12.0 | 9456 | 1.9036 | 2.8294 | 17.9857 | | 1.8276 | 13.0 | 10244 | 1.8695 | 2.9018 | 17.98 | | 1.7715 | 14.0 | 11032 | 1.8584 | 3.04 | 17.9886 | | 1.7302 | 15.0 | 11820 | 1.8487 | 2.9588 | 18.0057 | | 1.6768 | 16.0 | 12608 | 1.8155 | 3.1968 | 17.9943 | | 1.6564 | 17.0 | 13396 | 1.8137 | 3.3315 | 17.9657 | | 1.6039 | 18.0 | 14184 | 1.7863 | 3.4057 | 18.0629 | | 1.5735 | 19.0 | 14972 | 1.7945 | 3.6905 | 17.9571 | | 1.5319 | 20.0 | 15760 | 1.7830 | 3.5128 | 17.9714 | | 1.4993 | 21.0 | 16548 | 1.7745 | 3.4125 | 18.0057 | | 1.4622 | 22.0 | 17336 | 1.7655 | 3.3974 | 17.9543 | | 1.448 | 23.0 | 18124 | 1.7599 | 3.75 | 17.9057 | | 1.3995 | 24.0 | 18912 | 1.7557 | 3.6852 | 17.8286 | | 1.373 | 25.0 | 19700 | 1.7478 | 3.5797 | 17.9343 | | 1.3513 | 26.0 | 20488 | 1.7558 | 3.8526 | 17.8457 | | 1.3291 | 27.0 | 21276 | 1.7485 | 3.7037 | 17.9143 | | 1.3002 | 28.0 | 22064 | 1.7480 | 3.7433 | 17.96 | | 1.2655 | 29.0 | 22852 | 1.7578 | 4.0584 | 17.8914 | | 1.2354 | 30.0 | 23640 | 1.7514 | 4.2106 | 17.8686 | | 1.2224 | 31.0 | 24428 | 1.7576 | 3.9906 | 17.9 | | 1.1999 | 32.0 | 25216 | 1.7627 | 4.1242 | 17.92 | | 1.1672 | 33.0 | 26004 | 1.7548 | 4.1584 | 17.8286 | | 1.1547 | 34.0 | 26792 | 1.7446 | 4.1721 | 17.8143 | | 1.1313 | 35.0 | 27580 | 1.7613 | 4.3958 | 17.8457 | | 1.08 | 36.0 | 28368 | 1.7628 | 4.342 | 17.8829 | | 1.0927 | 37.0 | 29156 | 1.7685 | 4.4468 | 17.8971 | | 1.0751 | 38.0 | 29944 | 1.7731 | 4.4297 | 17.8886 | | 1.0492 | 39.0 | 30732 | 1.7641 | 4.5174 | 17.8714 | | 1.036 | 40.0 | 31520 | 1.7643 | 4.4578 | 17.84 | | 1.0172 | 41.0 | 32308 | 1.7820 | 4.5795 | 17.8429 | | 0.9966 | 42.0 | 33096 | 1.7830 | 4.3455 | 17.8743 | | 0.9812 | 43.0 | 33884 | 1.7890 | 4.3988 | 17.8486 | | 0.9624 | 44.0 | 34672 | 1.7953 | 4.5418 | 17.8143 | | 0.9485 | 45.0 | 35460 | 1.8046 | 4.5402 | 17.8143 | | 0.9383 | 46.0 | 36248 | 1.8010 | 4.5572 | 17.76 | | 0.9175 | 47.0 | 37036 | 1.8153 | 4.5916 | 17.7943 | | 0.8877 | 48.0 | 37824 | 1.8133 | 4.5799 | 17.7857 | | 0.8877 | 49.0 | 38612 | 1.8254 | 4.6511 | 17.7657 | | 0.8595 | 50.0 | 39400 | 1.8229 | 4.7338 | 17.7657 | | 0.8533 | 51.0 | 40188 | 1.8402 | 4.7568 | 17.7571 | | 0.8414 | 52.0 | 40976 | 1.8406 | 4.7573 | 17.8429 | | 0.8191 | 53.0 | 41764 | 1.8499 | 4.6985 | 17.76 | | 0.8228 | 54.0 | 42552 | 1.8629 | 4.7603 | 17.7114 | | 0.7987 | 55.0 | 43340 | 1.8638 | 4.5511 | 17.8 | | 0.7877 | 56.0 | 44128 | 1.8673 | 4.5068 | 17.7771 | | 0.7829 | 57.0 | 44916 | 1.8862 | 4.6033 | 17.7943 | | 0.7571 | 58.0 | 45704 | 1.8874 | 4.6694 | 17.7486 | | 0.7542 | 59.0 | 46492 | 1.8996 | 4.7531 | 17.7571 | | 0.7301 | 60.0 | 47280 | 1.8950 | 4.6951 | 17.7514 | | 0.73 | 61.0 | 48068 | 1.9035 | 4.7867 | 17.7343 | | 0.7065 | 62.0 | 48856 | 1.9127 | 4.5863 | 17.7257 | | 0.7015 | 63.0 | 49644 | 1.9418 | 4.9026 | 17.8086 | | 0.6921 | 64.0 | 50432 | 1.9322 | 4.8127 | 17.7943 | | 0.6714 | 65.0 | 51220 | 1.9382 | 4.5343 | 17.7286 | | 0.6599 | 66.0 | 52008 | 1.9508 | 4.5273 | 17.7343 | | 0.6529 | 67.0 | 52796 | 1.9577 | 4.6274 | 17.7743 | | 0.647 | 68.0 | 53584 | 1.9789 | 4.5575 | 17.7571 | | 0.627 | 69.0 | 54372 | 1.9795 | 4.319 | 17.7371 | | 0.6279 | 70.0 | 55160 | 1.9788 | 4.6788 | 17.7486 | | 0.5867 | 71.0 | 55948 | 2.0100 | 4.557 | 17.7714 | | 0.5985 | 72.0 | 56736 | 2.0256 | 4.6005 | 17.8229 | | 0.5939 | 73.0 | 57524 | 2.0336 | 4.7289 | 17.8 | | 0.5727 | 74.0 | 58312 | 2.0328 | 4.5894 | 17.7229 | | 0.5702 | 75.0 | 59100 | 2.0436 | 4.7621 | 17.78 | | 0.5744 | 76.0 | 59888 | 2.0662 | 4.6161 | 17.8057 | | 0.5554 | 77.0 | 60676 | 2.0586 | 4.6424 | 17.8057 | | 0.5436 | 78.0 | 61464 | 2.0532 | 4.5742 | 17.7886 | | 0.5359 | 79.0 | 62252 | 2.0680 | 4.8312 | 17.7886 | | 0.5291 | 80.0 | 63040 | 2.0858 | 4.6342 | 17.8457 | | 0.5034 | 81.0 | 63828 | 2.0861 | 4.7405 | 17.8257 | | 0.5155 | 82.0 | 64616 | 2.1003 | 4.3956 | 17.7571 | | 0.4989 | 83.0 | 65404 | 2.1072 | 4.339 | 17.7914 | | 0.4903 | 84.0 | 66192 | 2.1113 | 4.3804 | 17.8143 | | 0.4836 | 85.0 | 66980 | 2.1202 | 4.5776 | 17.8371 | | 0.4794 | 86.0 | 67768 | 2.1277 | 4.6548 | 17.7686 | | 0.4689 | 87.0 | 68556 | 2.1360 | 4.6453 | 17.7571 | | 0.4623 | 88.0 | 69344 | 2.1460 | 4.7885 | 17.7771 | | 0.4551 | 89.0 | 70132 | 2.1610 | 4.5342 | 17.7686 | | 0.4405 | 90.0 | 70920 | 2.1649 | 4.5593 | 17.8057 | | 0.4478 | 91.0 | 71708 | 2.1518 | 4.4945 | 17.8314 | | 0.4265 | 92.0 | 72496 | 2.1873 | 4.453 | 17.8086 | | 0.4191 | 93.0 | 73284 | 2.1808 | 4.6432 | 17.8057 | | 0.4169 | 94.0 | 74072 | 2.1871 | 4.5543 | 17.82 | | 0.4087 | 95.0 | 74860 | 2.2109 | 4.8367 | 17.7971 | | 0.4054 | 96.0 | 75648 | 2.2092 | 4.7079 | 17.8171 | | 0.3872 | 97.0 | 76436 | 2.2103 | 4.6996 | 17.7943 | | 0.3884 | 98.0 | 77224 | 2.2111 | 4.9398 | 17.8314 | | 0.3837 | 99.0 | 78012 | 2.2316 | 4.7849 | 17.8143 | | 0.3777 | 100.0 | 78800 | 2.2298 | 4.7595 | 17.8343 | | 0.3719 | 101.0 | 79588 | 2.2404 | 4.6768 | 17.8457 | | 0.364 | 102.0 | 80376 | 2.2658 | 4.5789 | 17.8229 | | 0.3549 | 103.0 | 81164 | 2.2790 | 4.6549 | 17.8029 | | 0.3598 | 104.0 | 81952 | 2.2953 | 4.7411 | 17.8486 | | 0.346 | 105.0 | 82740 | 2.2812 | 4.7529 | 17.7657 | | 0.3376 | 106.0 | 83528 | 2.2997 | 4.5128 | 17.7886 | | 0.3363 | 107.0 | 84316 | 2.2938 | 4.6983 | 17.7914 | | 0.3368 | 108.0 | 85104 | 2.2909 | 4.4977 | 17.8257 | | 0.3243 | 109.0 | 85892 | 2.3100 | 4.5156 | 17.8286 | | 0.3197 | 110.0 | 86680 | 2.3310 | 4.7516 | 17.7943 | | 0.3165 | 111.0 | 87468 | 2.3354 | 4.608 | 17.8114 | | 0.3128 | 112.0 | 88256 | 2.3334 | 4.7388 | 17.8314 | | 0.3038 | 113.0 | 89044 | 2.3343 | 4.6356 | 17.7914 | | 0.3055 | 114.0 | 89832 | 2.3553 | 4.6694 | 17.7971 | | 0.2977 | 115.0 | 90620 | 2.3530 | 4.6176 | 17.8086 | | 0.2925 | 116.0 | 91408 | 2.3687 | 4.6855 | 17.8886 | | 0.2794 | 117.0 | 92196 | 2.3856 | 4.5948 | 17.84 | | 0.2913 | 118.0 | 92984 | 2.3844 | 4.7569 | 17.7943 | | 0.2812 | 119.0 | 93772 | 2.3973 | 4.6009 | 17.7629 | | 0.2731 | 120.0 | 94560 | 2.4074 | 4.7287 | 17.8086 | | 0.2781 | 121.0 | 95348 | 2.4083 | 4.7944 | 17.8571 | | 0.2708 | 122.0 | 96136 | 2.4414 | 4.7454 | 17.8829 | | 0.2607 | 123.0 | 96924 | 2.4202 | 4.5074 | 17.8486 | | 0.2617 | 124.0 | 97712 | 2.4371 | 4.6055 | 17.8629 | | 0.2527 | 125.0 | 98500 | 2.4314 | 4.5891 | 17.8 | | 0.2528 | 126.0 | 99288 | 2.4548 | 4.8362 | 17.8571 | | 0.2522 | 127.0 | 100076 | 2.4461 | 4.6966 | 17.8514 | | 0.2434 | 128.0 | 100864 | 2.4492 | 4.5774 | 17.8514 | | 0.2381 | 129.0 | 101652 | 2.4720 | 4.4607 | 17.86 | | 0.2411 | 130.0 | 102440 | 2.4820 | 4.484 | 17.8371 | | 0.2352 | 131.0 | 103228 | 2.4954 | 4.8091 | 17.8457 | | 0.2275 | 132.0 | 104016 | 2.4863 | 4.7008 | 17.8743 | | 0.2244 | 133.0 | 104804 | 2.5089 | 4.8076 | 17.8571 | | 0.2251 | 134.0 | 105592 | 2.5085 | 4.7374 | 17.8029 | | 0.2242 | 135.0 | 106380 | 2.4979 | 4.851 | 17.8171 | | 0.2217 | 136.0 | 107168 | 2.5122 | 4.6295 | 17.8314 | | 0.2111 | 137.0 | 107956 | 2.5131 | 4.6315 | 17.8229 | | 0.2078 | 138.0 | 108744 | 2.5216 | 4.6177 | 17.8229 | | 0.2113 | 139.0 | 109532 | 2.5292 | 4.5603 | 17.8257 | | 0.21 | 140.0 | 110320 | 2.5494 | 4.6128 | 17.7971 | | 0.1994 | 141.0 | 111108 | 2.5435 | 4.9231 | 17.8714 | | 0.2018 | 142.0 | 111896 | 2.5605 | 4.827 | 17.8314 | | 0.1971 | 143.0 | 112684 | 2.5624 | 4.8075 | 17.78 | | 0.1959 | 144.0 | 113472 | 2.5666 | 4.6358 | 17.84 | | 0.1916 | 145.0 | 114260 | 2.5740 | 4.6628 | 17.8257 | | 0.1939 | 146.0 | 115048 | 2.5730 | 4.8445 | 17.8286 | | 0.1832 | 147.0 | 115836 | 2.5918 | 4.8198 | 17.8571 | | 0.1884 | 148.0 | 116624 | 2.6013 | 4.7955 | 17.8257 | | 0.1777 | 149.0 | 117412 | 2.5996 | 4.7503 | 17.8114 | | 0.1711 | 150.0 | 118200 | 2.5971 | 4.5452 | 17.8514 | | 0.1843 | 151.0 | 118988 | 2.6075 | 4.817 | 17.8143 | | 0.1747 | 152.0 | 119776 | 2.6161 | 4.5231 | 17.8257 | | 0.1698 | 153.0 | 120564 | 2.6225 | 4.7232 | 17.82 | | 0.1685 | 154.0 | 121352 | 2.6285 | 4.7105 | 17.8229 | | 0.1685 | 155.0 | 122140 | 2.6443 | 4.4228 | 17.8686 | | 0.1695 | 156.0 | 122928 | 2.6356 | 4.5458 | 17.8657 | | 0.1649 | 157.0 | 123716 | 2.6418 | 4.5955 | 17.8286 | | 0.1643 | 158.0 | 124504 | 2.6565 | 4.5943 | 17.8457 | | 0.1573 | 159.0 | 125292 | 2.6434 | 4.762 | 17.8429 | | 0.1573 | 160.0 | 126080 | 2.6615 | 4.5916 | 17.8229 | | 0.1558 | 161.0 | 126868 | 2.6529 | 4.527 | 17.8371 | | 0.1545 | 162.0 | 127656 | 2.6697 | 4.705 | 17.7886 | | 0.1563 | 163.0 | 128444 | 2.6747 | 4.6848 | 17.8086 | | 0.1529 | 164.0 | 129232 | 2.6711 | 4.5149 | 17.8171 | | 0.151 | 165.0 | 130020 | 2.6807 | 4.6484 | 17.8543 | | 0.1471 | 166.0 | 130808 | 2.6909 | 4.7488 | 17.8657 | | 0.1465 | 167.0 | 131596 | 2.6889 | 4.6446 | 17.8086 | | 0.1345 | 168.0 | 132384 | 2.6935 | 4.6107 | 17.7971 | | 0.1447 | 169.0 | 133172 | 2.6971 | 4.4718 | 17.86 | | 0.1426 | 170.0 | 133960 | 2.7083 | 4.6878 | 17.84 | | 0.1402 | 171.0 | 134748 | 2.7053 | 4.7539 | 17.8286 | | 0.1382 | 172.0 | 135536 | 2.7140 | 4.7697 | 17.8343 | | 0.1367 | 173.0 | 136324 | 2.7221 | 4.6764 | 17.8429 | | 0.1365 | 174.0 | 137112 | 2.7364 | 4.7535 | 17.8343 | | 0.1277 | 175.0 | 137900 | 2.7232 | 4.7312 | 17.8343 | | 0.1331 | 176.0 | 138688 | 2.7292 | 4.8578 | 17.8171 | | 0.1332 | 177.0 | 139476 | 2.7565 | 4.7861 | 17.8 | | 0.1291 | 178.0 | 140264 | 2.7577 | 4.8903 | 17.7686 | | 0.1298 | 179.0 | 141052 | 2.7474 | 4.7653 | 17.8171 | | 0.1268 | 180.0 | 141840 | 2.7466 | 4.7403 | 17.8143 | | 0.123 | 181.0 | 142628 | 2.7517 | 4.7989 | 17.8171 | | 0.1267 | 182.0 | 143416 | 2.7634 | 4.7267 | 17.84 | | 0.1246 | 183.0 | 144204 | 2.7620 | 4.8103 | 17.8343 | | 0.1221 | 184.0 | 144992 | 2.7686 | 4.968 | 17.8429 | | 0.1202 | 185.0 | 145780 | 2.7624 | 4.806 | 17.7914 | | 0.1222 | 186.0 | 146568 | 2.7735 | 4.8647 | 17.82 | | 0.1187 | 187.0 | 147356 | 2.7775 | 4.5615 | 17.8229 | | 0.1175 | 188.0 | 148144 | 2.7703 | 4.824 | 17.82 | | 0.121 | 189.0 | 148932 | 2.7824 | 4.8669 | 17.78 | | 0.114 | 190.0 | 149720 | 2.7807 | 4.8833 | 17.8257 | | 0.1146 | 191.0 | 150508 | 2.7869 | 4.9505 | 17.7857 | | 0.1133 | 192.0 | 151296 | 2.7900 | 4.9474 | 17.7257 | | 0.1137 | 193.0 | 152084 | 2.8008 | 4.8476 | 17.7371 | | 0.1098 | 194.0 | 152872 | 2.7971 | 4.736 | 17.7543 | | 0.1072 | 195.0 | 153660 | 2.7956 | 4.7635 | 17.8057 | | 0.1106 | 196.0 | 154448 | 2.8019 | 4.6805 | 17.7657 | | 0.1077 | 197.0 | 155236 | 2.8134 | 4.6501 | 17.8029 | | 0.1076 | 198.0 | 156024 | 2.8222 | 4.5361 | 17.82 | | 0.1054 | 199.0 | 156812 | 2.8173 | 4.8964 | 17.78 | | 0.1045 | 200.0 | 157600 | 2.8248 | 4.9418 | 17.7771 | | 0.1083 | 201.0 | 158388 | 2.8214 | 4.8408 | 17.7829 | | 0.1035 | 202.0 | 159176 | 2.8277 | 4.66 | 17.8 | | 0.1033 | 203.0 | 159964 | 2.8342 | 4.616 | 17.8114 | | 0.1013 | 204.0 | 160752 | 2.8392 | 4.7213 | 17.8371 | | 0.1012 | 205.0 | 161540 | 2.8313 | 4.7918 | 17.8 | | 0.1021 | 206.0 | 162328 | 2.8372 | 4.8182 | 17.8371 | | 0.0979 | 207.0 | 163116 | 2.8500 | 4.759 | 17.8657 | | 0.0985 | 208.0 | 163904 | 2.8458 | 4.6711 | 17.8171 | | 0.1006 | 209.0 | 164692 | 2.8468 | 4.7997 | 17.8286 | | 0.0994 | 210.0 | 165480 | 2.8426 | 4.7327 | 17.8571 | | 0.0981 | 211.0 | 166268 | 2.8565 | 4.7288 | 17.8457 | | 0.0985 | 212.0 | 167056 | 2.8608 | 4.8843 | 17.8457 | | 0.0933 | 213.0 | 167844 | 2.8656 | 4.7052 | 17.8143 | | 0.0963 | 214.0 | 168632 | 2.8650 | 4.8149 | 17.7771 | | 0.092 | 215.0 | 169420 | 2.8569 | 4.6251 | 17.8 | | 0.0958 | 216.0 | 170208 | 2.8688 | 4.7479 | 17.7714 | | 0.094 | 217.0 | 170996 | 2.8657 | 4.7716 | 17.8229 | | 0.0926 | 218.0 | 171784 | 2.8741 | 4.6749 | 17.8143 | | 0.0924 | 219.0 | 172572 | 2.8727 | 4.8438 | 17.82 | | 0.0932 | 220.0 | 173360 | 2.8749 | 4.6733 | 17.84 | | 0.0899 | 221.0 | 174148 | 2.8774 | 4.6198 | 17.8286 | | 0.0925 | 222.0 | 174936 | 2.8796 | 4.6945 | 17.8286 | | 0.0904 | 223.0 | 175724 | 2.8872 | 4.6184 | 17.82 | | 0.0886 | 224.0 | 176512 | 2.8974 | 4.74 | 17.7743 | | 0.0898 | 225.0 | 177300 | 2.8879 | 4.5856 | 17.8229 | | 0.0874 | 226.0 | 178088 | 2.8880 | 4.582 | 17.8171 | | 0.0877 | 227.0 | 178876 | 2.8941 | 4.64 | 17.8057 | | 0.0892 | 228.0 | 179664 | 2.8975 | 4.7271 | 17.8114 | | 0.0857 | 229.0 | 180452 | 2.8957 | 4.6847 | 17.7943 | | 0.088 | 230.0 | 181240 | 2.8950 | 4.7799 | 17.8086 | | 0.0885 | 231.0 | 182028 | 2.9061 | 4.699 | 17.7829 | | 0.0863 | 232.0 | 182816 | 2.9085 | 4.7863 | 17.7771 | | 0.0853 | 233.0 | 183604 | 2.9083 | 4.7545 | 17.7857 | | 0.0838 | 234.0 | 184392 | 2.9067 | 4.6354 | 17.7829 | | 0.0835 | 235.0 | 185180 | 2.9139 | 4.5979 | 17.8371 | | 0.0865 | 236.0 | 185968 | 2.9094 | 4.7646 | 17.8314 | | 0.0853 | 237.0 | 186756 | 2.9127 | 4.6967 | 17.7971 | | 0.082 | 238.0 | 187544 | 2.9205 | 4.7171 | 17.8029 | | 0.0811 | 239.0 | 188332 | 2.9204 | 4.6172 | 17.7971 | | 0.0837 | 240.0 | 189120 | 2.9202 | 4.6729 | 17.8057 | | 0.0803 | 241.0 | 189908 | 2.9190 | 4.9057 | 17.8143 | | 0.0813 | 242.0 | 190696 | 2.9236 | 4.7919 | 17.8429 | | 0.0814 | 243.0 | 191484 | 2.9307 | 4.7492 | 17.8286 | | 0.0822 | 244.0 | 192272 | 2.9238 | 4.7454 | 17.8429 | | 0.0823 | 245.0 | 193060 | 2.9269 | 4.8462 | 17.8257 | | 0.0803 | 246.0 | 193848 | 2.9293 | 4.738 | 17.8286 | | 0.0806 | 247.0 | 194636 | 2.9280 | 4.8432 | 17.78 | | 0.0757 | 248.0 | 195424 | 2.9371 | 4.8563 | 17.8171 | | 0.0774 | 249.0 | 196212 | 2.9330 | 4.7717 | 17.8057 | | 0.079 | 250.0 | 197000 | 2.9373 | 4.7938 | 17.8371 | | 0.0784 | 251.0 | 197788 | 2.9397 | 4.8316 | 17.82 | | 0.0801 | 252.0 | 198576 | 2.9378 | 4.9071 | 17.8314 | | 0.0795 | 253.0 | 199364 | 2.9366 | 4.8581 | 17.8343 | | 0.077 | 254.0 | 200152 | 2.9372 | 4.8495 | 17.7971 | | 0.0787 | 255.0 | 200940 | 2.9447 | 4.8479 | 17.8086 | | 0.077 | 256.0 | 201728 | 2.9380 | 4.8716 | 17.84 | | 0.0765 | 257.0 | 202516 | 2.9410 | 4.8944 | 17.7571 | | 0.0762 | 258.0 | 203304 | 2.9423 | 4.7536 | 17.7971 | | 0.0772 | 259.0 | 204092 | 2.9485 | 4.8251 | 17.8343 | | 0.0761 | 260.0 | 204880 | 2.9401 | 4.7726 | 17.82 | | 0.0766 | 261.0 | 205668 | 2.9427 | 4.8626 | 17.8286 | | 0.0766 | 262.0 | 206456 | 2.9428 | 5.0326 | 17.8143 | | 0.074 | 263.0 | 207244 | 2.9463 | 5.0095 | 17.8286 | | 0.0758 | 264.0 | 208032 | 2.9497 | 4.987 | 17.8029 | | 0.0778 | 265.0 | 208820 | 2.9534 | 4.9829 | 17.8086 | | 0.0748 | 266.0 | 209608 | 2.9521 | 4.9309 | 17.8286 | | 0.0759 | 267.0 | 210396 | 2.9519 | 4.9294 | 17.84 | | 0.0738 | 268.0 | 211184 | 2.9521 | 4.9953 | 17.8486 | | 0.077 | 269.0 | 211972 | 2.9521 | 4.8414 | 17.8486 | | 0.0759 | 270.0 | 212760 | 2.9533 | 4.8158 | 17.8286 | | 0.0725 | 271.0 | 213548 | 2.9534 | 4.8427 | 17.8457 | | 0.0749 | 272.0 | 214336 | 2.9512 | 4.8769 | 17.8314 | | 0.0745 | 273.0 | 215124 | 2.9520 | 4.8782 | 17.8257 | | 0.0723 | 274.0 | 215912 | 2.9546 | 4.8465 | 17.8229 | | 0.0748 | 275.0 | 216700 | 2.9567 | 4.8704 | 17.8343 | | 0.072 | 276.0 | 217488 | 2.9569 | 4.8633 | 17.8371 | | 0.0747 | 277.0 | 218276 | 2.9578 | 4.8667 | 17.8457 | | 0.0722 | 278.0 | 219064 | 2.9566 | 4.8686 | 17.8371 | | 0.0733 | 279.0 | 219852 | 2.9563 | 4.846 | 17.84 | | 0.0713 | 280.0 | 220640 | 2.9566 | 4.8513 | 17.84 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.12.1+cu113 - Datasets 2.3.2 - Tokenizers 0.10.3

