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 2025-09-10 00:38:21	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 551 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-10 00:38:17	card stringlengths 11 1.01M
waboucay/camembert-large-finetuned-xnli_fr_3_classes-finetuned-rua_wl_3_classes	waboucay	2022-06-20T09:34:17Z	6	0	transformers	[ "transformers", "pytorch", "camembert", "text-classification", "nli", "fr", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-20T09:23:44Z	--- language: - fr tags: - nli metrics: - f1 --- ## Eval results We obtain the following results on ```validation``` and ```test``` sets: \| Set \| F1<sub>micro</sub> \| F1<sub>macro</sub> \| \|------------\|--------------------\|--------------------\| \| validation \| 72.4 \| 72.2 \| \| test \| 72.8 \| 72.5 \|
qgrantq/bert-finetuned-squad	qgrantq	2022-06-20T08:03:46Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-06-20T05:30:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bert-finetuned-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. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) 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 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
jacobbieker/dgmr	jacobbieker	2022-06-20T07:43:41Z	4	1	transformers	[ "transformers", "pytorch", "nowcasting", "forecasting", "timeseries", "remote-sensing", "gan", "license:mit", "endpoints_compatible", "region:us" ]	null	2022-06-20T07:44:17Z	--- license: mit tags: - nowcasting - forecasting - timeseries - remote-sensing - gan --- # DGMR ## Model description [More information needed] ## Intended uses & limitations [More information needed] ## How to use [More information needed] ## Limitations and bias [More information needed] ## Training data [More information needed] ## Training procedure [More information needed] ## Evaluation results [More information needed]
Hausax/albert-xxlarge-v2-finetuned-Poems	Hausax	2022-06-20T07:19:43Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "albert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-19T10:02:39Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: albert-xxlarge-v2-finetuned-Poems 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. --> # albert-xxlarge-v2-finetuned-Poems This model is a fine-tuned version of [albert-xxlarge-v2](https://huggingface.co/albert-xxlarge-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1923 ## 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-07 - 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.482 \| 1.0 \| 19375 \| 2.2959 \| \| 2.258 \| 2.0 \| 38750 \| 2.2357 \| \| 2.2146 \| 3.0 \| 58125 \| 2.2085 \| \| 2.1975 \| 4.0 \| 77500 \| 2.1929 \| \| 2.1893 \| 5.0 \| 96875 \| 2.1863 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
KM4STfulltext/CSSCI_ABS_roberta_wwm	KM4STfulltext	2022-06-20T07:06:48Z	4	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-15T15:33:54Z	--- license: apache-2.0 --- # Pre-trained Language Model for the Humanities and Social Sciences in Chinese ## Introduction The research for social science texts in Chinese needs the support natural language processing tools. The pre-trained language model has greatly improved the accuracy of text mining in general texts. At present, there is an urgent need for a pre-trained language model specifically for the automatic processing of scientific texts in Chinese social science. We used the abstract of social science research as the training set. Based on the deep language model framework of BERT, we constructed CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm pre-training language models by [transformers/run_mlm.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py) and [transformers/mlm_wwm](https://github.com/huggingface/transformers/tree/main/examples/research_projects/mlm_wwm). We designed four downstream tasks of Text Classification on different Chinese social scientific article corpus to verify the performance of the model. - CSSCI_ABS_BERT , CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm are trained on the abstract of articles published in CSSCI journals. The training set involved in the experiment included a total of `510,956,094 words`. - Based on the idea of Domain-Adaptive Pretraining, `CSSCI_ABS_BERT` and `CSSCI_ABS_roberta` combine a large amount of abstracts of scientific articles in Chinese based on the BERT structure, and continue to train the BERT and Chinese-RoBERTa models respectively to obtain pre-training models for the automatic processing of Chinese Social science research texts. ## News - 2022-06-15 : CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm has been put forward for the first time. ## How to use ### Huggingface Transformers The `from_pretrained` method based on [Huggingface Transformers](https://github.com/huggingface/transformers) can directly obtain CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm models online. - CSSCI_ABS_BERT ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_BERT") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_BERT") ``` - CSSCI_ABS_roberta ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta") ``` - CSSCI_ABS_roberta-wwm ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta_wwm") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta_wwm") ``` ### Download Models - The version of the model we provide is `PyTorch`. ### From Huggingface - Download directly through Huggingface's official website. - [KM4STfulltext/CSSCI_ABS_BERT](https://huggingface.co/KM4STfulltext/CSSCI_ABS_BERT) - [KM4STfulltext/CSSCI_ABS_roberta](https://huggingface.co/KM4STfulltext/CSSCI_ABS_roberta) - [KM4STfulltext/CSSCI_ABS_roberta_wwm](https://huggingface.co/KM4STfulltext/CSSCI_ABS_roberta_wwm) ## Evaluation & Results - We useCSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm to perform Text Classificationon different social science research corpus. The experimental results are as follows. #### Discipline classification experiments of articles published in CSSCI journals https://github.com/S-T-Full-Text-Knowledge-Mining/CSSCI-BERT #### Movement recognition experiments for data analysis and knowledge discovery abstract \| Tag \| bert-base-Chinese \| chinese-roberta-wwm,ext \| CSSCI_ABS_BERT \| CSSCI_ABS_roberta \| CSSCI_ABS_roberta_wwm \| support \| \| ------------ \| ----------------- \| ----------------------- \| -------------- \| ----------------- \| --------------------- \| ------- \| \| Abstract \| 55.23 \| 62.44 \| 56.8 \| 57.96 \| 58.26 \| 223 \| \| Location \| 61.61 \| 54.38 \| 61.83 \| 61.4 \| 61.94 \| 2866 \| \| Metric \| 45.08 \| 41 \| 45.27 \| 46.74 \| 47.13 \| 622 \| \| Organization \| 46.85 \| 35.29 \| 45.72 \| 45.44 \| 44.65 \| 327 \| \| Person \| 88.66 \| 82.79 \| 88.21 \| 88.29 \| 88.51 \| 4850 \| \| Thing \| 71.68 \| 65.34 \| 71.88 \| 71.68 \| 71.81 \| 5993 \| \| Time \| 65.35 \| 60.38 \| 64.15 \| 65.26 \| 66.03 \| 1272 \| \| avg \| 72.69 \| 66.62 \| 72.59 \| 72.61 \| 72.89 \| 16153 \| #### Chinese literary entity recognition \| Tag \| bert-base-Chinese \| chinese-roberta-wwm,ext \| CSSCI_ABS_BERT \| CSSCI_ABS_roberta \| CSSCI_ABS_roberta_wwm \| support \| \| ------------ \| ----------------- \| ----------------------- \| -------------- \| ----------------- \| --------------------- \| ------- \| \| Abstract \| 55.23 \| 62.44 \| 56.8 \| 57.96 \| 58.26 \| 223 \| \| Location \| 61.61 \| 54.38 \| 61.83 \| 61.4 \| 61.94 \| 2866 \| \| Metric \| 45.08 \| 41 \| 45.27 \| 46.74 \| 47.13 \| 622 \| \| Organization \| 46.85 \| 35.29 \| 45.72 \| 45.44 \| 44.65 \| 327 \| \| Person \| 88.66 \| 82.79 \| 88.21 \| 88.29 \| 88.51 \| 4850 \| \| Thing \| 71.68 \| 65.34 \| 71.88 \| 71.68 \| 71.81 \| 5993 \| \| Time \| 65.35 \| 60.38 \| 64.15 \| 65.26 \| 66.03 \| 1272 \| \| avg \| 72.69 \| 66.62 \| 72.59 \| 72.61 \| 72.89 \| 16153 \| ## Cited - If our content is helpful for your research work, please quote our research in your article. - If you want to quote our research, you can use this url [S-T-Full-Text-Knowledge-Mining/CSSCI-BERT (github.com)](https://github.com/S-T-Full-Text-Knowledge-Mining/CSSCI-BERT) as an alternative before our paper is published. ## Disclaimer - The experimental results presented in the report only show the performance under a specific data set and hyperparameter combination, and cannot represent the essence of each model. The experimental results may change due to random number seeds and computing equipment. - Users can use the model arbitrarily within the scope of the license, but we are not responsible for the direct or indirect losses caused by using the content of the project. ## Acknowledgment - CSSCI_ABS_BERT was trained based on [BERT-Base-Chinese]([google-research/bert: TensorFlow code and pre-trained models for BERT (github.com)](https://github.com/google-research/bert)). - CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm was trained based on [RoBERTa-wwm-ext, Chinese]([ymcui/Chinese-BERT-wwm: Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型） (github.com)](https://github.com/ymcui/Chinese-BERT-wwm)).
anas-awadalla/prompt-tuned-t5-small-num-tokens-100-squad	anas-awadalla	2022-06-20T04:47:43Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2022-06-20T00:50:38Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: prompt-tuned-t5-small-num-tokens-100-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. --> # prompt-tuned-t5-small-num-tokens-100-squad This model is a fine-tuned version of [google/t5-small-lm-adapt](https://huggingface.co/google/t5-small-lm-adapt) 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: 0.3 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 30000 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
huggingtweets/bartoszmilewski	huggingtweets	2022-06-20T02:35:22Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-20T02:33:39Z	--- language: en thumbnail: http://www.huggingtweets.com/bartoszmilewski/1655692518288/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/1000136690/IslandBartosz_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Bartosz Milewski</div> <div style="text-align: center; font-size: 14px;">@bartoszmilewski</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 Bartosz Milewski. \| Data \| Bartosz Milewski \| \| --- \| --- \| \| Tweets downloaded \| 3248 \| \| Retweets \| 79 \| \| Short tweets \| 778 \| \| Tweets kept \| 2391 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2689vaqz/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 @bartoszmilewski's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1f1jpc3z) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1f1jpc3z/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/bartoszmilewski') 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)
ali2066/DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_french_second_train_set_french_False	ali2066	2022-06-20T01:54:34Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-05-02T14:07:53Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: _ctxSentence_TRAIN_all_TEST_french_second_train_set_french_False 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. --> # _ctxSentence_TRAIN_all_TEST_french_second_train_set_french_False This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4936 - Precision: 0.8189 - Recall: 0.9811 - F1: 0.8927 - Accuracy: 0.8120 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 13 \| 0.5150 \| 0.7447 \| 1.0 \| 0.8537 \| 0.7447 \| \| No log \| 2.0 \| 26 \| 0.5565 \| 0.7447 \| 1.0 \| 0.8537 \| 0.7447 \| \| No log \| 3.0 \| 39 \| 0.5438 \| 0.7778 \| 1.0 \| 0.8750 \| 0.7872 \| \| No log \| 4.0 \| 52 \| 0.5495 \| 0.7778 \| 1.0 \| 0.8750 \| 0.7872 \| \| No log \| 5.0 \| 65 \| 0.5936 \| 0.7778 \| 1.0 \| 0.8750 \| 0.7872 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
huggingtweets/borisjohnson-elonmusk-majornelson	huggingtweets	2022-06-19T22:42:51Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-19T22:42:06Z	--- language: en thumbnail: http://www.huggingtweets.com/borisjohnson-elonmusk-majornelson/1655678567047/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/1529956155937759233/Nyn1HZWF_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/1519703427240013824/FOED2v9N_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/1500170386520129536/Rr2G6A-N_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 & Larry Hryb 🇺🇦 & Boris Johnson</div> <div style="text-align: center; font-size: 14px;">@borisjohnson-elonmusk-majornelson</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 & Larry Hryb 🇺🇦 & Boris Johnson. \| Data \| Elon Musk \| Larry Hryb 🇺🇦 \| Boris Johnson \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3250 \| 3250 \| 3248 \| \| Retweets \| 147 \| 736 \| 653 \| \| Short tweets \| 985 \| 86 \| 17 \| \| Tweets kept \| 2118 \| 2428 \| 2578 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/22m356ew/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 @borisjohnson-elonmusk-majornelson's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/316f3w9h) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/316f3w9h/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/borisjohnson-elonmusk-majornelson') 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)
sevlabr/unit-1-PPO-LunarLander-v2	sevlabr	2022-06-19T21:52:28Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-19T21:51:58Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 222.00 +/- 55.66 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 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 ... ```
chradden/generation_xyz	chradden	2022-06-19T21:33:52Z	54	1	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-06-19T21:33:37Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: generation_xyz results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.5504587292671204 --- # generation_xyz Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### Baby Boomers ![Baby Boomers](images/Baby_Boomers.jpg) #### Generation Alpha ![Generation Alpha](images/Generation_Alpha.jpg) #### Generation X ![Generation X](images/Generation_X.jpg) #### Generation Z ![Generation Z](images/Generation_Z.jpg) #### Millennials ![Millennials](images/Millennials.jpg)
voleg44/dqn-SpaceInvadersNoFrameskip-v4	voleg44	2022-06-19T20:06:30Z	3	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-19T20:05:54Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 434.50 +/- 143.59 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga voleg44 -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga voleg44 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
anas-awadalla/prophetnet-large-squad	anas-awadalla	2022-06-19T19:16:54Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "prophetnet", "text2text-generation", "generated_from_trainer", "dataset:squad", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-19T18:12:00Z	--- tags: - generated_from_trainer datasets: - squad model-index: - name: prophetnet-large-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. --> # prophetnet-large-squad This model is a fine-tuned version of [microsoft/prophetnet-large-uncased](https://huggingface.co/microsoft/prophetnet-large-uncased) 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: 3e-05 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 256 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6
martin-ha/text_encoder_in_dual	martin-ha	2022-06-19T19:11:19Z	0	0	keras	[ "keras", "tf-keras", "region:us" ]	null	2022-06-19T19:10:58Z	--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training Metrics Model history needed ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
martin-ha/vision_encoder_in_dual	martin-ha	2022-06-19T19:07:22Z	0	0	keras	[ "keras", "tf-keras", "region:us" ]	null	2022-06-19T19:06:52Z	--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training Metrics Model history needed ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
diversifix/diversiformer	diversifix	2022-06-19T16:44:04Z	6	3	transformers	[ "transformers", "tf", "t5", "text2text-generation", "de", "arxiv:2010.11934", "license:gpl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-19T12:44:02Z	--- language: - de license: gpl widget: - text: "Ersetze \"Lehrer\" durch \"Lehrerin oder Lehrer\": Ein promovierter Mathelehrer ist noch nie im Unterricht eingeschlafen." example_title: "Example 1" - text: "Ersetze \"Student\" durch \"studierende Person\": Maria ist kein Student." example_title: "Example 2" inference: parameters: max_length: 500 --- # Diversiformer 🤗 🏳️‍🌈 🇩🇪 _Work in progress._ Language model for inclusive language in German, fine-tuned on [mT5](https://arxiv.org/abs/2010.11934). An experimental model version is released [on Huggingface](https://huggingface.co/diversifix/diversiformer). Source code for fine-tuning is available [on GitHub](https://github.com/diversifix/diversiformer). ## Tasks - DETECT: Recognizes instances of the generic masculine, and of other exclusive language. To do. - SUGGEST: Suggest inclusive alternatives to masculine and exclusive words. To do. - REPLACE: Replace one phrase by another, while preserving grammatical coherence. Work in progress. - ▶️ `Ersetze "Schüler" durch "Schülerin oder Schüler": Die Schüler kamen zu spät.` ◀️ `Die Schülerinnen und Schüler kamen zu spät.` - ▶️ `Ersetze "Lehrer" durch "Kollegium": Die wartenden Lehrer wunderten sich.` ◀️ `Das wartende Kollegium wunderte sich.` ## Usage ```python >>> from transformers import pipeline >>> generator = pipeline("text2text-generation", model="diversifix/diversiformer") >>> generator('Ersetze "Schüler" durch "Schülerin oder Schüler": Die Schüler kamen zu spät.', max_length=500) ``` ## License Diversiformer. Transformer model for inclusive language. Copyright (C) 2022 [Diversifix e. V.](mailto:vorstand@diversifix.org) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.
anjankumar/mbart-large-50-finetuned-en-to-te	anjankumar	2022-06-19T16:32:07Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "generated_from_trainer", "dataset:kde4", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-07T07:02:05Z	--- tags: - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: mbart-large-50-finetuned-en-to-te results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 args: en-te metrics: - name: Bleu type: bleu value: 0.7152 --- <!-- 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. --> # mbart-large-50-finetuned-en-to-te This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 13.8521 - Bleu: 0.7152 - Gen Len: 20.5 ## 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: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 7 \| 13.8521 \| 0.7152 \| 20.5 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
thaidv96/lead-reliability-scoring	thaidv96	2022-06-19T16:15:46Z	6	1	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-19T15:44:25Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: lead-reliability-scoring 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. --> # lead-reliability-scoring This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0123 - F1: 0.9937 ## 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 50 \| 0.3866 \| 0.5761 \| \| No log \| 2.0 \| 100 \| 0.3352 \| 0.6538 \| \| No log \| 3.0 \| 150 \| 0.1786 \| 0.8283 \| \| No log \| 4.0 \| 200 \| 0.1862 \| 0.8345 \| \| No log \| 5.0 \| 250 \| 0.1367 \| 0.8736 \| \| No log \| 6.0 \| 300 \| 0.0642 \| 0.9477 \| \| No log \| 7.0 \| 350 \| 0.0343 \| 0.9748 \| \| No log \| 8.0 \| 400 \| 0.0190 \| 0.9874 \| \| No log \| 9.0 \| 450 \| 0.0123 \| 0.9937 \| \| 0.2051 \| 10.0 \| 500 \| 0.0058 \| 0.9937 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
waboucay/camembert-large-xnli	waboucay	2022-06-19T14:38:51Z	6	0	transformers	[ "transformers", "pytorch", "camembert", "text-classification", "nli", "fr", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-19T14:35:57Z	--- language: - fr tags: - nli metrics: - f1 --- ## Eval results We obtain the following results on ```validation``` and ```test``` sets: \| Set \| F1<sub>micro</sub> \| F1<sub>macro</sub> \| \|------------\|--------------------\|--------------------\| \| validation \| 85.8 \| 85.9 \| \| test \| 84.2 \| 84.3 \|
waboucay/camembert-large-finetuned-rua_wl_3_classes	waboucay	2022-06-19T14:35:04Z	5	0	transformers	[ "transformers", "pytorch", "camembert", "text-classification", "nli", "fr", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-19T14:31:32Z	--- language: - fr tags: - nli metrics: - f1 --- ## Eval results We obtain the following results on ```validation``` and ```test``` sets: \| Set \| F1<sub>micro</sub> \| F1<sub>macro</sub> \| \|------------\|--------------------\|--------------------\| \| validation \| 75.3 \| 74.9 \| \| test \| 75.8 \| 75.3 \|
waboucay/camembert-large-finetuned-repnum_wl_3_classes	waboucay	2022-06-19T14:30:19Z	5	0	transformers	[ "transformers", "pytorch", "camembert", "text-classification", "nli", "fr", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-19T14:22:13Z	--- language: - fr tags: - nli metrics: - f1 --- ## Eval results We obtain the following results on ```validation``` and ```test``` sets: \| Set \| F1<sub>micro</sub> \| F1<sub>macro</sub> \| \|------------\|--------------------\|--------------------\| \| validation \| 79.4 \| 79.4 \| \| test \| 80.6 \| 80.6 \|
ctoraman/RoBERTweetTurkCovid	ctoraman	2022-06-19T14:25:58Z	5	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "tr", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-08T11:59:09Z	--- language: - tr tags: - roberta license: cc-by-nc-sa-4.0 --- # RoBERTweetTurkCovid (uncased) Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. The pretrained corpus is a Turkish tweets collection related to COVID-19. Model architecture is similar to RoBERTa-base (12 layers, 12 heads, and 768 hidden size). Tokenization algorithm is WordPiece. Vocabulary size is 30k. The details of pretraining can be found at this paper: ```bibtex @InProceedings{clef-checkthat:2022:task1:oguzhan, author = {Cagri Toraman and Oguzhan Ozcelik and Furkan Şahinuç and Umitcan Sahin}, title = "{ARC-NLP at CheckThat! 2022:} Contradiction for Harmful Tweet Detection", year = {2022}, booktitle = "Working Notes of {CLEF} 2022 - Conference and Labs of the Evaluation Forum", editor = {Faggioli, Guglielmo andd Ferro, Nicola and Hanbury, Allan and Potthast, Martin}, series = {CLEF~'2022}, address = {Bologna, Italy}, } ``` The following code can be used for model loading and tokenization, example max length (768) can be changed: ``` model = AutoModel.from_pretrained([model_path]) #for sequence classification: #model = AutoModelForSequenceClassification.from_pretrained([model_path], num_labels=[num_classes]) tokenizer = PreTrainedTokenizerFast(tokenizer_file=[file_path]) tokenizer.mask_token = "[MASK]" tokenizer.cls_token = "[CLS]" tokenizer.sep_token = "[SEP]" tokenizer.pad_token = "[PAD]" tokenizer.unk_token = "[UNK]" tokenizer.bos_token = "[CLS]" tokenizer.eos_token = "[SEP]" tokenizer.model_max_length = 768 ``` ### BibTeX entry and citation info ```bibtex @InProceedings{clef-checkthat:2022:task1:oguzhan, author = {Cagri Toraman and Oguzhan Ozcelik and Furkan Şahinuç and Umitcan Sahin}, title = "{ARC-NLP at CheckThat! 2022:} Contradiction for Harmful Tweet Detection", year = {2022}, booktitle = "Working Notes of {CLEF} 2022 - Conference and Labs of the Evaluation Forum", editor = {Faggioli, Guglielmo andd Ferro, Nicola and Hanbury, Allan and Potthast, Martin}, series = {CLEF~'2022}, address = {Bologna, Italy}, } ```
rajistics/dqn-SpaceInvadersNoFrameskip-v4	rajistics	2022-06-19T13:48:15Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-19T13:47:41Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 435.50 +/- 129.62 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rajistics -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga rajistics ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
Classroom-workshop/assignment2-llama	Classroom-workshop	2022-06-19T13:46:40Z	7	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-02T15:27:20Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 200.68 +/- 7.11 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
huggingtweets/david_lynch	huggingtweets	2022-06-19T13:12:27Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-05-27T11:00:06Z	--- language: en thumbnail: http://www.huggingtweets.com/david_lynch/1655644342827/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/63730229/DL_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">David Lynch</div> <div style="text-align: center; font-size: 14px;">@david_lynch</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 David Lynch. \| Data \| David Lynch \| \| --- \| --- \| \| Tweets downloaded \| 912 \| \| Retweets \| 29 \| \| Short tweets \| 21 \| \| Tweets kept \| 862 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/do5yghsd/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 @david_lynch's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ddgwjhcj) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ddgwjhcj/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/david_lynch') 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)
gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v2	gary109	2022-06-19T12:14:27Z	3	1	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-06-19T00:34:26Z	--- tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer model-index: - name: ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v2 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. --> # ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v2 This model is a fine-tuned version of [gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v1](https://huggingface.co/gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v1) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING dataset. It achieves the following results on the evaluation set: - Loss: 0.4313 - Wer: 0.1645 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.148 \| 1.0 \| 552 \| 0.4313 \| 0.1645 \| \| 0.1301 \| 2.0 \| 1104 \| 0.4365 \| 0.1618 \| \| 0.1237 \| 3.0 \| 1656 \| 0.4470 \| 0.1595 \| \| 0.1063 \| 4.0 \| 2208 \| 0.4593 \| 0.1576 \| \| 0.128 \| 5.0 \| 2760 \| 0.4525 \| 0.1601 \| \| 0.1099 \| 6.0 \| 3312 \| 0.4593 \| 0.1567 \| \| 0.0969 \| 7.0 \| 3864 \| 0.4625 \| 0.1550 \| \| 0.0994 \| 8.0 \| 4416 \| 0.4672 \| 0.1543 \| \| 0.125 \| 9.0 \| 4968 \| 0.4636 \| 0.1544 \| \| 0.0887 \| 10.0 \| 5520 \| 0.4601 \| 0.1538 \| ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1
ShannonAI/ChineseBERT-large	ShannonAI	2022-06-19T12:07:31Z	23	5	transformers	[ "transformers", "pytorch", "arxiv:2106.16038", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	# ChineseBERT-large This repository contains code, model, dataset for ChineseBERT at ACL2021. paper: [ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://arxiv.org/abs/2106.16038) Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu and Jiwei Li code: [ChineseBERT github link](https://github.com/ShannonAI/ChineseBert) ## Model description We propose ChineseBERT, which incorporates both the glyph and pinyin information of Chinese characters into language model pretraining. First, for each Chinese character, we get three kind of embedding. - Char Embedding: the same as origin BERT token embedding. - Glyph Embedding: capture visual features based on different fonts of a Chinese character. - Pinyin Embedding: capture phonetic feature from the pinyin sequence ot a Chinese Character. Then, char embedding, glyph embedding and pinyin embedding are first concatenated, and mapped to a D-dimensional embedding through a fully connected layer to form the fusion embedding. Finally, the fusion embedding is added with the position embedding, which is fed as input to the BERT model. The following image shows an overview architecture of ChineseBERT model. ![MODEL](https://raw.githubusercontent.com/ShannonAI/ChineseBert/main/images/ChineseBERT.png) ChineseBERT leverages the glyph and pinyin information of Chinese characters to enhance the model's ability of capturing context semantics from surface character forms and disambiguating polyphonic characters in Chinese.