ShishckovA/results

ShishckovA

2022-11-29T07:33:29Z

105

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-29T07:31:07Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: results results: [] ---  # results This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2721 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 0.505 | 1.0 | 26878 | 0.4409 | | 0.4063 | 2.0 | 53756 | 0.3390 | | 0.358 | 3.0 | 80634 | 0.2967 | | 0.3383 | 4.0 | 107512 | 0.2777 | | 0.3289 | 5.0 | 134390 | 0.2721 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

mlxen/electra-contrastdata-squad

mlxen

2022-11-29T07:16:20Z

104

0

transformers

[ "transformers", "pytorch", "tensorboard", "electra", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-11-28T07:16:40Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: electra-contrastdata-squad results: [] ---  # electra-contrastdata-squad This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

nagais/sd-class-butterflies-32

nagais

2022-11-29T07:06:12Z

32

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T06:51:12Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(nagais/sd-class-butterflies-32) image = pipeline().images[0] image ```

jl8771/sd-class-butterflies-32

jl8771

2022-11-29T05:41:50Z

37

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T05:41:45Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(jl8771/sd-class-butterflies-32) image = pipeline().images[0] image ```

manter/momoko

manter

2022-11-29T05:21:52Z

0

8

null

[ "doi:10.57967/hf/0147", "license:unknown", "region:us" ]

null

2022-11-29T03:32:48Z

--- license: unknown --- This was a stable diffusion based model that was based off of anythingv3 and momoko which I still don't know the orgin of. (personal story: How I fond this was from going to a outdated stable diffusion web ui link and hitting generate. It came out good so I googled it and found this.) Sorce: https://www.kaggle.com/code/inmine/novelai-with-webui-stable-diffusion-version/data, https://www.kaggle.com/datasets/inmine/momoko btw here is a prompt (prompt:Masterpiece, best quality,)(negitive prompt:lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewerdigits, cropped, worst quality, low quality, normal quality, ipeg artifacts, signature, watermark,username, blurry) That's what I found work's the best, The main thing it generates is woman so be warned.