dibsondivya/ernie-phmtweets-sutd	dibsondivya	2022-06-19T11:38:29Z	14	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "ernie", "health", "tweet", "dataset:custom-phm-tweets", "arxiv:1802.09130", "arxiv:1907.12412", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-19T11:20:14Z	--- tags: - ernie - health - tweet datasets: - custom-phm-tweets metrics: - accuracy model-index: - name: ernie-phmtweets-sutd results: - task: name: Text Classification type: text-classification dataset: name: custom-phm-tweets type: labelled metrics: - name: Accuracy type: accuracy value: 0.885 --- # ernie-phmtweets-sutd This model is a fine-tuned version of [ernie-2.0-en](https://huggingface.co/nghuyong/ernie-2.0-en) for text classification to identify public health events through tweets. The project was based on an [Emory University Study on Detection of Personal Health Mentions in Social Media paper](https://arxiv.org/pdf/1802.09130v2.pdf), that worked with this [custom dataset](https://github.com/emory-irlab/PHM2017). It achieves the following results on the evaluation set: - Accuracy: 0.885 ## Usage ```Python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dibsondivya/ernie-phmtweets-sutd") model = AutoModelForSequenceClassification.from_pretrained("dibsondivya/ernie-phmtweets-sutd") ``` ### Model Evaluation Results With Validation Set - Accuracy: 0.889763779527559 With Test Set - Accuracy: 0.884643644379133 ## References for ERNIE 2.0 Model ```bibtex @article{sun2019ernie20, title={ERNIE 2.0: A Continual Pre-training Framework for Language Understanding}, author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:1907.12412}, year={2019} } ```
levgil2/stam-finetuned-imdb	levgil2	2022-06-19T11:26:02Z	4	0	transformers	[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-19T11:21:46Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: levgil2/stam-finetuned-imdb 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. --> # levgil2/stam-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8517 - Validation Loss: 2.5705 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -687, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, '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 \| \|:----------:\|:---------------:\|:-----:\| \| 2.8517 \| 2.5705 \| 0 \| ### Framework versions - Transformers 4.20.0 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1
zakria/NLP_Project	zakria	2022-06-19T09:55:56Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-06-19T07:49:04Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: NLP_Project 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. --> # NLP_Project This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5308 - Wer: 0.3428 ## 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: 0.0001 - 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_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 3.5939 \| 1.0 \| 500 \| 2.1356 \| 1.0014 \| \| 0.9126 \| 2.01 \| 1000 \| 0.5469 \| 0.5354 \| \| 0.4491 \| 3.01 \| 1500 \| 0.4636 \| 0.4503 \| \| 0.3008 \| 4.02 \| 2000 \| 0.4269 \| 0.4330 \| \| 0.2229 \| 5.02 \| 2500 \| 0.4164 \| 0.4073 \| \| 0.188 \| 6.02 \| 3000 \| 0.4717 \| 0.4107 \| \| 0.1739 \| 7.03 \| 3500 \| 0.4306 \| 0.4031 \| \| 0.159 \| 8.03 \| 4000 \| 0.4394 \| 0.3993 \| \| 0.1342 \| 9.04 \| 4500 \| 0.4462 \| 0.3904 \| \| 0.1093 \| 10.04 \| 5000 \| 0.4387 \| 0.3759 \| \| 0.1005 \| 11.04 \| 5500 \| 0.5033 \| 0.3847 \| \| 0.0857 \| 12.05 \| 6000 \| 0.4805 \| 0.3876 \| \| 0.0779 \| 13.05 \| 6500 \| 0.5269 \| 0.3810 \| \| 0.072 \| 14.06 \| 7000 \| 0.5109 \| 0.3710 \| \| 0.0641 \| 15.06 \| 7500 \| 0.4865 \| 0.3638 \| \| 0.0584 \| 16.06 \| 8000 \| 0.5041 \| 0.3646 \| \| 0.0552 \| 17.07 \| 8500 \| 0.4987 \| 0.3537 \| \| 0.0535 \| 18.07 \| 9000 \| 0.4947 \| 0.3586 \| \| 0.0475 \| 19.08 \| 9500 \| 0.5237 \| 0.3647 \| \| 0.042 \| 20.08 \| 10000 \| 0.5338 \| 0.3561 \| \| 0.0416 \| 21.08 \| 10500 \| 0.5068 \| 0.3483 \| \| 0.0358 \| 22.09 \| 11000 \| 0.5126 \| 0.3532 \| \| 0.0334 \| 23.09 \| 11500 \| 0.5213 \| 0.3536 \| \| 0.0331 \| 24.1 \| 12000 \| 0.5378 \| 0.3496 \| \| 0.03 \| 25.1 \| 12500 \| 0.5167 \| 0.3470 \| \| 0.0254 \| 26.1 \| 13000 \| 0.5245 \| 0.3418 \| \| 0.0233 \| 27.11 \| 13500 \| 0.5393 \| 0.3456 \| \| 0.0232 \| 28.11 \| 14000 \| 0.5279 \| 0.3425 \| \| 0.022 \| 29.12 \| 14500 \| 0.5308 \| 0.3428 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1
sun1638650145/q-Taxi-v3	sun1638650145	2022-06-19T09:00:38Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-06-19T09:00:26Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.54 +/- 2.73 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # 使用Q-Learning智能体来玩Taxi-v3 这是一个使用Q-Learning训练有素的模型玩Taxi-v3. ## 用法 ```python model = load_from_hub(repo_id='sun1638650145/q-Taxi-v3', filename='q-learning.pkl') # 不要忘记检查是否需要添加额外的参数(例如is_slippery=False) env = gym.make(model['env_id']) evaluate_agent(env, model['max_steps'], model['n_eval_episodes'], model['qtable'], model['eval_seed']) ```
ShannonAI/ChineseBERT-base	ShannonAI	2022-06-19T08:14:46Z	109	20	transformers	[ "transformers", "pytorch", "arxiv:2106.16038", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	# ChineseBERT-base This repository contains code, model, dataset for ChineseBERT at ACL2021. paper: [ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://arxiv.org/abs/2106.16038) Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu and Jiwei Li code: [ChineseBERT github link](https://github.com/ShannonAI/ChineseBert) ## Model description We propose ChineseBERT, which incorporates both the glyph and pinyin information of Chinese characters into language model pretraining. First, for each Chinese character, we get three kind of embedding. - Char Embedding: the same as origin BERT token embedding. - Glyph Embedding: capture visual features based on different fonts of a Chinese character. - Pinyin Embedding: capture phonetic feature from the pinyin sequence ot a Chinese Character. Then, char embedding, glyph embedding and pinyin embedding are first concatenated, and mapped to a D-dimensional embedding through a fully connected layer to form the fusion embedding. Finally, the fusion embedding is added with the position embedding, which is fed as input to the BERT model. The following image shows an overview architecture of ChineseBERT model. ![MODEL](https://raw.githubusercontent.com/ShannonAI/ChineseBert/main/images/ChineseBERT.png) ChineseBERT leverages the glyph and pinyin information of Chinese characters to enhance the model's ability of capturing context semantics from surface character forms and disambiguating polyphonic characters in Chinese.
botika/checkpoint-124500-finetuned-squad	botika	2022-06-19T05:53:11Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2022-06-17T07:41:58Z	--- tags: - generated_from_trainer model-index: - name: checkpoint-124500-finetuned-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. --> # checkpoint-124500-finetuned-squad This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 14.9594 ## 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: 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: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 3.9975 \| 1.0 \| 3289 \| 3.8405 \| \| 3.7311 \| 2.0 \| 6578 \| 3.7114 \| \| 3.5681 \| 3.0 \| 9867 \| 3.6829 \| \| 3.4101 \| 4.0 \| 13156 \| 3.6368 \| \| 3.2487 \| 5.0 \| 16445 \| 3.6526 \| \| 3.1143 \| 6.0 \| 19734 \| 3.7567 \| \| 2.9783 \| 7.0 \| 23023 \| 3.8469 \| \| 2.8295 \| 8.0 \| 26312 \| 4.0040 \| \| 2.6912 \| 9.0 \| 29601 \| 4.1996 \| \| 2.5424 \| 10.0 \| 32890 \| 4.3387 \| \| 2.4161 \| 11.0 \| 36179 \| 4.4988 \| \| 2.2713 \| 12.0 \| 39468 \| 4.7861 \| \| 2.1413 \| 13.0 \| 42757 \| 4.9276 \| \| 2.0125 \| 14.0 \| 46046 \| 5.0598 \| \| 1.8798 \| 15.0 \| 49335 \| 5.3347 \| \| 1.726 \| 16.0 \| 52624 \| 5.5869 \| \| 1.5994 \| 17.0 \| 55913 \| 5.7161 \| \| 1.4643 \| 18.0 \| 59202 \| 6.0174 \| \| 1.3237 \| 19.0 \| 62491 \| 6.4926 \| \| 1.2155 \| 20.0 \| 65780 \| 6.4882 \| \| 1.1029 \| 21.0 \| 69069 \| 6.9922 \| \| 0.9948 \| 22.0 \| 72358 \| 7.1357 \| \| 0.9038 \| 23.0 \| 75647 \| 7.3676 \| \| 0.8099 \| 24.0 \| 78936 \| 7.4180 \| \| 0.7254 \| 25.0 \| 82225 \| 7.7753 \| \| 0.6598 \| 26.0 \| 85514 \| 7.8643 \| \| 0.5723 \| 27.0 \| 88803 \| 8.1798 \| \| 0.5337 \| 28.0 \| 92092 \| 8.3053 \| \| 0.4643 \| 29.0 \| 95381 \| 8.8597 \| \| 0.4241 \| 30.0 \| 98670 \| 8.9849 \| \| 0.3763 \| 31.0 \| 101959 \| 8.8406 \| \| 0.3479 \| 32.0 \| 105248 \| 9.1517 \| \| 0.3271 \| 33.0 \| 108537 \| 9.3659 \| \| 0.2911 \| 34.0 \| 111826 \| 9.4813 \| \| 0.2836 \| 35.0 \| 115115 \| 9.5746 \| \| 0.2528 \| 36.0 \| 118404 \| 9.7027 \| \| 0.2345 \| 37.0 \| 121693 \| 9.7515 \| \| 0.2184 \| 38.0 \| 124982 \| 9.9729 \| \| 0.2067 \| 39.0 \| 128271 \| 10.0828 \| \| 0.2077 \| 40.0 \| 131560 \| 10.0878 \| \| 0.1876 \| 41.0 \| 134849 \| 10.2974 \| \| 0.1719 \| 42.0 \| 138138 \| 10.2712 \| \| 0.1637 \| 43.0 \| 141427 \| 10.5788 \| \| 0.1482 \| 44.0 \| 144716 \| 10.7465 \| \| 0.1509 \| 45.0 \| 148005 \| 10.4603 \| \| 0.1358 \| 46.0 \| 151294 \| 10.7665 \| \| 0.1316 \| 47.0 \| 154583 \| 10.7724 \| \| 0.1223 \| 48.0 \| 157872 \| 11.1766 \| \| 0.1205 \| 49.0 \| 161161 \| 11.1870 \| \| 0.1203 \| 50.0 \| 164450 \| 11.1053 \| \| 0.1081 \| 51.0 \| 167739 \| 10.9696 \| \| 0.103 \| 52.0 \| 171028 \| 11.2010 \| \| 0.0938 \| 53.0 \| 174317 \| 11.6728 \| \| 0.0924 \| 54.0 \| 177606 \| 11.1423 \| \| 0.0922 \| 55.0 \| 180895 \| 11.7409 \| \| 0.0827 \| 56.0 \| 184184 \| 11.7850 \| \| 0.0829 \| 57.0 \| 187473 \| 11.8956 \| \| 0.073 \| 58.0 \| 190762 \| 11.8915 \| \| 0.0788 \| 59.0 \| 194051 \| 12.1617 \| \| 0.0734 \| 60.0 \| 197340 \| 12.2007 \| \| 0.0729 \| 61.0 \| 200629 \| 12.2388 \| \| 0.0663 \| 62.0 \| 203918 \| 12.2471 \| \| 0.0662 \| 63.0 \| 207207 \| 12.5830 \| \| 0.064 \| 64.0 \| 210496 \| 12.6105 \| \| 0.0599 \| 65.0 \| 213785 \| 12.3712 \| \| 0.0604 \| 66.0 \| 217074 \| 12.9249 \| \| 0.0574 \| 67.0 \| 220363 \| 12.7309 \| \| 0.0538 \| 68.0 \| 223652 \| 12.8068 \| \| 0.0526 \| 69.0 \| 226941 \| 13.4368 \| \| 0.0471 \| 70.0 \| 230230 \| 13.5148 \| \| 0.0436 \| 71.0 \| 233519 \| 13.3391 \| \| 0.0448 \| 72.0 \| 236808 \| 13.4100 \| \| 0.0428 \| 73.0 \| 240097 \| 13.5617 \| \| 0.0401 \| 74.0 \| 243386 \| 13.8674 \| \| 0.035 \| 75.0 \| 246675 \| 13.5746 \| \| 0.0342 \| 76.0 \| 249964 \| 13.5042 \| \| 0.0344 \| 77.0 \| 253253 \| 14.2085 \| \| 0.0365 \| 78.0 \| 256542 \| 13.6393 \| \| 0.0306 \| 79.0 \| 259831 \| 13.9807 \| \| 0.0311 \| 80.0 \| 263120 \| 13.9768 \| \| 0.0353 \| 81.0 \| 266409 \| 14.5245 \| \| 0.0299 \| 82.0 \| 269698 \| 13.9471 \| \| 0.0263 \| 83.0 \| 272987 \| 13.7899 \| \| 0.0254 \| 84.0 \| 276276 \| 14.3786 \| \| 0.0267 \| 85.0 \| 279565 \| 14.5611 \| \| 0.022 \| 86.0 \| 282854 \| 14.2658 \| \| 0.0198 \| 87.0 \| 286143 \| 14.9215 \| \| 0.0193 \| 88.0 \| 289432 \| 14.5650 \| \| 0.0228 \| 89.0 \| 292721 \| 14.7014 \| \| 0.0184 \| 90.0 \| 296010 \| 14.6946 \| \| 0.0182 \| 91.0 \| 299299 \| 14.6614 \| \| 0.0188 \| 92.0 \| 302588 \| 14.6915 \| \| 0.0196 \| 93.0 \| 305877 \| 14.7262 \| \| 0.0138 \| 94.0 \| 309166 \| 14.7625 \| \| 0.0201 \| 95.0 \| 312455 \| 15.0442 \| \| 0.0189 \| 96.0 \| 315744 \| 14.8832 \| \| 0.0148 \| 97.0 \| 319033 \| 14.8995 \| \| 0.0129 \| 98.0 \| 322322 \| 14.8974 \| \| 0.0132 \| 99.0 \| 325611 \| 14.9813 \| \| 0.0139 \| 100.0 \| 328900 \| 14.9594 \| ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu102 - Datasets 2.2.2 - Tokenizers 0.12.1
eslamxm/AraT5-base-title-generation-finetune-ar-xlsum	eslamxm	2022-06-19T05:23:32Z	28	1	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "summarization", "Arat5-base", "abstractive summarization", "ar", "xlsum", "generated_from_trainer", "dataset:xlsum", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	summarization	2022-06-18T19:19:57Z	--- tags: - summarization - Arat5-base - abstractive summarization - ar - xlsum - generated_from_trainer datasets: - xlsum model-index: - name: AraT5-base-title-generation-finetune-ar-xlsum 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. --> # AraT5-base-title-generation-finetune-ar-xlsum This model is a fine-tuned version of [UBC-NLP/AraT5-base-title-generation](https://huggingface.co/UBC-NLP/AraT5-base-title-generation) on the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 4.2837 - Rouge-1: 32.46 - Rouge-2: 15.15 - Rouge-l: 28.38 - Gen Len: 18.48 - Bertscore: 74.24 ## 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: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 10 - label_smoothing_factor: 0.1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge-1 \| Rouge-2 \| Rouge-l \| Gen Len \| Bertscore \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:-------:\|:---------:\| \| 5.815 \| 1.0 \| 293 \| 4.7437 \| 27.05 \| 10.49 \| 23.56 \| 18.03 \| 72.56 \| \| 5.0818 \| 2.0 \| 586 \| 4.5004 \| 28.92 \| 11.97 \| 25.09 \| 18.61 \| 73.08 \| \| 4.7855 \| 3.0 \| 879 \| 4.3910 \| 29.66 \| 12.57 \| 25.79 \| 18.58 \| 73.3 \| \| 4.588 \| 4.0 \| 1172 \| 4.3469 \| 30.22 \| 13.05 \| 26.36 \| 18.59 \| 73.61 \| \| 4.4388 \| 5.0 \| 1465 \| 4.3226 \| 30.88 \| 13.81 \| 27.01 \| 18.65 \| 73.78 \| \| 4.3162 \| 6.0 \| 1758 \| 4.2990 \| 30.9 \| 13.6 \| 26.92 \| 18.68 \| 73.78 \| \| 4.2178 \| 7.0 \| 2051 \| 4.2869 \| 31.35 \| 14.01 \| 27.41 \| 18.57 \| 73.96 \| \| 4.1387 \| 8.0 \| 2344 \| 4.2794 \| 31.28 \| 13.98 \| 27.34 \| 18.6 \| 73.87 \| \| 4.0787 \| 9.0 \| 2637 \| 4.2806 \| 31.45 \| 14.17 \| 27.46 \| 18.66 \| 73.97 \| \| 4.0371 \| 10.0 \| 2930 \| 4.2837 \| 31.55 \| 14.19 \| 27.52 \| 18.65 \| 74.0 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
Klinsc/q-FrozenLake-v1-4x4-noSlippery	Klinsc	2022-06-19T04:46:57Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-06-19T04:43:46Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # Q-Learning Agent playing FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Klinsc/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
Tstarshak/q-FrozenLake-v1-4x4-noSlippery	Tstarshak	2022-06-19T04:15:20Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-06-19T04:15:13Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # Q-Learning Agent playing FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Tstarshak/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
huggingtweets/shxtou	huggingtweets	2022-06-19T03:58:13Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-19T03:56:41Z	--- language: en thumbnail: http://www.huggingtweets.com/shxtou/1655611088443/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/1419320614205198350/gHkqH6YI_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Shoto 🗡️</div> <div style="text-align: center; font-size: 14px;">@shxtou</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 Shoto 🗡️. \| Data \| Shoto 🗡️ \| \| --- \| --- \| \| Tweets downloaded \| 3248 \| \| Retweets \| 617 \| \| Short tweets \| 533 \| \| Tweets kept \| 2098 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2mdmjop6/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 @shxtou's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1pdig81x) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1pdig81x/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/shxtou') 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)
huggingtweets/mysta_rias	huggingtweets	2022-06-19T03:40:55Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-19T03:05:09Z	--- language: en thumbnail: http://www.huggingtweets.com/mysta_rias/1655610050415/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/1533221230102433792/Dz_O5gZ7_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Mysta Rias 🕵️‍♂️🦊 NIJISANJI EN</div> <div style="text-align: center; font-size: 14px;">@mysta_rias</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 Mysta Rias 🕵️‍♂️🦊 NIJISANJI EN. \| Data \| Mysta Rias 🕵️‍♂️🦊 NIJISANJI EN \| \| --- \| --- \| \| Tweets downloaded \| 3245 \| \| Retweets \| 296 \| \| Short tweets \| 1005 \| \| Tweets kept \| 1944 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3r8af65s/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 @mysta_rias's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/zqhadryd) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/zqhadryd/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/mysta_rias') 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)
nicolasfeyer/t5-small-finetuned-la-to-en	nicolasfeyer	2022-06-19T02:21:23Z	3	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	2022-06-18T23:08:33Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: t5-small-finetuned-la-to-en 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-finetuned-la-to-en 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: 2.2297 - Bleu: 5.8915 - Gen Len: 16.2252 ## 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 \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:-------:\| \| 3.0883 \| 1.0 \| 4384 \| 2.7499 \| 2.8172 \| 16.4068 \| \| 2.8854 \| 2.0 \| 8768 \| 2.5664 \| 3.8141 \| 16.4581 \| \| 2.746 \| 3.0 \| 13152 \| 2.4524 \| 4.3903 \| 16.3977 \| \| 2.6617 \| 4.0 \| 17536 \| 2.3761 \| 4.7858 \| 16.3473 \| \| 2.6185 \| 5.0 \| 21920 \| 2.3205 \| 5.2502 \| 16.3161 \| \| 2.573 \| 6.0 \| 26304 \| 2.2763 \| 5.4374 \| 16.2916 \| \| 2.5285 \| 7.0 \| 30688 \| 2.2489 \| 5.628 \| 16.2875 \| \| 2.4944 \| 8.0 \| 35072 \| 2.2276 \| 5.7201 \| 16.291 \| \| 2.4749 \| 9.0 \| 39456 \| 2.2164 \| 5.8387 \| 16.2795 \| \| 2.4741 \| 10.0 \| 43840 \| 2.2129 \| 5.8654 \| 16.2789 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
raedinkhaled/swin-tiny-patch4-window7-224-finetuned-mri	raedinkhaled	2022-06-19T00:13:22Z	80	1	transformers	[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-06-18T16:25:22Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-mri results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder args: default metrics: - name: Accuracy type: accuracy value: 0.9806603773584905 --- <!-- 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. --> # swin-tiny-patch4-window7-224-finetuned-mri This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0608 - Accuracy: 0.9807 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0592 \| 1.0 \| 447 \| 0.0823 \| 0.9695 \| \| 0.0196 \| 2.0 \| 894 \| 0.0761 \| 0.9739 \| \| 0.0058 \| 3.0 \| 1341 \| 0.0608 \| 0.9807 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
kjunelee/pegasus-samsum	kjunelee	2022-06-18T22:35:27Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "pegasus", "text2text-generation", "generated_from_trainer", "dataset:samsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-18T08:01:44Z	--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum 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. --> # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0 - Datasets 2.2.3.dev0 - Tokenizers 0.12.1
biu-nlp/superpal	biu-nlp	2022-06-18T22:15:17Z	54	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "arxiv:2009.00590", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- widget: - text: "Prime Minister Hun Sen insisted that talks take place in Cambodia. </s><s> Cambodian leader Hun Sen rejected opposition parties' demands for talks outside the country." --- # SuperPAL model Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline Ori Ernst, Ori Shapira, Ramakanth Pasunuru, Michael Lepioshkin, Jacob Goldberger, Mohit Bansal, Ido Dagan, 2021. [PDF](https://arxiv.org/pdf/2009.00590) How to use? ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("biu-nlp/superpal") model = AutoModelForSequenceClassification.from_pretrained("biu-nlp/superpal") ``` The original repo is [here](https://github.com/oriern/SuperPAL). If you find our work useful, please cite the paper as: ```python @inproceedings{ernst-etal-2021-summary, title = "Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline", author = "Ernst, Ori and Shapira, Ori and Pasunuru, Ramakanth and Lepioshkin, Michael and Goldberger, Jacob and Bansal, Mohit and Dagan, Ido", booktitle = "Proceedings of the 25th Conference on Computational Natural Language Learning", month = nov, year = "2021", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.conll-1.25", pages = "310--322" } ```
BeardedJohn/bert-finetuned-seq-classification-fake-news	BeardedJohn	2022-06-18T21:03:42Z	4	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-17T16:58:33Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: BeardedJohn/bert-finetuned-seq-classification-fake-news 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. --> # BeardedJohn/bert-finetuned-seq-classification-fake-news 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: - Train Loss: 0.0719 - Validation Loss: 0.0214 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 332, '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: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.0719 \| 0.0214 \| 0 \| ### Framework versions - Transformers 4.20.0 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1
zakria/Project_NLP	zakria	2022-06-18T20:44:06Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-06-18T18:43:39Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: Project_NLP 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. --> # Project_NLP This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5324 - Wer: 0.3355 ## 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: 0.0001 - 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_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 3.5697 \| 1.0 \| 500 \| 2.1035 \| 0.9979 \| \| 0.8932 \| 2.01 \| 1000 \| 0.5649 \| 0.5621 \| \| 0.4363 \| 3.01 \| 1500 \| 0.4326 \| 0.4612 \| \| 0.3035 \| 4.02 \| 2000 \| 0.4120 \| 0.4191 \| \| 0.2343 \| 5.02 \| 2500 \| 0.4199 \| 0.3985 \| \| 0.1921 \| 6.02 \| 3000 \| 0.4380 \| 0.4043 \| \| 0.1549 \| 7.03 \| 3500 \| 0.4456 \| 0.3925 \| \| 0.1385 \| 8.03 \| 4000 \| 0.4264 \| 0.3871 \| \| 0.1217 \| 9.04 \| 4500 \| 0.4744 \| 0.3774 \| \| 0.1041 \| 10.04 \| 5000 \| 0.4498 \| 0.3745 \| \| 0.0968 \| 11.04 \| 5500 \| 0.4716 \| 0.3628 \| \| 0.0893 \| 12.05 \| 6000 \| 0.4680 \| 0.3764 \| \| 0.078 \| 13.05 \| 6500 \| 0.5100 \| 0.3623 \| \| 0.0704 \| 14.06 \| 7000 \| 0.4893 \| 0.3552 \| \| 0.0659 \| 15.06 \| 7500 \| 0.4956 \| 0.3565 \| \| 0.0578 \| 16.06 \| 8000 \| 0.5450 \| 0.3595 \| \| 0.0563 \| 17.07 \| 8500 \| 0.4891 \| 0.3614 \| \| 0.0557 \| 18.07 \| 9000 \| 0.5307 \| 0.3548 \| \| 0.0447 \| 19.08 \| 9500 \| 0.4923 \| 0.3493 \| \| 0.0456 \| 20.08 \| 10000 \| 0.5156 \| 0.3479 \| \| 0.0407 \| 21.08 \| 10500 \| 0.4979 \| 0.3389 \| \| 0.0354 \| 22.09 \| 11000 \| 0.5549 \| 0.3462 \| \| 0.0322 \| 23.09 \| 11500 \| 0.5601 \| 0.3439 \| \| 0.0342 \| 24.1 \| 12000 \| 0.5131 \| 0.3451 \| \| 0.0276 \| 25.1 \| 12500 \| 0.5206 \| 0.3392 \| \| 0.0245 \| 26.1 \| 13000 \| 0.5337 \| 0.3373 \| \| 0.0226 \| 27.11 \| 13500 \| 0.5311 \| 0.3353 \| \| 0.0229 \| 28.11 \| 14000 \| 0.5375 \| 0.3373 \| \| 0.0225 \| 29.12 \| 14500 \| 0.5324 \| 0.3355 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1
nutjung/dqn-SpaceInvadersNoFrameskip-v4	nutjung	2022-06-18T20:39:23Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-18T20:38:40Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 610.00 +/- 170.21 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga nutjung -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga nutjung ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
huggingtweets/svelounsegreto	huggingtweets	2022-06-18T18:31:10Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-18T18:29:46Z	--- language: en thumbnail: http://www.huggingtweets.com/svelounsegreto/1655577065862/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/1532495934944432147/fnWmG59I_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">TiSveloUnSegreto</div> <div style="text-align: center; font-size: 14px;">@svelounsegreto</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 TiSveloUnSegreto. \| Data \| TiSveloUnSegreto \| \| --- \| --- \| \| Tweets downloaded \| 233 \| \| Retweets \| 0 \| \| Short tweets \| 0 \| \| Tweets kept \| 233 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2dufvfue/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 @svelounsegreto's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/16tsvbvd) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/16tsvbvd/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/svelounsegreto') 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)