Shubham09/whisper63filescheck

Shubham09

2022-11-29T05:12:22Z

77

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-29T05:07:16Z

--- tags: - generated_from_trainer metrics: - wer model-index: - name: whisper63filescheck results: [] ---  # whisper63filescheck This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0638 - Wer: 23.7647 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1324 | 14.29 | 100 | 1.0638 | 23.7647 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2

Urigavilan03/Tiempo

Urigavilan03

2022-11-29T05:12:14Z

0

null

[ "region:us" ]

null

2022-11-29T05:09:08Z

un reloj de bolsillo antiguo en medio de unas hojas escritas en cursiva desenfocada

laroy23/ddpm-butterflies-128

laroy23

2022-11-29T04:33:59Z

0

diffusers

[ "diffusers", "tensorboard", "en", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

null

2022-11-28T13:56:37Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: ./cifar-10-batches-py metrics: [] ---  # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `./cifar-10-batches-py` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/laroy23/ddpm-butterflies-128/tensorboard?#scalars)

elRivx/gAWoman

elRivx

2022-11-29T04:33:34Z

0

2

null

[ "stable-diffusion", "text-to-image", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2022-11-29T04:22:28Z

--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image --- # gAWoman This is my second Stable Diffusion custom model that bring to you a generic woman generated with non-licenced images. The magic word is: gAWoman If you enjoy my work, please consider supporting me: [![Buy me a coffee](https://badgen.net/badge/icon/buymeacoffee?icon=buymeacoffee&label)](https://www.buymeacoffee.com/elrivx) Examples: <img src=https://imgur.com/B5XkfuG.png width=30% height=30%> <img src=https://imgur.com/N8lNtZo.png width=30% height=30%> ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

NSandra/distilbert-base-uncased-finetuned-ner

NSandra

2022-11-29T04:09:17Z

125

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-29T03:55:13Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: [] ---  # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2393 - Precision: 1.0 - Recall: 1.0 - F1: 1.0 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 1 | 1.5491 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 2.0 | 2 | 1.3278 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 3.0 | 3 | 1.2393 | 1.0 | 1.0 | 1.0 | 1.0 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

nhanv/cv_parser

nhanv

2022-11-29T04:00:56Z

167

3

transformers

[ "transformers", "pytorch", "deberta-v2", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-29T03:23:32Z

--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: cv-ner results: [] ---  # cv-ner This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0956 - Precision: 0.8906 - Recall: 0.9325 - F1: 0.9111 - Accuracy: 0.9851 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 91 | 0.2049 | 0.6618 | 0.7362 | 0.6970 | 0.9534 | | 0.5036 | 2.0 | 182 | 0.1156 | 0.7873 | 0.8630 | 0.8234 | 0.9722 | | 0.1442 | 3.0 | 273 | 0.1078 | 0.8262 | 0.9039 | 0.8633 | 0.9771 | | 0.0757 | 4.0 | 364 | 0.1179 | 0.8652 | 0.9059 | 0.8851 | 0.9780 | | 0.0526 | 5.0 | 455 | 0.0907 | 0.888 | 0.9080 | 0.8979 | 0.9837 | | 0.0342 | 6.0 | 546 | 0.0972 | 0.8926 | 0.9346 | 0.9131 | 0.9832 | | 0.0245 | 7.0 | 637 | 0.1064 | 0.8937 | 0.9284 | 0.9107 | 0.9834 | | 0.0188 | 8.0 | 728 | 0.0965 | 0.8980 | 0.9366 | 0.9169 | 0.9850 | | 0.0159 | 9.0 | 819 | 0.0999 | 0.91 | 0.9305 | 0.9201 | 0.9846 | | 0.0141 | 10.0 | 910 | 0.0956 | 0.8906 | 0.9325 | 0.9111 | 0.9851 | ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

jeraldflowers/vit_model

jeraldflowers

2022-11-29T03:51:31Z

188

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:beans", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-11-27T05:06:17Z

--- license: apache-2.0 tags: - image-classification - generated_from_trainer datasets: - beans metrics: - accuracy widget: - src: https://huggingface.co/jeraldflowers/vit_model/blob/main/healthy.jpeg example_title: Healthy - src: https://huggingface.co/jeraldflowers/vit_model/blob/main/bean_rust.jpeg example_title: Bean Rust model-index: - name: vit_model results: - task: name: Image Classification type: image-classification dataset: name: beans type: beans config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 1.0 ---  # vit_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0095 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1526 | 3.85 | 500 | 0.0095 | 1.0 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

UCSYNLP/MyanBERTa

UCSYNLP

2022-11-29T03:35:58Z

297

3

transformers

[ "transformers", "pytorch", "roberta", "fill-mask", "MyanBERTa", "Myanmar", "BERT", "RoBERTa", "my", "dataset:MyCorpus", "dataset:Web", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-07-25T06:57:10Z

--- language: my tags: - MyanBERTa - Myanmar - BERT - RoBERTa license: apache-2.0 datasets: - MyCorpus - Web --- ## Model description This model is a BERT based Myanmar pre-trained language model. MyanBERTa was pre-trained for 528K steps on a word segmented Myanmar dataset consisting of 5,992,299 sentences (136M words). As the tokenizer, byte-leve BPE tokenizer of 30,522 subword units which is learned after word segmentation is applied. Cite this work as: ``` Aye Mya Hlaing, Win Pa Pa, "MyanBERTa: A Pre-trained Language Model For Myanmar", In Proceedings of 2022 International Conference on Communication and Computer Research (ICCR2022), November 2022, Seoul, Republic of Korea ``` [Download Paper](https://journal-home.s3.ap-northeast-2.amazonaws.com/site/iccr2022/abs/QOHFI-0004.pdf)

jeraldflowers/distilroberts-base-mrpc-glue-jeraldflowers

jeraldflowers

2022-11-29T02:57:36Z

108

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-28T05:30:00Z

--- license: apache-2.0 tags: - text-classification - generated_from_trainer datasets: - glue metrics: - accuracy - f1 widget: - text: ["Yucaipa owned Dominick's before selling the chain to Safeway in 1998 for $ 2.5 billion.", "Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998."] example_title: Not Equivalent - text: ["Revenue in the first quarter of the year dropped 15 percent from the same period a year earlier.", "With the scandal hanging over Stewart's company revenue the first quarter of the year dropped 15 percent from the same period a year earlier."] example_title: Equivalent model-index: - name: distilroberts-base-mrpc-glue-jeraldflowers results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: train args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8431372549019608 - name: F1 type: f1 value: 0.8814814814814815 ---  # distilroberts-base-mrpc-glue-jeraldflowers This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets. It achieves the following results on the evaluation set: - Loss: 0.4990 - Accuracy: 0.8431 - F1: 0.8815 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5289 | 1.09 | 500 | 0.5668 | 0.8211 | 0.8689 | | 0.3675 | 2.18 | 1000 | 0.4990 | 0.8431 | 0.8815 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

neulab/omnitab-large-128shot-finetuned-wtq-128shot

neulab

2022-11-29T02:55:31Z

47

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:wikitablequestions", "arxiv:2207.03637", "autotrain_compatible", "endpoints_compatible", "region:us" ]

table-question-answering

2022-11-29T02:54:00Z

--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large-128shot-finetuned-wtq-128shot` (based on BART architecture) is initialized with `neulab/omnitab-large-128shot` and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) in the 128-shot setting. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-128shot-finetuned-wtq-128shot") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-128shot-finetuned-wtq-128shot") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```

neulab/omnitab-large-1024shot-finetuned-wtq-1024shot

neulab

2022-11-29T02:45:55Z

51

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:wikitablequestions", "arxiv:2207.03637", "autotrain_compatible", "endpoints_compatible", "region:us" ]

table-question-answering

2022-11-29T02:44:57Z

--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large-1024shot-finetuned-wtq-1024shot` (based on BART architecture) is initialized with `neulab/omnitab-large-1024shot` and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) in the 1024-shot setting. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-1024shot-finetuned-wtq-1024shot") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-1024shot-finetuned-wtq-1024shot") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```

neulab/omnitab-large-1024shot

neulab

2022-11-29T02:38:18Z

48

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:wikitablequestions", "arxiv:2207.03637", "autotrain_compatible", "endpoints_compatible", "region:us" ]

table-question-answering

2022-11-29T02:37:18Z

--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large-1024shot` (based on BART architecture) is initialized with `microsoft/tapex-large` and continuously pretrained on natural and synthetic data (SQL2NL model trained in the 1024-shot setting). ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-1024shot") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-1024shot") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```

SiddharthaM/hasoc19-xlm-roberta-base-sentiment-new

SiddharthaM

2022-11-29T02:13:32Z

114

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-29T00:44:19Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: hasoc19-xlm-roberta-base-sentiment-new results: [] ---  # hasoc19-xlm-roberta-base-sentiment-new This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3840 - Accuracy: 0.8726 - Precision: 0.8724 - Recall: 0.8726 - F1: 0.8725 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.4786 | 1.0 | 537 | 0.3999 | 0.8381 | 0.8391 | 0.8381 | 0.8363 | | 0.349 | 2.0 | 1074 | 0.3443 | 0.8606 | 0.8603 | 0.8606 | 0.8603 | | 0.2927 | 3.0 | 1611 | 0.3412 | 0.8669 | 0.8668 | 0.8669 | 0.8662 | | 0.2471 | 4.0 | 2148 | 0.3408 | 0.8705 | 0.8708 | 0.8705 | 0.8706 | | 0.2195 | 5.0 | 2685 | 0.3897 | 0.8726 | 0.8725 | 0.8726 | 0.8721 | | 0.1849 | 6.0 | 3222 | 0.3840 | 0.8726 | 0.8724 | 0.8726 | 0.8725 | ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1

neulab/omnitab-large-16shot-finetuned-wtq-16shot

neulab

2022-11-29T02:10:07Z

52

1

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:wikitablequestions", "arxiv:2207.03637", "autotrain_compatible", "endpoints_compatible", "region:us" ]

table-question-answering

2022-11-29T01:48:24Z

--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large-16shot-finetuned-wtq-16shot` (based on BART architecture) is initialized with `neulab/omnitab-large-16shot` and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) in the 16-shot setting. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-16shot-finetuned-wtq-16shot") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-16shot-finetuned-wtq-16shot") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```

romendiratta/fin-unsupersvised-mt5-4000

romendiratta

2022-11-29T02:07:11Z

4

0

transformers

[ "transformers", "jax", "tensorboard", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-29T01:55:24Z

This model contains MT5 which has been trained via masked language modeling on a financial dataset in an unsupervised manner. --- license: mit ---