theojolliffe/bart-cnn-science-v3-e2-v4-e2-manual	theojolliffe	2022-06-18T18:01:15Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-18T17:39:12Z	--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-cnn-science-v3-e2-v4-e2-manual 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-cnn-science-v3-e2-v4-e2-manual This model is a fine-tuned version of [theojolliffe/bart-cnn-science-v3-e2](https://huggingface.co/theojolliffe/bart-cnn-science-v3-e2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9189 - Rouge1: 55.982 - Rouge2: 36.9147 - Rougel: 39.1563 - Rougelsum: 53.5959 - Gen Len: 142.0 ## 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: 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: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 42 \| 0.9365 \| 53.4332 \| 34.0477 \| 36.9735 \| 51.1918 \| 142.0 \| \| No log \| 2.0 \| 84 \| 0.9189 \| 55.982 \| 36.9147 \| 39.1563 \| 53.5959 \| 142.0 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
vai6hav/wav2vec2-large-xls-r-300m-hindi-epochs15-colab	vai6hav	2022-06-18T17:42:12Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-06-18T16:56:04Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-hindi-epochs15-colab 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. --> # wav2vec2-large-xls-r-300m-hindi-epochs15-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 3.5705 - Wer: 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---:\| \| 20.2764 \| 5.53 \| 50 \| 8.1197 \| 1.0 \| \| 5.2964 \| 11.11 \| 100 \| 3.5705 \| 1.0 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
theojolliffe/bart-cnn-science-v3-e1-v4-e6-manual	theojolliffe	2022-06-18T17:37:36Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-18T17:14:13Z	--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-cnn-science-v3-e1-v4-e6-manual 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-cnn-science-v3-e1-v4-e6-manual This model is a fine-tuned version of [theojolliffe/bart-cnn-science-v3-e1](https://huggingface.co/theojolliffe/bart-cnn-science-v3-e1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4513 - Rouge1: 51.4471 - Rouge2: 31.5595 - Rougel: 31.7717 - Rougelsum: 49.4999 - Gen Len: 142.0 ## 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: 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: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 42 \| 1.0691 \| 51.1883 \| 31.2479 \| 33.7004 \| 48.9571 \| 142.0 \| \| No log \| 2.0 \| 84 \| 1.0883 \| 51.7634 \| 29.8573 \| 30.7155 \| 49.3378 \| 142.0 \| \| No log \| 3.0 \| 126 \| 1.2355 \| 52.9606 \| 31.3539 \| 33.5131 \| 49.9275 \| 142.0 \| \| No log \| 4.0 \| 168 \| 1.3430 \| 52.2108 \| 32.7896 \| 34.65 \| 50.4271 \| 139.1 \| \| No log \| 5.0 \| 210 \| 1.3963 \| 51.5335 \| 30.4157 \| 31.5759 \| 49.6904 \| 142.0 \| \| No log \| 6.0 \| 252 \| 1.4513 \| 51.4471 \| 31.5595 \| 31.7717 \| 49.4999 \| 142.0 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
theojolliffe/bart-cnn-science-v3-e1-v4-e4-manual	theojolliffe	2022-06-18T17:13:00Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-18T16:46:47Z	--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-cnn-science-v3-e1-v4-e4-manual 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-cnn-science-v3-e1-v4-e4-manual This model is a fine-tuned version of [theojolliffe/bart-cnn-science-v3-e1](https://huggingface.co/theojolliffe/bart-cnn-science-v3-e1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2615 - Rouge1: 53.36 - Rouge2: 32.0237 - Rougel: 33.2835 - Rougelsum: 50.7455 - Gen Len: 142.0 ## 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: 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: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 42 \| 1.0675 \| 51.743 \| 31.3774 \| 34.1939 \| 48.7234 \| 142.0 \| \| No log \| 2.0 \| 84 \| 1.0669 \| 49.4166 \| 28.1438 \| 30.188 \| 46.0289 \| 142.0 \| \| No log \| 3.0 \| 126 \| 1.1799 \| 52.6909 \| 31.0174 \| 35.441 \| 50.0351 \| 142.0 \| \| No log \| 4.0 \| 168 \| 1.2615 \| 53.36 \| 32.0237 \| 33.2835 \| 50.7455 \| 142.0 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
harryb0905/dqn-MountainCar-v0-1-million	harryb0905	2022-06-18T16:57:28Z	2	0	stable-baselines3	[ "stable-baselines3", "MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-18T16:57:05Z	--- library_name: stable-baselines3 tags: - MountainCar-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: -200.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: MountainCar-v0 type: MountainCar-v0 --- # DQN Agent playing MountainCar-v0 This is a trained model of a DQN agent playing MountainCar-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 ... ```
theojolliffe/bart-cnn-science-v3-e2-v4-e4-manual	theojolliffe	2022-06-18T16:45:26Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-18T14:55:43Z	--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-cnn-science-v3-e2-v4-e4-manual 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-cnn-science-v3-e2-v4-e4-manual This model is a fine-tuned version of [theojolliffe/bart-cnn-science-v3-e2](https://huggingface.co/theojolliffe/bart-cnn-science-v3-e2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1223 - Rouge1: 50.8519 - Rouge2: 30.3314 - Rougel: 31.5149 - Rougelsum: 48.4389 - Gen Len: 142.0 ## 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: 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: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 42 \| 0.9420 \| 53.5234 \| 33.6131 \| 35.8383 \| 51.1499 \| 142.0 \| \| No log \| 2.0 \| 84 \| 0.9439 \| 52.388 \| 32.1451 \| 35.2339 \| 49.6554 \| 142.0 \| \| No log \| 3.0 \| 126 \| 1.0321 \| 56.2765 \| 37.671 \| 39.2693 \| 53.5596 \| 142.0 \| \| No log \| 4.0 \| 168 \| 1.1223 \| 50.8519 \| 30.3314 \| 31.5149 \| 48.4389 \| 142.0 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
Ambiwlans/PPO-1m-SpaceInvadersNoFrameskip-v4	Ambiwlans	2022-06-18T15:58:34Z	1	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-18T15:57:59Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 273.00 +/- 82.29 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # PPO Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo ppo --env SpaceInvadersNoFrameskip-v4 -orga Ambiwlans -f logs/ python enjoy.py --algo ppo --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo ppo --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo ppo --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Ambiwlans ``` ## Hyperparameters ```python OrderedDict([('batch_size', 256), ('clip_range', 'lin_0.1'), ('ent_coef', 0.01), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('frame_stack', 4), ('learning_rate', 'lin_2.5e-4'), ('n_envs', 8), ('n_epochs', 4), ('n_steps', 128), ('n_timesteps', 1000000.0), ('policy', 'CnnPolicy'), ('vf_coef', 0.5), ('normalize', False)]) ```
anibahug/mt5-small-finetuned-amazon-en-de	anibahug	2022-06-18T15:39:26Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "summarization", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-06-18T14:20:45Z	--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-finetuned-amazon-en-de 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. --> # mt5-small-finetuned-amazon-en-de This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the [Amazon reviews multi](https://huggingface.co/datasets/amazon_reviews_multi) dataset. It achieves the following results on the evaluation set: - Loss: 3.2896 - Rouge1: 14.7163 - Rouge2: 6.6341 - Rougel: 14.2052 - Rougelsum: 14.2318 ## Model description the model can summarize texts for english and deutsch ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure the training was done on google colab ( using it's free GPU) ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-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: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\| \| 7.2925 \| 1.0 \| 1276 \| 3.5751 \| 13.6254 \| 6.0527 \| 13.109 \| 13.1438 \| \| 4.0677 \| 2.0 \| 2552 \| 3.4031 \| 13.5907 \| 6.068 \| 13.3526 \| 13.2471 \| \| 3.7458 \| 3.0 \| 3828 \| 3.3434 \| 14.7229 \| 6.8482 \| 14.1443 \| 14.2218 \| \| 3.5831 \| 4.0 \| 5104 \| 3.3353 \| 14.8696 \| 6.6371 \| 14.1342 \| 14.2907 \| \| 3.4841 \| 5.0 \| 6380 \| 3.3037 \| 14.233 \| 6.2318 \| 13.9218 \| 13.9781 \| \| 3.4142 \| 6.0 \| 7656 \| 3.2914 \| 13.7344 \| 5.9446 \| 13.5476 \| 13.6362 \| \| 3.3587 \| 7.0 \| 8932 \| 3.2959 \| 14.2007 \| 6.1905 \| 13.5255 \| 13.5237 \| \| 3.3448 \| 8.0 \| 10208 \| 3.2896 \| 14.7163 \| 6.6341 \| 14.2052 \| 14.2318 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
anibahug/marian-finetuned-kde4-en-to-ar	anibahug	2022-06-18T15:19:04Z	6	1	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:kde4", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-06-17T14:21:35Z	--- license: apache-2.0 tags: - translation - generated_from_trainer datasets: - kde4 model-index: - name: marian-finetuned-kde4-en-to-ar 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. --> # marian-finetuned-kde4-en-to-ar This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the kde4 dataset. ## Model description if you want to learn about the model used check [Helsinki-NLP Model](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) ## Intended uses & limitations ## Training and evaluation data More information needed ## Training procedure the training was done on google colab. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - 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 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
nutjung/q-Taxi-v3	nutjung	2022-06-18T14:55:53Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-06-18T14:38:07Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # Q-Learning Agent playing Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="nutjung/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
S2312dal/M6_cross	S2312dal	2022-06-18T14:10:31Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-17T19:41:29Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - spearmanr model-index: - name: M6_cross 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. --> # M6_cross 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: 0.0084 - Pearson: 0.9811 - Spearmanr: 0.9075 ## 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: 20 - eval_batch_size: 20 - seed: 25 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 6.0 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Pearson \| Spearmanr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:---------:\| \| 0.0059 \| 1.0 \| 105 \| 0.0158 \| 0.9633 \| 0.9054 \| \| 0.001 \| 2.0 \| 210 \| 0.0102 \| 0.9770 \| 0.9103 \| \| 0.0008 \| 3.0 \| 315 \| 0.0083 \| 0.9805 \| 0.9052 \| \| 0.0011 \| 4.0 \| 420 \| 0.0075 \| 0.9812 \| 0.9082 \| \| 0.0017 \| 5.0 \| 525 \| 0.0084 \| 0.9811 \| 0.9075 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
KoichiYasuoka/deberta-base-japanese-unidic	KoichiYasuoka	2022-06-18T14:02:31Z	7	0	transformers	[ "transformers", "pytorch", "deberta-v2", "fill-mask", "japanese", "masked-lm", "ja", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-08T08:05:32Z	--- language: - "ja" tags: - "japanese" - "masked-lm" license: "cc-by-sa-4.0" pipeline_tag: "fill-mask" mask_token: "[MASK]" widget: - text: "日本に着いたら[MASK]を訪ねなさい。" --- # deberta-base-japanese-unidic ## Model Description This is a DeBERTa(V2) model pre-trained on 青空文庫 texts with BertJapaneseTokenizer. You can fine-tune `deberta-base-japanese-unidic` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-unidic-luw-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-unidic-ud-head), and so on. ## How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-unidic") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/deberta-base-japanese-unidic") ``` [fugashi](https://pypi.org/project/fugashi) and [unidic-lite](https://pypi.org/project/unidic-lite) are required.