alexziweiwang/retrain5_oneTimeTraining_MTL-1epoch

alexziweiwang

2022-11-29T02:00:29Z

31

0

transformers

[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2022-11-29T01:43:16Z

--- tags: - generated_from_trainer model-index: - name: retrain5_oneTimeTraining_MTL-1epoch results: [] ---  # retrain5_oneTimeTraining_MTL-1epoch This model is a fine-tuned version of [alexziweiwang/exp21-uaspeech-foundation](https://huggingface.co/alexziweiwang/exp21-uaspeech-foundation) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 7.1861 - Acc: 0.285 - Wer: 1.1126 - Correct: 57 - Total: 200 - Strlen: 200 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Acc | Wer | Correct | Total | Strlen | |:-------------:|:-----:|:----:|:---------------:|:-----:|:------:|:-------:|:-----:|:------:| | No log | 0.02 | 5 | 13.9337 | 0.01 | 1.2925 | 2 | 200 | 200 | | 12.4373 | 0.04 | 10 | 13.7513 | 0.08 | 1.5296 | 16 | 200 | 200 | | 12.4373 | 0.06 | 15 | 13.5517 | 0.125 | 2.1126 | 25 | 200 | 200 | | 12.6667 | 0.08 | 20 | 13.3400 | 0.165 | 2.5791 | 33 | 200 | 200 | | 12.6667 | 0.11 | 25 | 13.1141 | 0.205 | 3.6561 | 41 | 200 | 200 | | 11.1856 | 0.13 | 30 | 12.8805 | 0.22 | 2.7451 | 44 | 200 | 200 | | 11.1856 | 0.15 | 35 | 12.6423 | 0.245 | 2.5178 | 49 | 200 | 200 | | 10.6635 | 0.17 | 40 | 12.4028 | 0.27 | 2.4308 | 54 | 200 | 200 | | 10.6635 | 0.19 | 45 | 12.1660 | 0.3 | 2.1818 | 60 | 200 | 200 | | 10.7952 | 0.21 | 50 | 11.9291 | 0.305 | 1.9348 | 61 | 200 | 200 | | 10.7952 | 0.23 | 55 | 11.6945 | 0.31 | 1.6858 | 62 | 200 | 200 | | 10.3867 | 0.25 | 60 | 11.4608 | 0.315 | 1.5237 | 63 | 200 | 200 | | 10.3867 | 0.27 | 65 | 11.2313 | 0.315 | 1.3953 | 63 | 200 | 200 | | 10.252 | 0.3 | 70 | 11.0102 | 0.315 | 1.3162 | 63 | 200 | 200 | | 10.252 | 0.32 | 75 | 10.7918 | 0.315 | 1.2826 | 63 | 200 | 200 | | 10.1788 | 0.34 | 80 | 10.5736 | 0.315 | 1.2628 | 63 | 200 | 200 | | 10.1788 | 0.36 | 85 | 10.3607 | 0.32 | 1.2391 | 64 | 200 | 200 | | 9.1361 | 0.38 | 90 | 10.1527 | 0.31 | 1.2253 | 62 | 200 | 200 | | 9.1361 | 0.4 | 95 | 9.9507 | 0.31 | 1.2036 | 62 | 200 | 200 | | 9.5447 | 0.42 | 100 | 9.7553 | 0.315 | 1.2095 | 63 | 200 | 200 | | 9.5447 | 0.44 | 105 | 9.5599 | 0.31 | 1.2016 | 62 | 200 | 200 | | 9.1579 | 0.46 | 110 | 9.3711 | 0.295 | 1.1996 | 59 | 200 | 200 | | 9.1579 | 0.48 | 115 | 9.1892 | 0.295 | 1.1897 | 59 | 200 | 200 | | 7.9217 | 0.51 | 120 | 9.0143 | 0.3 | 1.1858 | 60 | 200 | 200 | | 7.9217 | 0.53 | 125 | 8.8493 | 0.305 | 1.1719 | 61 | 200 | 200 | | 8.4439 | 0.55 | 130 | 8.6946 | 0.305 | 1.1739 | 61 | 200 | 200 | | 8.4439 | 0.57 | 135 | 8.5492 | 0.31 | 1.1581 | 62 | 200 | 200 | | 8.0639 | 0.59 | 140 | 8.4153 | 0.315 | 1.1502 | 63 | 200 | 200 | | 8.0639 | 0.61 | 145 | 8.2872 | 0.32 | 1.1482 | 64 | 200 | 200 | | 8.4173 | 0.63 | 150 | 8.1649 | 0.33 | 1.1443 | 66 | 200 | 200 | | 8.4173 | 0.65 | 155 | 8.0500 | 0.325 | 1.1403 | 65 | 200 | 200 | | 7.8991 | 0.67 | 160 | 7.9422 | 0.33 | 1.1364 | 66 | 200 | 200 | | 7.8991 | 0.7 | 165 | 7.8410 | 0.32 | 1.1344 | 64 | 200 | 200 | | 6.9206 | 0.72 | 170 | 7.7469 | 0.32 | 1.1304 | 64 | 200 | 200 | | 6.9206 | 0.74 | 175 | 7.6601 | 0.325 | 1.1285 | 65 | 200 | 200 | | 7.1911 | 0.76 | 180 | 7.5832 | 0.305 | 1.1206 | 61 | 200 | 200 | | 7.1911 | 0.78 | 185 | 7.5163 | 0.305 | 1.1225 | 61 | 200 | 200 | | 7.201 | 0.8 | 190 | 7.4565 | 0.305 | 1.1245 | 61 | 200 | 200 | | 7.201 | 0.82 | 195 | 7.4049 | 0.295 | 1.1245 | 59 | 200 | 200 | | 7.1507 | 0.84 | 200 | 7.3568 | 0.295 | 1.1225 | 59 | 200 | 200 | | 7.1507 | 0.86 | 205 | 7.3139 | 0.3 | 1.1206 | 60 | 200 | 200 | | 6.6223 | 0.89 | 210 | 7.2774 | 0.295 | 1.1186 | 59 | 200 | 200 | | 6.6223 | 0.91 | 215 | 7.2469 | 0.295 | 1.1186 | 59 | 200 | 200 | | 7.1645 | 0.93 | 220 | 7.2220 | 0.295 | 1.1166 | 59 | 200 | 200 | | 7.1645 | 0.95 | 225 | 7.2041 | 0.29 | 1.1146 | 58 | 200 | 200 | | 6.2562 | 0.97 | 230 | 7.1921 | 0.29 | 1.1146 | 58 | 200 | 200 | | 6.2562 | 0.99 | 235 | 7.1861 | 0.285 | 1.1126 | 57 | 200 | 200 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2

BunnyViking/bvSketchOutline

BunnyViking

2022-11-29T01:26:30Z

0

12

null

[ "license:mit", "region:us" ]

null

2022-11-28T02:53:16Z

--- license: mit --- Sketch Outline style - a scratchy concept-art like style to give the appearance of quickly rendered pencil and ink art. The model is trained on humans, some animals, some structures and a few vehicles but it is best at humans and monsters. NOTE - the model has been trained with some artistic nudes included and can generate unintended NSFW content on occasion. Custom style trained off SD 1.5 DDLM Token: bvSketchOutline Not using the token (or using prompts like 'stroke' or 'outline') or placing the token at the start or end of the prompt will have different interesting effect. Higher versions will improve the overall style at the cost of flexibility. The model will skew more toward humans at the higher versions. The higher versions will also create more monstrous animals. I recommend a confidence between 7.5 and 12.5 v2 2000 - some outline and flexible CFG 7.5 is fine 7.5 ![v2000-7-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620449747-631c8158aa346997917dcf5d.jpeg) 12.5 ![v2000-12-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620450973-631c8158aa346997917dcf5d.jpeg) v2 3000 - sketchy and flexible CFG 7.5 is fine 7.5 ![v3000-7-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620450689-631c8158aa346997917dcf5d.jpeg) 12.5 ![v3000-12-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620451447-631c8158aa346997917dcf5d.jpeg) v2 4000 - sketchy outline and extra outline strokes. recommend increasing CFG to 12.5 so less flexible 7.5 ![v4000-7-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620550007-631c8158aa346997917dcf5d.jpeg) 12.5 ![v4000-12-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620550245-631c8158aa346997917dcf5d.jpeg) v2 5000 - smoother outlines much less flexible, will start skewing strongly toward humans even at 7.5 CFG. At 12.5 CFG it will be sketchier with more outline strokes, almost like v2 2000 in look but at higher quality. 7.5 ![v5000-7-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620549994-631c8158aa346997917dcf5d.jpeg) 12.5 ![v5000-12-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620550011-631c8158aa346997917dcf5d.jpeg) v2 6000 - very sketchy and scratchy at 7.5 CFG, more inky, may lose detail. At 12.5 is quite inky in its outlines. 7.5 ![v6000-7-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620597579-631c8158aa346997917dcf5d.jpeg) 12.5 ![v6000-12-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620598073-631c8158aa346997917dcf5d.jpeg) v2 7000 - sketchy and many flowing outlines at 7.5 CFG. Can have compromised details. At 12.5 CFG the style becomes very inky and loses detail almost wet watercolour 7.5 ![v7000-7-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620597830-631c8158aa346997917dcf5d.jpeg) 12.5 ![v7000-12-5.jpg](https://s3.amazonaws.com/moonup/production/uploads/1669620598527-631c8158aa346997917dcf5d.jpeg)

alexziweiwang/retrain2_oneTimeTraining_MTL-1epoch

alexziweiwang

2022-11-29T01:04:58Z

31

0

transformers

[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2022-11-29T00:47:27Z

--- tags: - generated_from_trainer model-index: - name: retrain2_oneTimeTraining_MTL-1epoch results: [] ---  # retrain2_oneTimeTraining_MTL-1epoch This model is a fine-tuned version of [alexziweiwang/exp21-uaspeech-foundation](https://huggingface.co/alexziweiwang/exp21-uaspeech-foundation) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.9312 - Acc: 0.265 - Wer: 1.0 - Correct: 53 - Total: 200 - Strlen: 200 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Acc | Wer | Correct | Total | Strlen | |:-------------:|:-----:|:----:|:---------------:|:-----:|:------:|:-------:|:-----:|:------:| | No log | 0.02 | 5 | 13.6638 | 0.005 | 1.6126 | 1 | 200 | 200 | | 12.2282 | 0.04 | 10 | 13.4030 | 0.005 | 1.4743 | 1 | 200 | 200 | | 12.2282 | 0.06 | 15 | 13.1289 | 0.005 | 1.3953 | 1 | 200 | 200 | | 12.3565 | 0.08 | 20 | 12.8538 | 0.005 | 1.3043 | 1 | 200 | 200 | | 12.3565 | 0.11 | 25 | 12.5711 | 0.005 | 1.2095 | 1 | 200 | 200 | | 10.7997 | 0.13 | 30 | 12.2891 | 0.005 | 1.1462 | 1 | 200 | 200 | | 10.7997 | 0.15 | 35 | 12.0060 | 0.005 | 1.0909 | 1 | 200 | 200 | | 10.1556 | 0.17 | 40 | 11.7183 | 0.005 | 1.0632 | 1 | 200 | 200 | | 10.1556 | 0.19 | 45 | 11.4347 | 0.01 | 1.0395 | 2 | 200 | 200 | | 10.3187 | 0.21 | 50 | 11.1549 | 0.01 | 1.0178 | 2 | 200 | 200 | | 10.3187 | 0.23 | 55 | 10.8828 | 0.01 | 1.0099 | 2 | 200 | 200 | | 9.8042 | 0.25 | 60 | 10.6161 | 0.01 | 1.0040 | 2 | 200 | 200 | | 9.8042 | 0.27 | 65 | 10.3539 | 0.01 | 0.9980 | 2 | 200 | 200 | | 9.6489 | 0.3 | 70 | 10.0954 | 0.015 | 1.0 | 3 | 200 | 200 | | 9.6489 | 0.32 | 75 | 9.8456 | 0.025 | 1.0 | 5 | 200 | 200 | | 9.6112 | 0.34 | 80 | 9.5980 | 0.045 | 1.0 | 9 | 200 | 200 | | 9.6112 | 0.36 | 85 | 9.3535 | 0.055 | 1.0 | 11 | 200 | 200 | | 8.4257 | 0.38 | 90 | 9.1168 | 0.085 | 1.0 | 17 | 200 | 200 | | 8.4257 | 0.4 | 95 | 8.8920 | 0.105 | 1.0 | 21 | 200 | 200 | | 8.7311 | 0.42 | 100 | 8.6739 | 0.11 | 1.0 | 22 | 200 | 200 | | 8.7311 | 0.44 | 105 | 8.4607 | 0.135 | 1.0 | 27 | 200 | 200 | | 8.3653 | 0.46 | 110 | 8.2551 | 0.165 | 1.0 | 33 | 200 | 200 | | 8.3653 | 0.48 | 115 | 8.0573 | 0.17 | 1.0 | 34 | 200 | 200 | | 7.1342 | 0.51 | 120 | 7.8700 | 0.175 | 1.0 | 35 | 200 | 200 | | 7.1342 | 0.53 | 125 | 7.6908 | 0.185 | 1.0 | 37 | 200 | 200 | | 7.5411 | 0.55 | 130 | 7.5221 | 0.205 | 1.0 | 41 | 200 | 200 | | 7.5411 | 0.57 | 135 | 7.3628 | 0.22 | 1.0 | 44 | 200 | 200 | | 7.2449 | 0.59 | 140 | 7.2131 | 0.23 | 1.0 | 46 | 200 | 200 | | 7.2449 | 0.61 | 145 | 7.0735 | 0.23 | 1.0 | 46 | 200 | 200 | | 7.5166 | 0.63 | 150 | 6.9396 | 0.25 | 1.0 | 50 | 200 | 200 | | 7.5166 | 0.65 | 155 | 6.8186 | 0.25 | 1.0 | 50 | 200 | 200 | | 7.0016 | 0.67 | 160 | 6.7015 | 0.25 | 1.0 | 50 | 200 | 200 | | 7.0016 | 0.7 | 165 | 6.5904 | 0.25 | 1.0 | 50 | 200 | 200 | | 6.0715 | 0.72 | 170 | 6.4879 | 0.255 | 1.0 | 51 | 200 | 200 | | 6.0715 | 0.74 | 175 | 6.3980 | 0.26 | 1.0 | 52 | 200 | 200 | | 6.312 | 0.76 | 180 | 6.3198 | 0.26 | 1.0 | 52 | 200 | 200 | | 6.312 | 0.78 | 185 | 6.2532 | 0.26 | 1.0 | 52 | 200 | 200 | | 6.3694 | 0.8 | 190 | 6.1952 | 0.26 | 1.0 | 52 | 200 | 200 | | 6.3694 | 0.82 | 195 | 6.1453 | 0.26 | 1.0 | 52 | 200 | 200 | | 6.2196 | 0.84 | 200 | 6.0993 | 0.26 | 1.0 | 52 | 200 | 200 | | 6.2196 | 0.86 | 205 | 6.0556 | 0.265 | 1.0 | 53 | 200 | 200 | | 5.7131 | 0.89 | 210 | 6.0181 | 0.265 | 1.0 | 53 | 200 | 200 | | 5.7131 | 0.91 | 215 | 5.9873 | 0.265 | 1.0 | 53 | 200 | 200 | | 6.1827 | 0.93 | 220 | 5.9619 | 0.265 | 1.0 | 53 | 200 | 200 | | 6.1827 | 0.95 | 225 | 5.9460 | 0.265 | 1.0 | 53 | 200 | 200 | | 5.3823 | 0.97 | 230 | 5.9359 | 0.265 | 1.0 | 53 | 200 | 200 | | 5.3823 | 0.99 | 235 | 5.9312 | 0.265 | 1.0 | 53 | 200 | 200 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2

dlwh/legal-xlm-base_128k

dlwh

2022-11-29T00:48:35Z

4

2

transformers

[ "transformers", "roberta", "fill-mask", "bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-11-29T00:41:54Z