tuni/xlm-roberta-large-xnli-finetuned-mnli-SJP-v2	tuni	2022-06-18T13:48:56Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:swiss_judgment_prediction", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-18T11:53:32Z	--- license: mit tags: - generated_from_trainer datasets: - swiss_judgment_prediction metrics: - accuracy model-index: - name: xlm-roberta-large-xnli-finetuned-mnli-SJP-v2 results: - task: name: Text Classification type: text-classification dataset: name: swiss_judgment_prediction type: swiss_judgment_prediction args: all_languages metrics: - name: Accuracy type: accuracy value: 0.5954285714285714 --- <!-- 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. --> # xlm-roberta-large-xnli-finetuned-mnli-SJP-v2 This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the swiss_judgment_prediction dataset. It achieves the following results on the evaluation set: - Loss: 0.8093 - Accuracy: 0.5954 ## 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: 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 5 \| 0.8879 \| 0.5191 \| \| No log \| 2.0 \| 10 \| 0.8093 \| 0.5954 \| \| No log \| 3.0 \| 15 \| 2.4452 \| 0.3176 \| \| No log \| 4.0 \| 20 \| 3.6636 \| 0.3084 \| \| No log \| 5.0 \| 25 \| 3.7687 \| 0.3393 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
eslamxm/mbart-finetuned-fa	eslamxm	2022-06-18T13:40:54Z	14	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "summarization", "fa", "Abstractive Summarization", "generated_from_trainer", "dataset:pn_summary", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-06-17T00:40:11Z	--- tags: - summarization - fa - mbart - Abstractive Summarization - generated_from_trainer datasets: - pn_summary model-index: - name: mbart-finetuned-fa 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. --> # mbart-finetuned-fa This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the pn_summary dataset. It achieves the following results on the evaluation set: - Loss: 3.2877 - Rouge-1: 44.07 - Rouge-2: 25.81 - Rouge-l: 38.96 - Gen Len: 41.7 - Bertscore: 78.95 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 5 - label_smoothing_factor: 0.1 ### Training results ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
Muennighoff/SGPT-2.7B-weightedmean-nli-bitfit	Muennighoff	2022-06-18T13:11:04Z	7	1	sentence-transformers	[ "sentence-transformers", "pytorch", "gpt_neo", "feature-extraction", "sentence-similarity", "arxiv:2202.08904", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:04Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # SGPT-2.7B-weightedmean-nli-bitfit ## Usage For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt ## Evaluation Results For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904 ## Training The model was trained with the parameters: DataLoader: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 70456 with parameters: ``` {'batch_size': 8} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 7045, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 0.0002 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 7046, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: GPTNeoModel (1): Pooling({'word_embedding_dimension': 2560, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors ```bibtex @article{muennighoff2022sgpt, title={SGPT: GPT Sentence Embeddings for Semantic Search}, author={Muennighoff, Niklas}, journal={arXiv preprint arXiv:2202.08904}, year={2022} } ```
Muennighoff/SGPT-1.3B-weightedmean-nli-bitfit	Muennighoff	2022-06-18T13:04:47Z	387	0	sentence-transformers	[ "sentence-transformers", "pytorch", "gpt_neo", "feature-extraction", "sentence-similarity", "arxiv:2202.08904", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:04Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # SGPT-1.3B-weightedmean-nli-bitfit ## Usage For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt ## Evaluation Results For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904 ## Training The model was trained with the parameters: DataLoader: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 93941 with parameters: ``` {'batch_size': 6} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 9394, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 0.0001 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 9395, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: GPTNeoModel (1): Pooling({'word_embedding_dimension': 2048, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors ```bibtex @article{muennighoff2022sgpt, title={SGPT: GPT Sentence Embeddings for Semantic Search}, author={Muennighoff, Niklas}, journal={arXiv preprint arXiv:2202.08904}, year={2022} } ```
huggingtweets/joejoinerr	huggingtweets	2022-06-18T12:02:03Z	243	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-05-03T12:31:27Z	--- language: en thumbnail: http://www.huggingtweets.com/joejoinerr/1655553718810/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/1477268531561517057/MhgifvbO_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Joe 🍞</div> <div style="text-align: center; font-size: 14px;">@joejoinerr</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 Joe 🍞. \| Data \| Joe 🍞 \| \| --- \| --- \| \| Tweets downloaded \| 3176 \| \| Retweets \| 611 \| \| Short tweets \| 281 \| \| Tweets kept \| 2284 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3f3589ez/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 @joejoinerr's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/35u823qi) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/35u823qi/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/joejoinerr') 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)
nestoralvaro/mt5-small-test-amazon	nestoralvaro	2022-06-18T11:51:27Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "summarization", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-06-18T11:05:22Z	--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-test-amazon 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. --> # mt5-small-test-amazon 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: - Loss: 1.9515 - Rouge1: 30.3066 - Rouge2: 3.3019 - Rougel: 30.1887 - Rougelsum: 30.0314 ## 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: 5.6e-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: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\| \| 10.0147 \| 1.0 \| 1004 \| 2.9904 \| 7.3703 \| 0.2358 \| 7.3703 \| 7.4292 \| \| 3.4892 \| 2.0 \| 2008 \| 2.4061 \| 23.4178 \| 2.4764 \| 23.2901 \| 23.3097 \| \| 2.724 \| 3.0 \| 3012 \| 2.1630 \| 26.6706 \| 2.8302 \| 26.6509 \| 26.5723 \| \| 2.4395 \| 4.0 \| 4016 \| 2.0815 \| 26.7296 \| 2.9481 \| 26.6313 \| 26.533 \| \| 2.2881 \| 5.0 \| 5020 \| 2.0048 \| 30.1887 \| 3.3019 \| 30.0708 \| 29.9135 \| \| 2.1946 \| 6.0 \| 6024 \| 1.9712 \| 29.4811 \| 2.9481 \| 29.4025 \| 29.3042 \| \| 2.1458 \| 7.0 \| 7028 \| 1.9545 \| 29.8153 \| 3.3019 \| 29.717 \| 29.5204 \| \| 2.1069 \| 8.0 \| 8032 \| 1.9515 \| 30.3066 \| 3.3019 \| 30.1887 \| 30.0314 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
Willy/bert-base-spanish-wwm-cased-finetuned-NLP-IE-2	Willy	2022-06-18T10:07:25Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-18T05:31:54Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-base-spanish-wwm-cased-finetuned-NLP-IE-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. --> # bert-base-spanish-wwm-cased-finetuned-NLP-IE-2 This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5279 - Accuracy: 0.7836 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6008 \| 1.0 \| 9 \| 0.5243 \| 0.7836 \| \| 0.6014 \| 2.0 \| 18 \| 0.5279 \| 0.7836 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
c17hawke/bert-fine-tuned-cola_2	c17hawke	2022-06-18T09:40:26Z	6	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-18T09:20:05Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: bert-fine-tuned-cola_2 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. --> # bert-fine-tuned-cola_2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3078 - Validation Loss: 0.4072 - Epoch: 1 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.4976 \| 0.4236 \| 0 \| \| 0.3078 \| 0.4072 \| 1 \| ### Framework versions - Transformers 4.20.0 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1
S2312dal/M4_MLM_cross	S2312dal	2022-06-18T08:48:02Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-18T08:13:26Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - spearmanr model-index: - name: M4_MLM_cross 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. --> # M4_MLM_cross This model is a fine-tuned version of [S2312dal/M4_MLM](https://huggingface.co/S2312dal/M4_MLM) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0222 - Pearson: 0.9472 - Spearmanr: 0.8983 ## 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: 25 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 8.0 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Pearson \| Spearmanr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:---------:\| \| 0.0353 \| 1.0 \| 131 \| 0.0590 \| 0.8326 \| 0.8225 \| \| 0.0478 \| 2.0 \| 262 \| 0.0368 \| 0.9234 \| 0.8894 \| \| 0.0256 \| 3.0 \| 393 \| 0.0222 \| 0.9472 \| 0.8983 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
janeel/muppet-roberta-base-finetuned-squad	janeel	2022-06-18T07:57:35Z	15	2	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "dataset:squad_v2", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2022-06-18T04:37:07Z	--- license: mit tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: muppet-roberta-base-finetuned-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. --> # muppet-roberta-base-finetuned-squad This model is a fine-tuned version of [facebook/muppet-roberta-base](https://huggingface.co/facebook/muppet-roberta-base) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.9017 ## 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.7007 \| 1.0 \| 8239 \| 0.7905 \| \| 0.4719 \| 2.0 \| 16478 \| 0.9017 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
ouiame/bert2gpt2frenchSumm	ouiame	2022-06-18T06:31:16Z	4	1	transformers	[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "autotrain", "unk", "dataset:ouiame/autotrain-data-orangesum", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-17T23:10:00Z	--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - ouiame/autotrain-data-orangesum co2_eq_emissions: 999.838587232387 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 1000833138 - CO2 Emissions (in grams): 999.838587232387 ## Validation Metrics - Loss: 2.4244203567504883 - Rouge1: 25.7023 - Rouge2: 8.5872 - RougeL: 18.6776 - RougeLsum: 19.821 - Gen Len: 39.732 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/ouiame/autotrain-orangesum-1000833138 ```
HHHHHHHHHHHHHHHHHHHHHHHHH/Fart	HHHHHHHHHHHHHHHHHHHHHHHHH	2022-06-18T00:49:05Z	0	0	null	[ "region:us" ]	null	2022-06-18T00:48:25Z	license: afl-3.0 it makes fart noise
kornosk/polibertweet-political-twitter-roberta-mlm	kornosk	2022-06-17T23:45:14Z	496	2	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "twitter", "masked-token-prediction", "bertweet", "election2020", "politics", "en", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-05-02T22:20:16Z	--- language: "en" tags: - twitter - masked-token-prediction - bertweet - election2020 - politics license: "gpl-3.0" --- # Pre-trained BERT on Twitter US Political Election 2020 Pre-trained weights for PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter, LREC 2022. Please see the [official repository](https://github.com/GU-DataLab/PoliBERTweet) for more detail. We use the initialized weights from [BERTweet](https://huggingface.co/vinai/bertweet-base) or `vinai/bertweet-base`. # Training Data This model is pre-trained on over 83 million English tweets about the 2020 US Presidential Election. # Training Objective This model is initialized with BERTweet and trained with an MLM objective. # Usage This pre-trained language model can be fine-tunned to any downstream task (e.g. classification). ```python from transformers import AutoModel, AutoTokenizer, pipeline import torch # choose GPU if available device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # select mode path here pretrained_LM_path = "kornosk/polibertweet-mlm" # load model tokenizer = AutoTokenizer.from_pretrained(pretrained_LM_path) model = AutoModel.from_pretrained(pretrained_LM_path) # fill mask example = "Trump is the <mask> of USA" fill_mask = pipeline('fill-mask', model=pretrained_LM_path, tokenizer=tokenizer) outputs = fill_mask(example) print(outputs) # see embeddings inputs = tokenizer(example, return_tensors="pt") outputs = model(**inputs) print(outputs) # OR you can use this model to train on your downstream task! # please consider citing our paper if you feel this is useful :) ``` # Reference - [PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter](XXX), LREC 2022. # Citation ```bibtex @inproceedings{kawintiranon2022polibertweet, title = {PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter}, author = {Kawintiranon, Kornraphop and Singh, Lisa}, booktitle = {Proceedings of the Language Resources and Evaluation Conference}, year = {2022}, publisher = {European Language Resources Association} } ```
gemasphi/laprador_f	gemasphi	2022-06-17T21:11:03Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-06-17T21:10:48Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # gemasphi/laprador_f 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('gemasphi/laprador_f') 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('gemasphi/laprador_f') model = AutoModel.from_pretrained('gemasphi/laprador_f') # 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=gemasphi/laprador_f) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel (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 -->
Mahmoud1816Yasser/tmp_trainer	Mahmoud1816Yasser	2022-06-17T21:10:28Z	4	0	transformers	[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "endpoints_compatible", "region:us" ]	audio-classification	2022-06-17T21:05:23Z	--- tags: - generated_from_trainer model-index: - name: tmp_trainer 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. --> # tmp_trainer This model was trained from scratch 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 - num_epochs: 3.0 ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
huggingtweets/itsamedevdev	huggingtweets	2022-06-17T20:01:28Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-17T20:01:21Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true 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/1502217816421941249/jOIqVIE2_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">ItAMeDevDev</div> <div style="text-align: center; font-size: 14px;">@itsamedevdev</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 ItAMeDevDev. \| Data \| ItAMeDevDev \| \| --- \| --- \| \| Tweets downloaded \| 2842 \| \| Retweets \| 1052 \| \| Short tweets \| 474 \| \| Tweets kept \| 1316 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/lr4yyk0f/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 @itsamedevdev's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2advtlvo) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2advtlvo/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/itsamedevdev') 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)
huggingtweets/pdchina	huggingtweets	2022-06-17T18:03:08Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-17T18:01:23Z	--- language: en thumbnail: http://www.huggingtweets.com/pdchina/1655488982839/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/1246469365089939456/jAjE_fKB_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">People's Daily, China</div> <div style="text-align: center; font-size: 14px;">@pdchina</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 People's Daily, China. \| Data \| People's Daily, China \| \| --- \| --- \| \| Tweets downloaded \| 3250 \| \| Retweets \| 20 \| \| Short tweets \| 2 \| \| Tweets kept \| 3228 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3b8is5jg/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 @pdchina's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3rg0kmkg) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3rg0kmkg/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/pdchina') 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)
eslamxm/MBart-finetuned-ur-xlsum	eslamxm	2022-06-17T14:59:58Z	30	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "summarization", "ur", "seq2seq", "Abstractive Summarization", "generated_from_trainer", "dataset:xlsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-06-16T23:41:23Z	--- tags: - summarization - ur - seq2seq - mbart - Abstractive Summarization - generated_from_trainer datasets: - xlsum model-index: - name: MBart-finetuned-ur-xlsum 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. --> # MBart-finetuned-ur-xlsum This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 3.2663 - Rouge-1: 40.6 - Rouge-2: 18.9 - Rouge-l: 34.39 - Gen Len: 37.88 - Bertscore: 77.06 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 5 - label_smoothing_factor: 0.1 ### Training results ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
wiselinjayajos/finetuned-bert-mrpc	wiselinjayajos	2022-06-17T14:58:18Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-17T12:08:42Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: finetuned-bert-mrpc results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8455882352941176 - name: F1 type: f1 value: 0.8908145580589255 --- <!-- 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. --> # finetuned-bert-mrpc This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4755 - Accuracy: 0.8456 - F1: 0.8908 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure Trained on my local laptop and the training time took 3 hours. ### 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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5331 \| 1.0 \| 230 \| 0.3837 \| 0.8505 \| 0.8943 \| \| 0.3023 \| 2.0 \| 460 \| 0.3934 \| 0.8505 \| 0.8954 \| \| 0.1472 \| 3.0 \| 690 \| 0.4755 \| 0.8456 \| 0.8908 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
efederici/convnext-base-224-22k-1k-orig-cats-vs-dogs	efederici	2022-06-17T14:11:20Z	56	0	transformers	[ "transformers", "pytorch", "tensorboard", "convnext", "image-classification", "vision", "dataset:cats_vs_dogs", "arxiv:2201.03545", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-06-17T09:33:45Z	--- license: apache-2.0 tags: - image-classification - vision datasets: - cats_vs_dogs metrics: - accuracy model-index: - name: convnext-base-224-22k-1k-orig-cats-vs-dogs results: - task: name: Image Classification type: image-classification dataset: name: cats_vs_dogs type: cats_vs_dogs args: default metrics: - name: Accuracy type: accuracy value: 0.9973333333333333 --- # convnext-base-224-22k-1k-orig-cats-vs-dogs This model is a fine-tuned version of [facebook/convnext-base-224-22k-1k](https://huggingface.co/facebook/convnext-base-224-22k-1k) on the cats_vs_dogs dataset. It achieves the following results on the evaluation set: - Loss: 0.0103 - Accuracy: 0.9973 <p align="center"> <img src="https://files.ocula.com/anzax/09/09f77133-7740-4130-a567-84fb56736362_650_544.jpg" width="600"> </br> Jockum Nordström, Cat Dog Cat, 2016 </p> ## Model description The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2201-03545, author = {Zhuang Liu and Hanzi Mao and Chao{-}Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie}, title = {A ConvNet for the 2020s}, journal = {CoRR}, volume = {abs/2201.03545}, year = {2022}, url = {https://arxiv.org/abs/2201.03545}, eprinttype = {arXiv}, eprint = {2201.03545}, timestamp = {Thu, 20 Jan 2022 14:21:35 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
backnotprop/informative-drawings-image-to-opensketch-onnx	backnotprop	2022-06-17T14:08:31Z	0	0	null	[ "onnx", "license:mit", "region:us" ]	null	2022-06-17T14:07:35Z	--- license: mit --- All credit to this repo: https://huggingface.co/spaces/carolineec/informativedrawings
huggingtweets/aiww-bbcworld-elonmusk	huggingtweets	2022-06-17T14:04:23Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-17T14:04:15Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true 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/1529956155937759233/Nyn1HZWF_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/1529107170448523264/q3VwEx38_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/2972716369/e27a35486a2ec507063cb19c89e3ce82_400x400.jpeg')"> </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 & BBC News (World) & 艾未未 Ai Weiwei</div> <div style="text-align: center; font-size: 14px;">@aiww-bbcworld-elonmusk</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 & BBC News (World) & 艾未未 Ai Weiwei. \| Data \| Elon Musk \| BBC News (World) \| 艾未未 Ai Weiwei \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3200 \| 3250 \| 3243 \| \| Retweets \| 145 \| 240 \| 680 \| \| Short tweets \| 966 \| 0 \| 2116 \| \| Tweets kept \| 2089 \| 3010 \| 447 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1xg6gwun/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 @aiww-bbcworld-elonmusk's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3f692l8n) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3f692l8n/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/aiww-bbcworld-elonmusk') 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)
classla/bcms-bertic-parlasent-bcs-bi	classla	2022-06-17T13:51:54Z	11	0	transformers	[ "transformers", "pytorch", "electra", "text-classification", "sentiment-analysis", "hr", "arxiv:2206.00929", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-01T09:10:17Z	--- language: "hr" tags: - text-classification - sentiment-analysis widget: - text: "Poštovani potpredsjedničke Vlade i ministre hrvatskih branitelja, mislite li da ste zapravo iznevjerili svoje suborce s kojima ste 555 dana prosvjedovali u šatoru protiv tadašnjih dužnosnika jer ste zapravo donijeli zakon koji je neprovediv, a birali ste si suradnike koji nemaju etički integritet." --- # bcms-bertic-parlasent-bcs-bi Binary text classification model based on [`classla/bcms-bertic`](https://huggingface.co/classla/bcms-bertic) and fine-tuned on the BCS Political Sentiment dataset (sentence-level data). This classifier classifies text into only two categories: Negative vs. Other. For the ternary classifier (Negative, Neutral, Positive) check [this model](https://huggingface.co/classla/bcms-bertic-parlasent-bcs-ter). For details on the dataset and the finetuning procedure, please see [this paper](https://arxiv.org/abs/2206.00929). ## Fine-tuning hyperparameters Fine-tuning was performed with `simpletransformers`. Beforehand a brief sweep for the optimal number of epochs was performed and the presumed best value was 9. Other arguments were kept default. ```python model_args = { "num_train_epochs": 9 } ``` ## Performance in comparison with ternary classifier \| model \| average macro F1 \| \|-------------------------------------------\|------------------\| \| bcms-bertic-parlasent-bcs-ter \| 0.7941 ± 0.0101 \| \| bcms-bertic-parlasent-bcs-bi (this model) \| 0.8999 ± 0.012 \| ## Use example with `simpletransformers==0.63.7` ```python from simpletransformers.classification import ClassificationModel model = ClassificationModel("electra", "classla/bcms-bertic-parlasent-bcs-bi") predictions, logits = model.predict([ "Đački autobusi moraju da voze svaki dan", "Vi niste normalni" ] ) predictions # Output: array([1, 0]) [model.config.id2label[i] for i in predictions] # Output: ['Other', 'Negative'] ``` ## Citation If you use the model, please cite the following paper on which the original model is based: ``` @inproceedings{ljubesic-lauc-2021-bertic, title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian", author = "Ljube{\v{s}}i{\'c}, Nikola and Lauc, Davor", booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", month = apr, year = "2021", address = "Kiyv, Ukraine", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.bsnlp-1.5", pages = "37--42", } ``` and the paper describing the dataset and methods for the current finetuning: ``` @misc{https://doi.org/10.48550/arxiv.2206.00929, doi = {10.48550/ARXIV.2206.00929}, url = {https://arxiv.org/abs/2206.00929}, author = {Mochtak, Michal and Rupnik, Peter and Ljubešič, Nikola}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {The ParlaSent-BCS dataset of sentiment-annotated parliamentary debates from Bosnia-Herzegovina, Croatia, and Serbia}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution Share Alike 4.0 International} } ```
frollo/word2vec-for-crime-categorization	frollo	2022-06-17T13:49:03Z	0	1	null	[ "license:cc0-1.0", "region:us" ]	null	2022-06-17T13:45:21Z	--- license: cc0-1.0 --- Word2Vec model obtained by training the model of [1] on a dataset of 17,500 Italian news articles related to crime events [1] Di Gennaro G., Buonanno A., Di Girolamo A., Ospedale A., Palmieri F.A.N., Fedele G. (2021) An Analysis of Word2Vec for the Italian Language. In: Esposito A., Faundez-Zanuy M., Morabito F., Pasero E. (eds) Progresses in Artificial Intelligence and Neural Systems. Smart Innovation, Systems and Technologies, vol 184. Springer, Singapore. https://doi.org/10.1007/978-981-15-5093-5_13 If the dataset is useful, please consider citing paper using the BibTex entry below. ``` @inproceedings{bonisoli2021fedcsis, author = {Giovanni Bonisoli and Federica Rollo and Laura Po}, editor = {Maria Ganzha and Leszek A. Maciaszek and Marcin Paprzycki and Dominik Slezak}, title = {Using Word Embeddings for Italian Crime News Categorization}, booktitle = {Proceedings of the 16th Conference on Computer Science and Intelligence Systems, Online, September 2-5, 2021}, pages = {461--470}, year = {2021}, url = {https://doi.org/10.15439/2021F118}, doi = {10.15439/2021F118} } ```
joitandr/dqn-SpaceInvadersNoFrameskip-v4	joitandr	2022-06-17T12:56:51Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-17T12:56:12Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 597.50 +/- 100.68 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga joitandr -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga joitandr ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
Guillaume63/q-Taxi-v3	Guillaume63	2022-06-17T12:24:35Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-06-17T12:24:27Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # Q-Learning Agent playing Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Guillaume63/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
mosesju/distilbert-base-uncased-finetuned-news	mosesju	2022-06-17T12:14:46Z	12	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:ag_news", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-14T20:16:59Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - ag_news metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-news results: - task: name: Text Classification type: text-classification dataset: name: ag_news type: ag_news args: default metrics: - name: Accuracy type: accuracy value: 0.9388157894736842 - name: F1 type: f1 value: 0.9388275184627893 --- <!-- 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-news This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset. It achieves the following results on the evaluation set: - Loss: 0.2117 - Accuracy: 0.9388 - F1: 0.9388 ## Model description This model is intended to categorize news headlines into one of four categories; World, Sports, Science & Technology, or Business ## Intended uses & limitations The model is limited by the training data it used. If you use the model with a news story that falls outside of the four intended categories, it produces quite confused results. ## 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: 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.2949 \| 1.0 \| 3750 \| 0.2501 \| 0.9262 \| 0.9261 \| \| 0.1569 \| 2.0 \| 7500 \| 0.2117 \| 0.9388 \| 0.9388 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