--- license: apache-2.0 language: - bg - cs - da - de - el - en - es - et - fi - fr - ga - hr - hu - it - lt - lv - mt - nl - pl - pt - ro - sk - sl - sv dataset: - joelito/MultiLegalPile_Wikipedia_Filtered --- Huggingface thinks this is a model, but it's just a tokenizer. Trained on https://huggingface.co/datasets/joelito/MultiLegalPile_Wikipedia_Filtered

Serhio/sd-fine-tune-v2

Serhio

2022-11-28T23:43:18Z

34

0

diffusers

[ "diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2022-11-28T23:41:46Z

--- license: creativeml-openrail-m tags: - text-to-image --- ### sd-fine-tune-v2 on Stable Diffusion via Dreambooth #### model by Serhio This your the Stable Diffusion model fine-tuned the sd-fine-tune-v2 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **Bashkov Sergey** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)

pig4431/TweetEval_BERT_5E

pig4431

2022-11-28T23:38:03Z

102

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-28T23:31:54Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - accuracy model-index: - name: TweetEval_BERT_5E results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: train args: sentiment metrics: - name: Accuracy type: accuracy value: 0.9266666666666666 ---  # TweetEval_BERT_5E This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.5419 - Accuracy: 0.9267 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6264 | 0.04 | 50 | 0.5266 | 0.74 | | 0.5054 | 0.08 | 100 | 0.5959 | 0.6333 | | 0.4732 | 0.12 | 150 | 0.3524 | 0.86 | | 0.3916 | 0.16 | 200 | 0.3195 | 0.8667 | | 0.3477 | 0.2 | 250 | 0.2878 | 0.8867 | | 0.3116 | 0.24 | 300 | 0.2903 | 0.92 | | 0.3039 | 0.28 | 350 | 0.2488 | 0.8933 | | 0.2633 | 0.32 | 400 | 0.2530 | 0.92 | | 0.2667 | 0.37 | 450 | 0.2125 | 0.9267 | | 0.2604 | 0.41 | 500 | 0.2628 | 0.8867 | | 0.278 | 0.45 | 550 | 0.2322 | 0.8867 | | 0.2625 | 0.49 | 600 | 0.1903 | 0.92 | | 0.2808 | 0.53 | 650 | 0.2400 | 0.8933 | | 0.2396 | 0.57 | 700 | 0.2184 | 0.9067 | | 0.2571 | 0.61 | 750 | 0.1906 | 0.9133 | | 0.2676 | 0.65 | 800 | 0.2467 | 0.9067 | | 0.2288 | 0.69 | 850 | 0.2038 | 0.9133 | | 0.2959 | 0.73 | 900 | 0.1941 | 0.9 | | 0.2619 | 0.77 | 950 | 0.2100 | 0.9333 | | 0.2504 | 0.81 | 1000 | 0.1523 | 0.9333 | | 0.2338 | 0.85 | 1050 | 0.1429 | 0.94 | | 0.2529 | 0.89 | 1100 | 0.1269 | 0.94 | | 0.2238 | 0.93 | 1150 | 0.1722 | 0.9333 | | 0.2295 | 0.97 | 1200 | 0.1874 | 0.94 | | 0.2089 | 1.01 | 1250 | 0.2214 | 0.9067 | | 0.1406 | 1.06 | 1300 | 0.3410 | 0.9133 | | 0.1587 | 1.1 | 1350 | 0.3330 | 0.9133 | | 0.1732 | 1.14 | 1400 | 0.2716 | 0.9133 | | 0.195 | 1.18 | 1450 | 0.3726 | 0.92 | | 0.1777 | 1.22 | 1500 | 0.2430 | 0.9267 | | 0.1433 | 1.26 | 1550 | 0.3011 | 0.9267 | | 0.1333 | 1.3 | 1600 | 0.2489 | 0.9333 | | 0.1516 | 1.34 | 1650 | 0.3340 | 0.9267 | | 0.1774 | 1.38 | 1700 | 0.2497 | 0.8933 | | 0.1608 | 1.42 | 1750 | 0.3234 | 0.9 | | 0.1534 | 1.46 | 1800 | 0.3383 | 0.9133 | | 0.1287 | 1.5 | 1850 | 0.3134 | 0.9133 | | 0.1422 | 1.54 | 1900 | 0.3330 | 0.9 | | 0.1578 | 1.58 | 1950 | 0.3281 | 0.9133 | | 0.1786 | 1.62 | 2000 | 0.2939 | 0.9267 | | 0.2019 | 1.66 | 2050 | 0.3535 | 0.9 | | 0.1995 | 1.7 | 2100 | 0.3032 | 0.9067 | | 0.159 | 1.75 | 2150 | 0.2598 | 0.9267 | | 0.1493 | 1.79 | 2200 | 0.2391 | 0.9267 | | 0.1748 | 1.83 | 2250 | 0.2258 | 0.92 | | 0.1783 | 1.87 | 2300 | 0.2749 | 0.9133 | | 0.1619 | 1.91 | 2350 | 0.2699 | 0.92 | | 0.1378 | 1.95 | 2400 | 0.2776 | 0.9067 | | 0.1529 | 1.99 | 2450 | 0.2235 | 0.9333 | | 0.1071 | 2.03 | 2500 | 0.2841 | 0.9267 | | 0.0812 | 2.07 | 2550 | 0.3178 | 0.9267 | | 0.0464 | 2.11 | 2600 | 0.3567 | 0.92 | | 0.1108 | 2.15 | 2650 | 0.2723 | 0.92 | | 0.0845 | 2.19 | 2700 | 0.2774 | 0.9267 | | 0.0795 | 2.23 | 2750 | 0.3027 | 0.9267 | | 0.0403 | 2.27 | 2800 | 0.3566 | 0.9267 | | 0.0664 | 2.31 | 2850 | 0.4015 | 0.92 | | 0.0659 | 2.35 | 2900 | 0.4298 | 0.9067 | | 0.1059 | 2.39 | 2950 | 0.4028 | 0.92 | | 0.105 | 2.44 | 3000 | 0.3701 | 0.92 | | 0.0808 | 2.48 | 3050 | 0.3206 | 0.9267 | | 0.0811 | 2.52 | 3100 | 0.3644 | 0.9133 | | 0.0458 | 2.56 | 3150 | 0.3781 | 0.9267 | | 0.0764 | 2.6 | 3200 | 0.3749 | 0.9267 | | 0.0567 | 2.64 | 3250 | 0.3995 | 0.92 | | 0.0971 | 2.68 | 3300 | 0.3455 | 0.92 | | 0.0579 | 2.72 | 3350 | 0.4508 | 0.92 | | 0.0853 | 2.76 | 3400 | 0.4350 | 0.92 | | 0.0577 | 2.8 | 3450 | 0.3804 | 0.9333 | | 0.0732 | 2.84 | 3500 | 0.4387 | 0.92 | | 0.0874 | 2.88 | 3550 | 0.3885 | 0.9333 | | 0.1031 | 2.92 | 3600 | 0.3937 | 0.92 | | 0.0335 | 2.96 | 3650 | 0.4963 | 0.8933 | | 0.0913 | 3.0 | 3700 | 0.3827 | 0.9333 | | 0.047 | 3.04 | 3750 | 0.4136 | 0.92 | | 0.0531 | 3.08 | 3800 | 0.4362 | 0.92 | | 0.0265 | 3.12 | 3850 | 0.4857 | 0.92 | | 0.038 | 3.17 | 3900 | 0.4425 | 0.92 | | 0.0294 | 3.21 | 3950 | 0.4347 | 0.92 | | 0.0367 | 3.25 | 4000 | 0.4291 | 0.9333 | | 0.0102 | 3.29 | 4050 | 0.5178 | 0.9267 | | 0.0311 | 3.33 | 4100 | 0.4784 | 0.9267 | | 0.0274 | 3.37 | 4150 | 0.5421 | 0.9267 | | 0.0275 | 3.41 | 4200 | 0.5194 | 0.92 | | 0.0795 | 3.45 | 4250 | 0.4788 | 0.92 | | 0.0413 | 3.49 | 4300 | 0.4393 | 0.9267 | | 0.0373 | 3.53 | 4350 | 0.4965 | 0.92 | | 0.0303 | 3.57 | 4400 | 0.4284 | 0.9267 | | 0.0248 | 3.61 | 4450 | 0.4476 | 0.9267 | | 0.0557 | 3.65 | 4500 | 0.4690 | 0.92 | | 0.0358 | 3.69 | 4550 | 0.4774 | 0.9133 | | 0.0194 | 3.73 | 4600 | 0.4755 | 0.92 | | 0.0473 | 3.77 | 4650 | 0.4637 | 0.92 | | 0.0133 | 3.81 | 4700 | 0.4868 | 0.92 | | 0.0204 | 3.86 | 4750 | 0.4886 | 0.9267 | | 0.0338 | 3.9 | 4800 | 0.5101 | 0.9267 | | 0.0424 | 3.94 | 4850 | 0.4812 | 0.9267 | | 0.0237 | 3.98 | 4900 | 0.4837 | 0.9267 | | 0.0372 | 4.02 | 4950 | 0.5000 | 0.9267 | | 0.0254 | 4.06 | 5000 | 0.5210 | 0.92 | | 0.024 | 4.1 | 5050 | 0.5272 | 0.92 | | 0.0117 | 4.14 | 5100 | 0.5447 | 0.92 | | 0.018 | 4.18 | 5150 | 0.5353 | 0.92 | | 0.0097 | 4.22 | 5200 | 0.5415 | 0.9267 | | 0.0151 | 4.26 | 5250 | 0.5447 | 0.9267 | | 0.0118 | 4.3 | 5300 | 0.5285 | 0.9267 | | 0.0004 | 4.34 | 5350 | 0.5399 | 0.9267 | | 0.0102 | 4.38 | 5400 | 0.5552 | 0.9267 | | 0.0012 | 4.42 | 5450 | 0.5689 | 0.92 | | 0.02 | 4.46 | 5500 | 0.5619 | 0.9267 | | 0.0056 | 4.5 | 5550 | 0.5784 | 0.92 | | 0.0271 | 4.55 | 5600 | 0.5766 | 0.92 | | 0.0191 | 4.59 | 5650 | 0.5662 | 0.92 | | 0.0311 | 4.63 | 5700 | 0.5514 | 0.9267 | | 0.0167 | 4.67 | 5750 | 0.5510 | 0.9267 | | 0.0293 | 4.71 | 5800 | 0.5571 | 0.9267 | | 0.0304 | 4.75 | 5850 | 0.5494 | 0.92 | | 0.0161 | 4.79 | 5900 | 0.5469 | 0.9267 | | 0.0017 | 4.83 | 5950 | 0.5468 | 0.9267 | | 0.0176 | 4.87 | 6000 | 0.5426 | 0.9267 | | 0.0094 | 4.91 | 6050 | 0.5402 | 0.9267 | | 0.0041 | 4.95 | 6100 | 0.5416 | 0.9267 | | 0.0281 | 4.99 | 6150 | 0.5419 | 0.9267 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.3.2 - Tokenizers 0.13.2

Pramodith/sd-class-butterflies-32

Pramodith

2022-11-28T23:19:08Z

38

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T23:18:35Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(Pramodith/sd-class-butterflies-32) image = pipeline().images[0] image ```

dogeplusplus/sd-class-butterflies-32

dogeplusplus

2022-11-28T23:02:51Z

35

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T23:02:05Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(dogeplusplus/sd-class-butterflies-32) image = pipeline().images[0] image ```

ali97/sd-class-butterflies-32

ali97

2022-11-28T22:31:50Z

37

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T22:31:00Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(ali97/sd-class-butterflies-32) image = pipeline().images[0] image ```

kanixwang/my-awesome-setfit-model

kanixwang

2022-11-28T22:19:56Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-11-28T22:02:13Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 40 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 40, "warmup_steps": 4, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

alryan1478/gpt-neo-125M-DOD-LOW

alryan1478

2022-11-28T22:19:47Z

103

0

transformers

[ "transformers", "pytorch", "gpt_neo", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-11-28T21:59:56Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: gpt-neo-125M-DOD-LOW results: [] ---  # gpt-neo-125M-DOD-LOW This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.0427 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 261 | 6.4768 | | 6.8863 | 2.0 | 522 | 6.1056 | | 6.8863 | 3.0 | 783 | 6.0427 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2

futuredatascience/action-classifier-v1

futuredatascience

2022-11-28T22:17:56Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-11-28T22:17:44Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 105 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 1050, "warmup_steps": 105, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

ThomasSimonini/ML-Agents-SnowballFight-1vs1-model

ThomasSimonini

2022-11-28T22:07:31Z

6

0

ml-agents

[ "ml-agents", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Snowballfight-1vs1", "region:us" ]

reinforcement-learning

2022-11-28T21:26:07Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Snowballfight-1vs1 library_name: ml-agents ---

alryan1478/gpt-neo-125M-wikitext2

alryan1478

2022-11-28T21:57:47Z

4

0

transformers

[ "transformers", "pytorch", "gpt_neo", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-11-22T20:55:28Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: gpt-neo-125M-wikitext2 results: [] ---  # gpt-neo-125M-wikitext2 This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.0325 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 259 | 6.4308 | | 6.8563 | 2.0 | 518 | 6.0898 | | 6.8563 | 3.0 | 777 | 6.0325 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2

michaelmayo704/sd-class-butterflies-64

michaelmayo704

2022-11-28T21:39:43Z

34

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T21:38:51Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(michaelmayo704/sd-class-butterflies-64) image = pipeline().images[0] image ```

pig4431/YELP_DistilBERT_5E

pig4431

2022-11-28T21:37:46Z

107

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:yelp_review_full", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-28T21:21:40Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - yelp_review_full metrics: - accuracy model-index: - name: YELP_DistilBERT_5E results: - task: name: Text Classification type: text-classification dataset: name: yelp_review_full type: yelp_review_full config: yelp_review_full split: train args: yelp_review_full metrics: - name: Accuracy type: accuracy value: 0.9666666666666667 ---  # YELP_DistilBERT_5E This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 0.1557 - Accuracy: 0.9667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6211 | 0.03 | 50 | 0.3873 | 0.8933 | | 0.3252 | 0.06 | 100 | 0.2181 | 0.92 | | 0.2241 | 0.1 | 150 | 0.1850 | 0.94 | | 0.2645 | 0.13 | 200 | 0.1514 | 0.9467 | | 0.2094 | 0.16 | 250 | 0.1850 | 0.92 | | 0.2693 | 0.19 | 300 | 0.1504 | 0.9467 | | 0.2524 | 0.22 | 350 | 0.1479 | 0.96 | | 0.2538 | 0.26 | 400 | 0.1375 | 0.94 | | 0.1937 | 0.29 | 450 | 0.1204 | 0.9467 | | 0.1692 | 0.32 | 500 | 0.1396 | 0.9533 | | 0.1987 | 0.35 | 550 | 0.1151 | 0.94 | | 0.207 | 0.38 | 600 | 0.1705 | 0.94 | | 0.2135 | 0.42 | 650 | 0.1189 | 0.9467 | | 0.1847 | 0.45 | 700 | 0.1315 | 0.9533 | | 0.169 | 0.48 | 750 | 0.1407 | 0.9533 | | 0.1767 | 0.51 | 800 | 0.1675 | 0.9333 | | 0.1899 | 0.54 | 850 | 0.0913 | 0.9467 | | 0.1641 | 0.58 | 900 | 0.0954 | 0.96 | | 0.1765 | 0.61 | 950 | 0.1237 | 0.9467 | | 0.1663 | 0.64 | 1000 | 0.1029 | 0.9533 | | 0.1238 | 0.67 | 1050 | 0.1267 | 0.96 | | 0.2087 | 0.7 | 1100 | 0.1111 | 0.96 | | 0.1354 | 0.74 | 1150 | 0.0916 | 0.9667 | | 0.1937 | 0.77 | 1200 | 0.1059 | 0.96 | | 0.2216 | 0.8 | 1250 | 0.1049 | 0.9467 | | 0.1788 | 0.83 | 1300 | 0.1472 | 0.94 | | 0.2138 | 0.86 | 1350 | 0.1234 | 0.9467 | | 0.1555 | 0.9 | 1400 | 0.1386 | 0.94 | | 0.1583 | 0.93 | 1450 | 0.1642 | 0.9467 | | 0.1525 | 0.96 | 1500 | 0.1571 | 0.94 | | 0.2049 | 0.99 | 1550 | 0.1257 | 0.9333 | | 0.1266 | 1.02 | 1600 | 0.1677 | 0.94 | | 0.1282 | 1.06 | 1650 | 0.1307 | 0.9533 | | 0.1007 | 1.09 | 1700 | 0.1375 | 0.9533 | | 0.0991 | 1.12 | 1750 | 0.1513 | 0.9533 | | 0.1211 | 1.15 | 1800 | 0.1229 | 0.9667 | | 0.1833 | 1.18 | 1850 | 0.1105 | 0.9733 | | 0.1596 | 1.22 | 1900 | 0.1279 | 0.9533 | | 0.1172 | 1.25 | 1950 | 0.1124 | 0.96 | | 0.1137 | 1.28 | 2000 | 0.1407 | 0.9467 | | 0.1135 | 1.31 | 2050 | 0.1377 | 0.96 | | 0.096 | 1.34 | 2100 | 0.1022 | 0.9667 | | 0.1203 | 1.38 | 2150 | 0.1719 | 0.9467 | | 0.1289 | 1.41 | 2200 | 0.1254 | 0.9667 | | 0.1392 | 1.44 | 2250 | 0.1086 | 0.9667 | | 0.1319 | 1.47 | 2300 | 0.1511 | 0.9467 | | 0.1161 | 1.5 | 2350 | 0.1758 | 0.9467 | | 0.1402 | 1.54 | 2400 | 0.1369 | 0.96 | | 0.1433 | 1.57 | 2450 | 0.1495 | 0.9667 | | 0.1882 | 1.6 | 2500 | 0.1186 | 0.9467 | | 0.1474 | 1.63 | 2550 | 0.1249 | 0.9533 | | 0.0937 | 1.66 | 2600 | 0.1390 | 0.96 | | 0.1231 | 1.7 | 2650 | 0.1467 | 0.96 | | 0.1485 | 1.73 | 2700 | 0.1602 | 0.9533 | | 0.1683 | 1.76 | 2750 | 0.1884 | 0.9533 | | 0.1141 | 1.79 | 2800 | 0.1634 | 0.96 | | 0.1351 | 1.82 | 2850 | 0.1212 | 0.9733 | | 0.1298 | 1.86 | 2900 | 0.1224 | 0.96 | | 0.1616 | 1.89 | 2950 | 0.1241 | 0.96 | | 0.1159 | 1.92 | 3000 | 0.1532 | 0.9533 | | 0.1101 | 1.95 | 3050 | 0.1105 | 0.96 | | 0.0779 | 1.98 | 3100 | 0.1334 | 0.9533 | | 0.1427 | 2.02 | 3150 | 0.1026 | 0.9733 | | 0.0673 | 2.05 | 3200 | 0.1231 | 0.96 | | 0.0901 | 2.08 | 3250 | 0.1077 | 0.9733 | | 0.0532 | 2.11 | 3300 | 0.1385 | 0.9467 | | 0.0984 | 2.14 | 3350 | 0.1432 | 0.9467 | | 0.1006 | 2.18 | 3400 | 0.1183 | 0.9667 | | 0.067 | 2.21 | 3450 | 0.1533 | 0.9533 | | 0.0901 | 2.24 | 3500 | 0.1314 | 0.9733 | | 0.0644 | 2.27 | 3550 | 0.1354 | 0.9667 | | 0.076 | 2.3 | 3600 | 0.1548 | 0.96 | | 0.0932 | 2.34 | 3650 | 0.1624 | 0.9667 | | 0.0777 | 2.37 | 3700 | 0.1878 | 0.9533 | | 0.106 | 2.4 | 3750 | 0.1721 | 0.96 | | 0.0621 | 2.43 | 3800 | 0.1470 | 0.9667 | | 0.0919 | 2.46 | 3850 | 0.1478 | 0.96 | | 0.091 | 2.5 | 3900 | 0.1371 | 0.9667 | | 0.0912 | 2.53 | 3950 | 0.1467 | 0.9667 | | 0.0775 | 2.56 | 4000 | 0.1289 | 0.9733 | | 0.1053 | 2.59 | 4050 | 0.1107 | 0.9733 | | 0.063 | 2.62 | 4100 | 0.1031 | 0.9733 | | 0.0859 | 2.66 | 4150 | 0.0953 | 0.98 | | 0.084 | 2.69 | 4200 | 0.1216 | 0.9733 | | 0.1215 | 2.72 | 4250 | 0.1025 | 0.9733 | | 0.0675 | 2.75 | 4300 | 0.0992 | 0.9667 | | 0.0608 | 2.78 | 4350 | 0.1288 | 0.96 | | 0.0965 | 2.82 | 4400 | 0.1179 | 0.9667 | | 0.061 | 2.85 | 4450 | 0.1178 | 0.9733 | | 0.0821 | 2.88 | 4500 | 0.1188 | 0.9733 | | 0.0802 | 2.91 | 4550 | 0.1423 | 0.9667 | | 0.0901 | 2.94 | 4600 | 0.1367 | 0.96 | | 0.1069 | 2.98 | 4650 | 0.1118 | 0.9733 | | 0.0653 | 3.01 | 4700 | 0.1359 | 0.9533 | | 0.0577 | 3.04 | 4750 | 0.1046 | 0.9667 | | 0.0467 | 3.07 | 4800 | 0.1366 | 0.96 | | 0.041 | 3.1 | 4850 | 0.1276 | 0.9667 | | 0.0585 | 3.13 | 4900 | 0.1426 | 0.9667 | | 0.0635 | 3.17 | 4950 | 0.1571 | 0.96 | | 0.0395 | 3.2 | 5000 | 0.1527 | 0.96 | | 0.034 | 3.23 | 5050 | 0.1323 | 0.9667 | | 0.0405 | 3.26 | 5100 | 0.1377 | 0.96 | | 0.0306 | 3.29 | 5150 | 0.1526 | 0.9667 | | 0.0471 | 3.33 | 5200 | 0.1419 | 0.9667 | | 0.0646 | 3.36 | 5250 | 0.1459 | 0.9667 | | 0.0508 | 3.39 | 5300 | 0.1312 | 0.9667 | | 0.0593 | 3.42 | 5350 | 0.1483 | 0.96 | | 0.05 | 3.45 | 5400 | 0.1076 | 0.9733 | | 0.0559 | 3.49 | 5450 | 0.1412 | 0.9667 | | 0.0614 | 3.52 | 5500 | 0.1597 | 0.9667 | | 0.0691 | 3.55 | 5550 | 0.1656 | 0.96 | | 0.0472 | 3.58 | 5600 | 0.1556 | 0.9667 | | 0.055 | 3.61 | 5650 | 0.1347 | 0.9667 | | 0.0564 | 3.65 | 5700 | 0.1424 | 0.96 | | 0.0567 | 3.68 | 5750 | 0.1448 | 0.9733 | | 0.0645 | 3.71 | 5800 | 0.1290 | 0.9667 | | 0.0361 | 3.74 | 5850 | 0.1367 | 0.9667 | | 0.0546 | 3.77 | 5900 | 0.1406 | 0.9667 | | 0.043 | 3.81 | 5950 | 0.1337 | 0.96 | | 0.0148 | 3.84 | 6000 | 0.1475 | 0.9533 | | 0.0922 | 3.87 | 6050 | 0.1318 | 0.9733 | | 0.0671 | 3.9 | 6100 | 0.1446 | 0.9733 | | 0.0295 | 3.93 | 6150 | 0.1217 | 0.9733 | | 0.0503 | 3.97 | 6200 | 0.1133 | 0.9733 | | 0.0457 | 4.0 | 6250 | 0.1145 | 0.9733 | | 0.0487 | 4.03 | 6300 | 0.1119 | 0.9733 | | 0.0491 | 4.06 | 6350 | 0.1274 | 0.9667 | | 0.0417 | 4.09 | 6400 | 0.1377 | 0.9733 | | 0.0595 | 4.13 | 6450 | 0.1271 | 0.9733 | | 0.035 | 4.16 | 6500 | 0.1183 | 0.9733 | | 0.0482 | 4.19 | 6550 | 0.1153 | 0.9733 | | 0.0196 | 4.22 | 6600 | 0.1388 | 0.9733 | | 0.028 | 4.25 | 6650 | 0.1310 | 0.9733 | | 0.0193 | 4.29 | 6700 | 0.1460 | 0.9667 | | 0.0233 | 4.32 | 6750 | 0.1233 | 0.9733 | | 0.0316 | 4.35 | 6800 | 0.1220 | 0.9667 | | 0.0132 | 4.38 | 6850 | 0.1350 | 0.9533 | | 0.0415 | 4.41 | 6900 | 0.1547 | 0.9667 | | 0.0157 | 4.45 | 6950 | 0.1562 | 0.9667 | | 0.0186 | 4.48 | 7000 | 0.1424 | 0.9667 | | 0.0012 | 4.51 | 7050 | 0.1421 | 0.9667 | | 0.0223 | 4.54 | 7100 | 0.1475 | 0.9733 | | 0.0455 | 4.57 | 7150 | 0.1457 | 0.96 | | 0.0571 | 4.61 | 7200 | 0.1559 | 0.9667 | | 0.0305 | 4.64 | 7250 | 0.1614 | 0.9667 | | 0.0457 | 4.67 | 7300 | 0.1691 | 0.9667 | | 0.022 | 4.7 | 7350 | 0.1622 | 0.9667 | | 0.0338 | 4.73 | 7400 | 0.1560 | 0.9667 | | 0.0365 | 4.77 | 7450 | 0.1553 | 0.9667 | | 0.025 | 4.8 | 7500 | 0.1512 | 0.9667 | | 0.0441 | 4.83 | 7550 | 0.1550 | 0.9667 | | 0.0363 | 4.86 | 7600 | 0.1564 | 0.9667 | | 0.0188 | 4.89 | 7650 | 0.1553 | 0.9667 | | 0.0427 | 4.93 | 7700 | 0.1572 | 0.9733 | | 0.0362 | 4.96 | 7750 | 0.1568 | 0.9667 | | 0.0115 | 4.99 | 7800 | 0.1557 | 0.9667 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.3.2 - Tokenizers 0.13.2

rlarios/distilbert-base-uncased-finetuned-emotion

rlarios

2022-11-28T21:34:34Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-25T20:15:59Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9325 - name: F1 type: f1 value: 0.9322428116765227 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2225 - Accuracy: 0.9325 - F1: 0.9322 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8372 | 1.0 | 250 | 0.3225 | 0.9045 | 0.9017 | | 0.2534 | 2.0 | 500 | 0.2225 | 0.9325 | 0.9322 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cpu - Datasets 2.6.1 - Tokenizers 0.13.1

pig4431/TUF_ALBERT_5E

pig4431

2022-11-28T21:34:30Z

105

0

transformers

[ "transformers", "pytorch", "albert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-28T21:32:18Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: TUF_ALBERT_5E results: [] ---  # TUF_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2389 - Accuracy: 0.9533 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5099 | 0.1 | 50 | 0.3861 | 0.8533 | | 0.2985 | 0.2 | 100 | 0.2961 | 0.8933 | | 0.2972 | 0.3 | 150 | 0.2335 | 0.9333 | | 0.2835 | 0.4 | 200 | 0.1872 | 0.94 | | 0.26 | 0.5 | 250 | 0.4147 | 0.9133 | | 0.2986 | 0.59 | 300 | 0.2080 | 0.9267 | | 0.2554 | 0.69 | 350 | 0.3984 | 0.9133 | | 0.2306 | 0.79 | 400 | 0.2136 | 0.9333 | | 0.2218 | 0.89 | 450 | 0.4455 | 0.8867 | | 0.2113 | 0.99 | 500 | 0.2205 | 0.94 | | 0.2541 | 1.09 | 550 | 0.1705 | 0.9333 | | 0.1947 | 1.19 | 600 | 0.3264 | 0.8933 | | 0.2409 | 1.29 | 650 | 0.2084 | 0.92 | | 0.1968 | 1.39 | 700 | 0.2550 | 0.9267 | | 0.172 | 1.49 | 750 | 0.2238 | 0.9467 | | 0.1478 | 1.58 | 800 | 0.2501 | 0.9533 | | 0.2199 | 1.68 | 850 | 0.2618 | 0.9133 | | 0.1792 | 1.78 | 900 | 0.2109 | 0.9267 | | 0.1831 | 1.88 | 950 | 0.2641 | 0.92 | | 0.1534 | 1.98 | 1000 | 0.1924 | 0.94 | | 0.1208 | 2.08 | 1050 | 0.2990 | 0.9333 | | 0.1118 | 2.18 | 1100 | 0.4952 | 0.9 | | 0.158 | 2.28 | 1150 | 0.1706 | 0.9533 | | 0.1163 | 2.38 | 1200 | 0.1238 | 0.9733 | | 0.1738 | 2.48 | 1250 | 0.1989 | 0.9467 | | 0.1305 | 2.57 | 1300 | 0.4354 | 0.9067 | | 0.1668 | 2.67 | 1350 | 0.1276 | 0.9667 | | 0.1195 | 2.77 | 1400 | 0.2170 | 0.9533 | | 0.1057 | 2.87 | 1450 | 0.2882 | 0.9333 | | 0.1172 | 2.97 | 1500 | 0.1435 | 0.9667 | | 0.0893 | 3.07 | 1550 | 0.1754 | 0.96 | | 0.0582 | 3.17 | 1600 | 0.1858 | 0.96 | | 0.0887 | 3.27 | 1650 | 0.4954 | 0.92 | | 0.1166 | 3.37 | 1700 | 0.2356 | 0.9467 | | 0.0518 | 3.47 | 1750 | 0.1910 | 0.96 | | 0.0741 | 3.56 | 1800 | 0.1328 | 0.9733 | | 0.072 | 3.66 | 1850 | 0.2769 | 0.9467 | | 0.0534 | 3.76 | 1900 | 0.3501 | 0.94 | | 0.0776 | 3.86 | 1950 | 0.3171 | 0.94 | | 0.0537 | 3.96 | 2000 | 0.2138 | 0.9533 | | 0.0683 | 4.06 | 2050 | 0.2934 | 0.94 | | 0.015 | 4.16 | 2100 | 0.2233 | 0.9533 | | 0.0236 | 4.26 | 2150 | 0.2673 | 0.9533 | | 0.0357 | 4.36 | 2200 | 0.2279 | 0.96 | | 0.0298 | 4.46 | 2250 | 0.3017 | 0.9467 | | 0.0357 | 4.55 | 2300 | 0.2910 | 0.9467 | | 0.0208 | 4.65 | 2350 | 0.2498 | 0.9533 | | 0.0345 | 4.75 | 2400 | 0.2259 | 0.9667 | | 0.0174 | 4.85 | 2450 | 0.2274 | 0.9667 | | 0.0393 | 4.95 | 2500 | 0.2389 | 0.9533 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2

anikethjr/PromoGen_K562_2080Ti_restart

anikethjr

2022-11-28T21:24:36Z

91

0

transformers

[ "transformers", "pytorch", "tensorboard", "prophetnet", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-11-27T05:27:24Z

--- tags: - generated_from_trainer model-index: - name: PromoGen_K562_2080Ti_restart results: [] ---  # PromoGen_K562_2080Ti_restart This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4624 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 0.7676 | 0.49 | 2500 | 0.7383 | | 0.7121 | 0.97 | 5000 | 0.6867 | | 0.6914 | 1.46 | 7500 | 0.6705 | | 0.6837 | 1.95 | 10000 | 0.6622 | | 0.6778 | 2.44 | 12500 | 0.6558 | | 0.6748 | 2.92 | 15000 | 0.6517 | | 0.6676 | 3.41 | 17500 | 0.6433 | | 0.6593 | 3.9 | 20000 | 0.6358 | | 0.6584 | 4.38 | 22500 | 0.6320 | | 0.6557 | 4.87 | 25000 | 0.6301 | | 0.6523 | 5.36 | 27500 | 0.6257 | | 0.6478 | 5.84 | 30000 | 0.6236 | | 0.6393 | 6.33 | 32500 | 0.6145 | | 0.6039 | 6.82 | 35000 | 0.5658 | | 0.5616 | 7.31 | 37500 | 0.5376 | | 0.5518 | 7.79 | 40000 | 0.5310 | | 0.5509 | 8.28 | 42500 | 0.5273 | | 0.5487 | 8.77 | 45000 | 0.5261 | | 0.5479 | 9.25 | 47500 | 0.5249 | | 0.546 | 9.74 | 50000 | 0.5242 | | 0.5447 | 10.23 | 52500 | 0.5229 | | 0.5439 | 10.71 | 55000 | 0.5220 | | 0.5433 | 11.2 | 57500 | 0.5209 | | 0.5394 | 11.69 | 60000 | 0.5162 | | 0.5153 | 12.18 | 62500 | 0.4944 | | 0.5137 | 12.66 | 65000 | 0.4932 | | 0.514 | 13.15 | 67500 | 0.4924 | | 0.5131 | 13.64 | 70000 | 0.4919 | | 0.5104 | 14.12 | 72500 | 0.4914 | | 0.5122 | 14.61 | 75000 | 0.4906 | | 0.5089 | 15.1 | 77500 | 0.4901 | | 0.5076 | 15.59 | 80000 | 0.4891 | | 0.4986 | 16.07 | 82500 | 0.4721 | | 0.4875 | 16.56 | 85000 | 0.4672 | | 0.4887 | 17.05 | 87500 | 0.4669 | | 0.4839 | 17.53 | 90000 | 0.4661 | | 0.4849 | 18.02 | 92500 | 0.4654 | | 0.4848 | 18.51 | 95000 | 0.4649 | | 0.4831 | 18.99 | 97500 | 0.4646 | | 0.4816 | 19.48 | 100000 | 0.4644 | | 0.4808 | 19.97 | 102500 | 0.4637 | | 0.4812 | 20.46 | 105000 | 0.4634 | | 0.4813 | 20.94 | 107500 | 0.4633 | | 0.4818 | 21.43 | 110000 | 0.4631 | | 0.4813 | 21.92 | 112500 | 0.4629 | | 0.4782 | 22.4 | 115000 | 0.4628 | | 0.4804 | 22.89 | 117500 | 0.4626 | | 0.4815 | 23.38 | 120000 | 0.4625 | | 0.4812 | 23.87 | 122500 | 0.4625 | | 0.4785 | 24.35 | 125000 | 0.4624 | | 0.4795 | 24.84 | 127500 | 0.4624 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.0 - Tokenizers 0.13.0.dev0

Inayat/Fine_tune_whisper_small

Inayat

2022-11-28T21:14:32Z

79

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-14T19:18:30Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: Fine_tune_whisper_small results: [] ---  # Fine_tune_whisper_small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8238 - Wer: 42.9362 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 900 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2994 | 3.92 | 200 | 0.6607 | 44.0797 | | 0.0201 | 7.84 | 400 | 0.7371 | 42.6042 | | 0.002 | 11.76 | 600 | 0.8027 | 42.5304 | | 0.0011 | 15.69 | 800 | 0.8238 | 42.9362 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

pig4431/TweetEval_DistilBERT_5E

pig4431

2022-11-28T21:09:36Z

103

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-28T21:03:38Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - accuracy model-index: - name: TweetEval_DistilBERT_5E results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: train args: sentiment metrics: - name: Accuracy type: accuracy value: 0.9133333333333333 ---  # TweetEval_DistilBERT_5E This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.4043 - Accuracy: 0.9133 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5747 | 0.04 | 50 | 0.4843 | 0.7333 | | 0.4336 | 0.08 | 100 | 0.2888 | 0.8667 | | 0.3437 | 0.12 | 150 | 0.2895 | 0.8667 | | 0.3375 | 0.16 | 200 | 0.2864 | 0.8733 | | 0.3072 | 0.2 | 250 | 0.2577 | 0.8867 | | 0.3019 | 0.24 | 300 | 0.2574 | 0.8933 | | 0.2662 | 0.28 | 350 | 0.2621 | 0.8867 | | 0.283 | 0.32 | 400 | 0.2340 | 0.92 | | 0.2949 | 0.37 | 450 | 0.2482 | 0.8933 | | 0.3066 | 0.41 | 500 | 0.2537 | 0.9 | | 0.2457 | 0.45 | 550 | 0.2473 | 0.9 | | 0.295 | 0.49 | 600 | 0.2177 | 0.9133 | | 0.2862 | 0.53 | 650 | 0.2215 | 0.9133 | | 0.2603 | 0.57 | 700 | 0.2272 | 0.9133 | | 0.2976 | 0.61 | 750 | 0.2298 | 0.9067 | | 0.2823 | 0.65 | 800 | 0.2451 | 0.8933 | | 0.2583 | 0.69 | 850 | 0.2645 | 0.8933 | | 0.2694 | 0.73 | 900 | 0.2352 | 0.9 | | 0.2433 | 0.77 | 950 | 0.2322 | 0.9133 | | 0.2598 | 0.81 | 1000 | 0.2300 | 0.9 | | 0.2701 | 0.85 | 1050 | 0.2162 | 0.9 | | 0.2227 | 0.89 | 1100 | 0.2135 | 0.8933 | | 0.2045 | 0.93 | 1150 | 0.2233 | 0.9133 | | 0.2821 | 0.97 | 1200 | 0.2194 | 0.9 | | 0.2342 | 1.01 | 1250 | 0.2488 | 0.88 | | 0.2028 | 1.06 | 1300 | 0.2451 | 0.8867 | | 0.1509 | 1.1 | 1350 | 0.3174 | 0.88 | | 0.1888 | 1.14 | 1400 | 0.2537 | 0.9133 | | 0.1825 | 1.18 | 1450 | 0.2559 | 0.9067 | | 0.1721 | 1.22 | 1500 | 0.2511 | 0.92 | | 0.2137 | 1.26 | 1550 | 0.2963 | 0.9133 | | 0.2153 | 1.3 | 1600 | 0.2210 | 0.92 | | 0.1989 | 1.34 | 1650 | 0.2231 | 0.9133 | | 0.2155 | 1.38 | 1700 | 0.1991 | 0.9133 | | 0.1912 | 1.42 | 1750 | 0.2146 | 0.92 | | 0.1623 | 1.46 | 1800 | 0.2721 | 0.9 | | 0.2236 | 1.5 | 1850 | 0.2301 | 0.9267 | | 0.1907 | 1.54 | 1900 | 0.1988 | 0.92 | | 0.1286 | 1.58 | 1950 | 0.2326 | 0.9 | | 0.2147 | 1.62 | 2000 | 0.2432 | 0.9267 | | 0.2018 | 1.66 | 2050 | 0.2162 | 0.9067 | | 0.2073 | 1.7 | 2100 | 0.2153 | 0.9133 | | 0.1498 | 1.75 | 2150 | 0.2335 | 0.92 | | 0.1812 | 1.79 | 2200 | 0.2275 | 0.9267 | | 0.1482 | 1.83 | 2250 | 0.2734 | 0.9 | | 0.2233 | 1.87 | 2300 | 0.2454 | 0.9 | | 0.1673 | 1.91 | 2350 | 0.2394 | 0.92 | | 0.1555 | 1.95 | 2400 | 0.2725 | 0.92 | | 0.2082 | 1.99 | 2450 | 0.2684 | 0.9133 | | 0.1545 | 2.03 | 2500 | 0.3049 | 0.9067 | | 0.1384 | 2.07 | 2550 | 0.2960 | 0.9133 | | 0.1201 | 2.11 | 2600 | 0.3259 | 0.9 | | 0.1348 | 2.15 | 2650 | 0.3091 | 0.9133 | | 0.1046 | 2.19 | 2700 | 0.2916 | 0.9267 | | 0.1506 | 2.23 | 2750 | 0.2910 | 0.9133 | | 0.1481 | 2.27 | 2800 | 0.2855 | 0.9067 | | 0.1318 | 2.31 | 2850 | 0.3075 | 0.9 | | 0.1204 | 2.35 | 2900 | 0.3169 | 0.8933 | | 0.1669 | 2.39 | 2950 | 0.3050 | 0.9067 | | 0.1725 | 2.44 | 3000 | 0.2970 | 0.9133 | | 0.1305 | 2.48 | 3050 | 0.3065 | 0.9 | | 0.1508 | 2.52 | 3100 | 0.3079 | 0.9133 | | 0.184 | 2.56 | 3150 | 0.3482 | 0.9067 | | 0.1263 | 2.6 | 3200 | 0.3310 | 0.9 | | 0.1282 | 2.64 | 3250 | 0.3520 | 0.8933 | | 0.1217 | 2.68 | 3300 | 0.3158 | 0.9067 | | 0.1203 | 2.72 | 3350 | 0.3351 | 0.92 | | 0.1068 | 2.76 | 3400 | 0.3239 | 0.92 | | 0.1517 | 2.8 | 3450 | 0.3247 | 0.92 | | 0.113 | 2.84 | 3500 | 0.3269 | 0.9133 | | 0.1276 | 2.88 | 3550 | 0.3162 | 0.92 | | 0.1548 | 2.92 | 3600 | 0.3196 | 0.9133 | | 0.1305 | 2.96 | 3650 | 0.3163 | 0.92 | | 0.149 | 3.0 | 3700 | 0.3013 | 0.92 | | 0.0816 | 3.04 | 3750 | 0.3097 | 0.9267 | | 0.0884 | 3.08 | 3800 | 0.3028 | 0.92 | | 0.0727 | 3.12 | 3850 | 0.3487 | 0.9133 | | 0.1018 | 3.17 | 3900 | 0.3447 | 0.92 | | 0.1266 | 3.21 | 3950 | 0.3589 | 0.9133 | | 0.1216 | 3.25 | 4000 | 0.3464 | 0.92 | | 0.091 | 3.29 | 4050 | 0.3454 | 0.92 | | 0.0829 | 3.33 | 4100 | 0.3450 | 0.92 | | 0.1084 | 3.37 | 4150 | 0.3670 | 0.92 | | 0.0754 | 3.41 | 4200 | 0.3661 | 0.92 | | 0.094 | 3.45 | 4250 | 0.3588 | 0.9067 | | 0.0641 | 3.49 | 4300 | 0.3936 | 0.92 | | 0.1138 | 3.53 | 4350 | 0.3616 | 0.92 | | 0.0744 | 3.57 | 4400 | 0.3562 | 0.92 | | 0.0697 | 3.61 | 4450 | 0.3532 | 0.9267 | | 0.1083 | 3.65 | 4500 | 0.3451 | 0.9267 | | 0.0701 | 3.69 | 4550 | 0.3307 | 0.92 | | 0.0849 | 3.73 | 4600 | 0.3797 | 0.92 | | 0.09 | 3.77 | 4650 | 0.3746 | 0.9267 | | 0.0799 | 3.81 | 4700 | 0.3799 | 0.92 | | 0.0589 | 3.86 | 4750 | 0.3805 | 0.92 | | 0.0578 | 3.9 | 4800 | 0.3910 | 0.9133 | | 0.0816 | 3.94 | 4850 | 0.3856 | 0.9133 | | 0.1366 | 3.98 | 4900 | 0.3707 | 0.92 | | 0.0846 | 4.02 | 4950 | 0.3802 | 0.92 | | 0.0401 | 4.06 | 5000 | 0.3842 | 0.92 | | 0.0851 | 4.1 | 5050 | 0.3773 | 0.9267 | | 0.0514 | 4.14 | 5100 | 0.3922 | 0.9133 | | 0.0909 | 4.18 | 5150 | 0.3893 | 0.92 | | 0.0764 | 4.22 | 5200 | 0.3818 | 0.9133 | | 0.1208 | 4.26 | 5250 | 0.4096 | 0.92 | | 0.0689 | 4.3 | 5300 | 0.3940 | 0.9133 | | 0.0524 | 4.34 | 5350 | 0.4020 | 0.9133 | | 0.0733 | 4.38 | 5400 | 0.4002 | 0.9133 | | 0.0699 | 4.42 | 5450 | 0.4013 | 0.9133 | | 0.0712 | 4.46 | 5500 | 0.4037 | 0.9067 | | 0.0557 | 4.5 | 5550 | 0.4121 | 0.92 | | 0.0679 | 4.55 | 5600 | 0.4067 | 0.9133 | | 0.0651 | 4.59 | 5650 | 0.4194 | 0.9133 | | 0.0607 | 4.63 | 5700 | 0.4007 | 0.9133 | | 0.0676 | 4.67 | 5750 | 0.4013 | 0.9133 | | 0.0303 | 4.71 | 5800 | 0.3984 | 0.9133 | | 0.0674 | 4.75 | 5850 | 0.4037 | 0.9133 | | 0.0842 | 4.79 | 5900 | 0.4072 | 0.9133 | | 0.0516 | 4.83 | 5950 | 0.4096 | 0.9133 | | 0.0556 | 4.87 | 6000 | 0.4111 | 0.92 | | 0.0277 | 4.91 | 6050 | 0.4079 | 0.9133 | | 0.0629 | 4.95 | 6100 | 0.4053 | 0.9133 | | 0.0426 | 4.99 | 6150 | 0.4043 | 0.9133 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.3.2 - Tokenizers 0.13.2

futuredatascience/to-classifier-v1

futuredatascience

2022-11-28T20:53:10Z

2

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-11-28T20:52:58Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 53 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 530, "warmup_steps": 53, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

futuredatascience/from-classifier-v1

futuredatascience

2022-11-28T20:07:27Z

4

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-11-28T20:07:15Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 53 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 530, "warmup_steps": 53, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

reubenjohn/stack-overflow-open-status-classifier-pt

reubenjohn

2022-11-28T20:01:21Z

4

0

transformers

[ "transformers", "pytorch", "albert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-16T03:44:14Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: stack-overflow-open-status-classifier-pt results: [] ---  # stack-overflow-open-status-classifier-pt This model is a fine-tuned version of [reubenjohn/stack-overflow-open-status-classifier-pt](https://huggingface.co/reubenjohn/stack-overflow-open-status-classifier-pt) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.9448 - eval_runtime: 3.554 - eval_samples_per_second: 28.137 - eval_steps_per_second: 0.563 - epoch: 0.01 - step: 60 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 1 ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

motmono/a2c-AntBulletEnv-v0

motmono

2022-11-28T19:58:24Z

2

0

stable-baselines3

[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-11-28T19:57:12Z

--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1539.68 +/- 213.96 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

pig4431/TUF_roBERTa_5E

pig4431

2022-11-28T19:55:07Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-28T19:48:29Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: TUF_roBERTa_5E results: [] ---  # TUF_roBERTa_5E This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2136 - Accuracy: 0.9667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4665 | 0.1 | 50 | 0.2587 | 0.9333 | | 0.245 | 0.2 | 100 | 0.1355 | 0.96 | | 0.2079 | 0.3 | 150 | 0.1454 | 0.9533 | | 0.2098 | 0.4 | 200 | 0.1809 | 0.9533 | | 0.1637 | 0.5 | 250 | 0.2299 | 0.94 | | 0.1869 | 0.59 | 300 | 0.1324 | 0.9667 | | 0.2202 | 0.69 | 350 | 0.1786 | 0.9467 | | 0.2084 | 0.79 | 400 | 0.1541 | 0.9533 | | 0.148 | 0.89 | 450 | 0.1790 | 0.9533 | | 0.1945 | 0.99 | 500 | 0.1168 | 0.9667 | | 0.1648 | 1.09 | 550 | 0.1153 | 0.96 | | 0.1099 | 1.19 | 600 | 0.1239 | 0.96 | | 0.1238 | 1.29 | 650 | 0.1486 | 0.9533 | | 0.1067 | 1.39 | 700 | 0.1195 | 0.96 | | 0.1324 | 1.49 | 750 | 0.1134 | 0.96 | | 0.1128 | 1.58 | 800 | 0.1180 | 0.9667 | | 0.1406 | 1.68 | 850 | 0.2081 | 0.9533 | | 0.1516 | 1.78 | 900 | 0.1987 | 0.9533 | | 0.1537 | 1.88 | 950 | 0.1644 | 0.96 | | 0.0957 | 1.98 | 1000 | 0.1660 | 0.96 | | 0.0699 | 2.08 | 1050 | 0.2057 | 0.9533 | | 0.1007 | 2.18 | 1100 | 0.2336 | 0.9533 | | 0.0677 | 2.28 | 1150 | 0.2399 | 0.9467 | | 0.059 | 2.38 | 1200 | 0.2331 | 0.96 | | 0.1051 | 2.48 | 1250 | 0.1974 | 0.9533 | | 0.0778 | 2.57 | 1300 | 0.2857 | 0.9467 | | 0.1099 | 2.67 | 1350 | 0.2641 | 0.9533 | | 0.0747 | 2.77 | 1400 | 0.2219 | 0.9533 | | 0.0874 | 2.87 | 1450 | 0.2780 | 0.9533 | | 0.0675 | 2.97 | 1500 | 0.1993 | 0.96 | | 0.052 | 3.07 | 1550 | 0.1918 | 0.96 | | 0.0214 | 3.17 | 1600 | 0.2410 | 0.96 | | 0.0512 | 3.27 | 1650 | 0.2353 | 0.96 | | 0.0548 | 3.37 | 1700 | 0.2722 | 0.9533 | | 0.0554 | 3.47 | 1750 | 0.1593 | 0.9733 | | 0.0742 | 3.56 | 1800 | 0.2568 | 0.96 | | 0.064 | 3.66 | 1850 | 0.2358 | 0.96 | | 0.052 | 3.76 | 1900 | 0.2161 | 0.9667 | | 0.0349 | 3.86 | 1950 | 0.2497 | 0.96 | | 0.0868 | 3.96 | 2000 | 0.1834 | 0.9667 | | 0.0445 | 4.06 | 2050 | 0.2441 | 0.9533 | | 0.0388 | 4.16 | 2100 | 0.2136 | 0.9667 | | 0.0484 | 4.26 | 2150 | 0.2114 | 0.9667 | | 0.0263 | 4.36 | 2200 | 0.2325 | 0.96 | | 0.0409 | 4.46 | 2250 | 0.2454 | 0.9533 | | 0.0324 | 4.55 | 2300 | 0.2105 | 0.9667 | | 0.0295 | 4.65 | 2350 | 0.2118 | 0.9667 | | 0.0372 | 4.75 | 2400 | 0.2005 | 0.9667 | | 0.0294 | 4.85 | 2450 | 0.2057 | 0.9667 | | 0.0354 | 4.95 | 2500 | 0.2136 | 0.9667 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2

altsoph/xlmr-AER

altsoph

2022-11-28T19:22:35Z

115

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "nlp", "roberta", "xlmr", "classifier", "aer", "narrative", "entity recognition", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-27T22:41:15Z

--- language: - en thumbnail: https://raw.githubusercontent.com/altsoph/misc/main/imgs/aer_logo.png tags: - nlp - roberta - xlmr - classifier - aer - narrative - entity recognition license: mit --- An XLM-Roberta based language model fine-tuned for AER (Actionable Entities Recognition) -- recognition of entities that protagonists could interact with for further plot development. We used 5K+ locations from 1K interactive text fiction games and extracted textual descriptions of locations and lists of actionable entities in them. The resulting [BAER dataset is available here](https://github.com/altsoph/BAER). Then we used it to train this model. The example of usage: ```py from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline MODEL_NAME = "altsoph/xlmr-AER" text = """This bedroom is extremely spare, with dirty laundry scattered haphazardly all over the floor. Cleaner clothing can be found in the dresser. A bathroom lies to the south, while a door to the east leads to the living room.""" model = AutoModelForTokenClassification.from_pretrained(MODEL_NAME) tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) pipe = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="simple", ignore_labels=['O','PAD']) entities = pipe(text) print(entities) ``` If you use the model, please cite the following: ``` @inproceedings{Tikhonov-etal-2022-AER, title = "Actionable Entities Recognition Benchmark for Interactive Fiction", author = "Alexey Tikhonov and Ivan P. Yamshchikov", year = "2022", } ```

essayproj/roberta-base-essay

essayproj

2022-11-28T19:08:54Z

59

0

transformers

[ "transformers", "tf", "roberta", "feature-extraction", "generated_from_keras_callback", "license:mit", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-28T19:08:03Z

--- license: mit tags: - generated_from_keras_callback model-index: - name: roberta-base-essay results: [] ---  # roberta-base-essay This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Tokenizers 0.13.2

Dagar/t5-small-science-papers-NIPS

Dagar

2022-11-28T18:21:27Z

107

1

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-28T18:00:28Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-science-papers-NIPS results: [] ---  # t5-small-science-papers-NIPS This model is a fine-tuned version of [Dagar/t5-small-science-papers](https://huggingface.co/Dagar/t5-small-science-papers) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.7566 - Rouge1: 15.7066 - Rouge2: 2.5654 - Rougel: 11.4679 - Rougelsum: 14.4017 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 318 | 5.1856 | 13.7172 | 2.0644 | 10.2189 | 12.838 | 19.0 | | 5.4522 | 2.0 | 636 | 5.0383 | 15.6211 | 2.1808 | 11.3561 | 14.3054 | 19.0 | | 5.4522 | 3.0 | 954 | 4.9486 | 15.1659 | 2.3308 | 11.1052 | 13.9456 | 19.0 | | 5.1254 | 4.0 | 1272 | 4.8851 | 15.716 | 2.4099 | 11.4954 | 14.5099 | 19.0 | | 4.9794 | 5.0 | 1590 | 4.8456 | 15.5507 | 2.4267 | 11.3867 | 14.3237 | 19.0 | | 4.9794 | 6.0 | 1908 | 4.8073 | 15.8406 | 2.4254 | 11.6878 | 14.6154 | 19.0 | | 4.8823 | 7.0 | 2226 | 4.7872 | 15.5554 | 2.4637 | 11.3401 | 14.3183 | 19.0 | | 4.8338 | 8.0 | 2544 | 4.7680 | 15.4783 | 2.4888 | 11.3364 | 14.2031 | 19.0 | | 4.8338 | 9.0 | 2862 | 4.7621 | 15.958 | 2.5662 | 11.6139 | 14.6576 | 19.0 | | 4.7838 | 10.0 | 3180 | 4.7566 | 15.7066 | 2.5654 | 11.4679 | 14.4017 | 19.0 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2