huggingtweets/techreview	huggingtweets	2022-06-17T09:38:07Z	3	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-17T09:28:26Z	--- language: en thumbnail: http://www.huggingtweets.com/techreview/1655458683048/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/1072880528712495106/ahuQUlOb_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">MIT Technology Review</div> <div style="text-align: center; font-size: 14px;">@techreview</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 MIT Technology Review. \| Data \| MIT Technology Review \| \| --- \| --- \| \| Tweets downloaded \| 3250 \| \| Retweets \| 293 \| \| Short tweets \| 1 \| \| Tweets kept \| 2956 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1zbwqwsb/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 @techreview's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2bzg3pev) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2bzg3pev/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/techreview') 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)
rajendra-ml/q-Taxi-v3	rajendra-ml	2022-06-17T09:22:13Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-06-17T09:22:03Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # Q-Learning Agent playing Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="rajendra-ml/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
huggingtweets/iantdr	huggingtweets	2022-06-17T09:09:33Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-17T09:09:26Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true 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/1365703183/YT_Croydon_Flyer_twitter_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">ian anderson</div> <div style="text-align: center; font-size: 14px;">@iantdr</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 ian anderson. \| Data \| ian anderson \| \| --- \| --- \| \| Tweets downloaded \| 3201 \| \| Retweets \| 2052 \| \| Short tweets \| 316 \| \| Tweets kept \| 833 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1bopfm9o/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 @iantdr's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1papgk0r) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1papgk0r/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/iantdr') 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)
S2312dal/M8_MLM	S2312dal	2022-06-17T08:55:06Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "albert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-17T08:52:59Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: M8_MLM 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. --> # M8_MLM This model is a fine-tuned version of [sentence-transformers/paraphrase-albert-small-v2](https://huggingface.co/sentence-transformers/paraphrase-albert-small-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 8.9140 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 9.5021 \| 1.0 \| 25 \| 9.1463 \| \| 9.0507 \| 2.0 \| 50 \| 8.9504 \| \| 8.9528 \| 3.0 \| 75 \| 8.9148 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
S2312dal/M7_MLM	S2312dal	2022-06-17T08:49:00Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-17T08:40:50Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: M7_MLM 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. --> # M7_MLM This model is a fine-tuned version of [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 8.2304 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 9.2227 \| 1.0 \| 25 \| 8.6091 \| \| 8.6536 \| 2.0 \| 50 \| 8.2492 \| \| 8.5065 \| 3.0 \| 75 \| 8.3056 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
S2312dal/M5_MLM	S2312dal	2022-06-17T08:25:48Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "deberta-v2", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-17T08:02:01Z	--- license: mit tags: - generated_from_trainer model-index: - name: M5_MLM 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. --> # M5_MLM 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: 7.0447 ## 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: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 9.8279 \| 1.0 \| 62 \| 7.9889 \| \| 7.7536 \| 2.0 \| 124 \| 7.3750 \| \| 7.2065 \| 3.0 \| 186 \| 6.8625 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
ArthurZ/tiny-random-bert-sharded	ArthurZ	2022-06-17T08:07:42Z	5,243	0	transformers	[ "transformers", "tf", "bert", "feature-extraction", "generated_from_keras_callback", "endpoints_compatible", "region:us" ]	feature-extraction	2022-06-17T07:49:01Z	--- tags: - generated_from_keras_callback model-index: - name: tiny-random-bert-sharded 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. --> # tiny-random-bert-sharded This model was trained from scratch 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 4.21.0.dev0 - TensorFlow 2.9.0 - Datasets 2.2.2 - Tokenizers 0.12.1
marcomameli01/segformer-b0-finetuned-segments-gear2	marcomameli01	2022-06-17T08:03:25Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "segformer", "vision", "gear-segmentation", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2022-06-17T07:37:58Z	--- license: apache-2.0 tags: - vision - gear-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-segments-gear2 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-finetuned-segments-gear2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the marcomameli01/gear dataset. It achieves the following results on the evaluation set: - Loss: 0.1268 - Mean Iou: 0.1254 - Mean Accuracy: 0.2509 - Overall Accuracy: 0.2509 - Per Category Iou: [0.0, 0.2508641975308642] - Per Category Accuracy: [nan, 0.2508641975308642] ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:--------------------------:\|:--------------------------:\| \| 0.4614 \| 5.0 \| 20 \| 0.4427 \| 0.0741 \| 0.1481 \| 0.1481 \| [0.0, 0.14814814814814814] \| [nan, 0.14814814814814814] \| \| 0.3327 \| 10.0 \| 40 \| 0.2933 \| 0.1726 \| 0.3453 \| 0.3453 \| [0.0, 0.34528395061728395] \| [nan, 0.34528395061728395] \| \| 0.2305 \| 15.0 \| 60 \| 0.2244 \| 0.0382 \| 0.0763 \| 0.0763 \| [0.0, 0.07634567901234568] \| [nan, 0.07634567901234568] \| \| 0.2011 \| 20.0 \| 80 \| 0.2130 \| 0.0374 \| 0.0748 \| 0.0748 \| [0.0, 0.07476543209876543] \| [nan, 0.07476543209876543] \| \| 0.1846 \| 25.0 \| 100 \| 0.1672 \| 0.1037 \| 0.2073 \| 0.2073 \| [0.0, 0.20730864197530866] \| [nan, 0.20730864197530866] \| \| 0.1622 \| 30.0 \| 120 \| 0.1532 \| 0.0805 \| 0.1611 \| 0.1611 \| [0.0, 0.1610864197530864] \| [nan, 0.1610864197530864] \| \| 0.139 \| 35.0 \| 140 \| 0.1396 \| 0.0971 \| 0.1942 \| 0.1942 \| [0.0, 0.19417283950617284] \| [nan, 0.19417283950617284] \| \| 0.1342 \| 40.0 \| 160 \| 0.1283 \| 0.0748 \| 0.1496 \| 0.1496 \| [0.0, 0.14962962962962964] \| [nan, 0.14962962962962964] \| \| 0.128 \| 45.0 \| 180 \| 0.1224 \| 0.1128 \| 0.2256 \| 0.2256 \| [0.0, 0.22558024691358025] \| [nan, 0.22558024691358025] \| \| 0.1243 \| 50.0 \| 200 \| 0.1268 \| 0.1254 \| 0.2509 \| 0.2509 \| [0.0, 0.2508641975308642] \| [nan, 0.2508641975308642] \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53	gary109	2022-06-17T07:30:08Z	3	1	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-06-15T08:57:00Z	--- license: apache-2.0 tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer model-index: - name: ai-light-dance_singing_ft_wav2vec2-large-xlsr-53 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. --> # ai-light-dance_singing_ft_wav2vec2-large-xlsr-53 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING dataset. It achieves the following results on the evaluation set: - Loss: 0.4327 - Wer: 0.2043 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.4089 \| 1.0 \| 552 \| 1.4750 \| 0.9054 \| \| 0.7995 \| 2.0 \| 1104 \| 0.9044 \| 0.6163 \| \| 0.6232 \| 3.0 \| 1656 \| 0.6645 \| 0.3980 \| \| 0.5351 \| 4.0 \| 2208 \| 0.5674 \| 0.3120 \| \| 0.472 \| 5.0 \| 2760 \| 0.5167 \| 0.2579 \| \| 0.3913 \| 6.0 \| 3312 \| 0.4553 \| 0.2335 \| \| 0.3306 \| 7.0 \| 3864 \| 0.4476 \| 0.2114 \| \| 0.3028 \| 8.0 \| 4416 \| 0.4327 \| 0.2043 \| \| 0.317 \| 9.0 \| 4968 \| 0.4355 \| 0.2033 \| \| 0.2494 \| 10.0 \| 5520 \| 0.4405 \| 0.2022 \| ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.1.dev0 - Tokenizers 0.12.1
gary109/wikitext_roberta-base	gary109	2022-06-17T06:44:42Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "dataset:wikitext", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-17T03:50:05Z	--- license: mit tags: - generated_from_trainer datasets: - wikitext metrics: - accuracy model-index: - name: wikitext_roberta-base results: - task: name: Masked Language Modeling type: fill-mask dataset: name: wikitext wikitext-2-raw-v1 type: wikitext args: wikitext-2-raw-v1 metrics: - name: Accuracy type: accuracy value: 0.7371052344006119 --- <!-- 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. --> # wikitext_roberta-base This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the wikitext wikitext-2-raw-v1 dataset. It achieves the following results on the evaluation set: - Loss: 1.2143 - Accuracy: 0.7371 ## 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.4175 \| 0.99 \| 37 \| 1.3355 \| 0.7194 \| \| 1.438 \| 1.99 \| 74 \| 1.2953 \| 0.7249 \| \| 1.4363 \| 2.99 \| 111 \| 1.2759 \| 0.7276 \| \| 1.3391 \| 3.99 \| 148 \| 1.2904 \| 0.7252 \| \| 1.3741 \| 4.99 \| 185 \| 1.2621 \| 0.7290 \| \| 1.2771 \| 5.99 \| 222 \| 1.2312 \| 0.7353 \| \| 1.287 \| 6.99 \| 259 \| 1.2542 \| 0.7289 \| \| 1.29 \| 7.99 \| 296 \| 1.2290 \| 0.7345 \| \| 1.2948 \| 8.99 \| 333 \| 1.2537 \| 0.7286 \| \| 1.2741 \| 9.99 \| 370 \| 1.2199 \| 0.7354 \| \| 1.2342 \| 10.99 \| 407 \| 1.2520 \| 0.7309 \| \| 1.2199 \| 11.99 \| 444 \| 1.2738 \| 0.7260 \| \| 1.206 \| 12.99 \| 481 \| 1.2286 \| 0.7335 \| \| 1.221 \| 13.99 \| 518 \| 1.2421 \| 0.7327 \| \| 1.2062 \| 14.99 \| 555 \| 1.2402 \| 0.7328 \| \| 1.2305 \| 15.99 \| 592 \| 1.2473 \| 0.7308 \| \| 1.2426 \| 16.99 \| 629 \| 1.2250 \| 0.7318 \| \| 1.2096 \| 17.99 \| 666 \| 1.2186 \| 0.7353 \| \| 1.1961 \| 18.99 \| 703 \| 1.2214 \| 0.7361 \| \| 1.2136 \| 19.99 \| 740 \| 1.2506 \| 0.7311 \| ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1
Sanjeev49/marian-finetuned-kde4-en-to-fr	Sanjeev49	2022-06-17T06:31:10Z	3	0	transformers	[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-15T12:07:09Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Sanjeev49/marian-finetuned-kde4-en-to-fr 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. --> # Sanjeev49/marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0601 - Validation Loss: 0.8952 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 5912, '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 \| \|:----------:\|:---------------:\|:-----:\| \| 1.0601 \| 0.8952 \| 0 \| ### Framework versions - Transformers 4.20.0 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1
anithapappu/wav2vec2-base-timit-google-colab	anithapappu	2022-06-17T03:05:35Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-05-23T19:00:34Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-google-colab 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. --> # wav2vec2-base-timit-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5506 - Wer: 0.3355 ## 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: 0.0001 - 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_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 3.4326 \| 1.0 \| 500 \| 1.5832 \| 1.0063 \| \| 0.8235 \| 2.01 \| 1000 \| 0.5310 \| 0.5134 \| \| 0.4224 \| 3.01 \| 1500 \| 0.4488 \| 0.4461 \| \| 0.2978 \| 4.02 \| 2000 \| 0.4243 \| 0.4191 \| \| 0.232 \| 5.02 \| 2500 \| 0.4532 \| 0.4149 \| \| 0.1902 \| 6.02 \| 3000 \| 0.4732 \| 0.3912 \| \| 0.1628 \| 7.03 \| 3500 \| 0.4807 \| 0.3868 \| \| 0.1437 \| 8.03 \| 4000 \| 0.5295 \| 0.3670 \| \| 0.1241 \| 9.04 \| 4500 \| 0.4602 \| 0.3810 \| \| 0.1206 \| 10.04 \| 5000 \| 0.4691 \| 0.3783 \| \| 0.0984 \| 11.04 \| 5500 \| 0.4500 \| 0.3710 \| \| 0.0929 \| 12.05 \| 6000 \| 0.5247 \| 0.3550 \| \| 0.0914 \| 13.05 \| 6500 \| 0.5546 \| 0.3821 \| \| 0.0742 \| 14.06 \| 7000 \| 0.4874 \| 0.3646 \| \| 0.0729 \| 15.06 \| 7500 \| 0.5327 \| 0.3934 \| \| 0.0663 \| 16.06 \| 8000 \| 0.5769 \| 0.3661 \| \| 0.0575 \| 17.07 \| 8500 \| 0.5191 \| 0.3524 \| \| 0.0588 \| 18.07 \| 9000 \| 0.5155 \| 0.3360 \| \| 0.0456 \| 19.08 \| 9500 \| 0.5135 \| 0.3539 \| \| 0.0444 \| 20.08 \| 10000 \| 0.5380 \| 0.3603 \| \| 0.0419 \| 21.08 \| 10500 \| 0.5275 \| 0.3467 \| \| 0.0366 \| 22.09 \| 11000 \| 0.5072 \| 0.3487 \| \| 0.0331 \| 23.09 \| 11500 \| 0.5450 \| 0.3437 \| \| 0.0345 \| 24.1 \| 12000 \| 0.5138 \| 0.3431 \| \| 0.029 \| 25.1 \| 12500 \| 0.5067 \| 0.3413 \| \| 0.0274 \| 26.1 \| 13000 \| 0.5421 \| 0.3422 \| \| 0.0243 \| 27.11 \| 13500 \| 0.5456 \| 0.3392 \| \| 0.0226 \| 28.11 \| 14000 \| 0.5665 \| 0.3368 \| \| 0.0216 \| 29.12 \| 14500 \| 0.5506 \| 0.3355 \| ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 1.13.3 - Tokenizers 0.12.1
sun1638650145/q-FrozenLake-v1-4x4-noSlippery	sun1638650145	2022-06-17T03:02:12Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-06-17T03:02:00Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # 使用Q-Learning智能体来玩FrozenLake-v1 这是一个使用Q-Learning训练有素的模型玩FrozenLake-v1. ## 用法 ```python model = load_from_hub(repo_id='sun1638650145/q-FrozenLake-v1-4x4-noSlippery', filename='q-learning.pkl') # 不要忘记检查是否需要添加额外的参数(例如is_slippery=False) env = gym.make(model['env_id']) evaluate_agent(env, model['max_steps'], model['n_eval_episodes'], model['qtable'], model['eval_seed']) ```
ouiame/autotrain-Robertatogpt2-995132944	ouiame	2022-06-17T01:09:06Z	4	0	transformers	[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "autotrain", "unk", "dataset:ouiame/autotrain-data-Robertatogpt2", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-16T20:14:06Z	--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - ouiame/autotrain-data-Robertatogpt2 co2_eq_emissions: 611.0958349328379 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 995132944 - CO2 Emissions (in grams): 611.0958349328379 ## Validation Metrics - Loss: 3.8850467205047607 - Rouge1: 16.6344 - Rouge2: 2.9899 - RougeL: 13.5872 - RougeLsum: 13.9042 - Gen Len: 20.0 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/ouiame/autotrain-Robertatogpt2-995132944 ```
huggingtweets/tomcruise	huggingtweets	2022-06-17T01:00:02Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-17T00:59:56Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true 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/603269306026106880/42CwEF4n_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Tom Cruise</div> <div style="text-align: center; font-size: 14px;">@tomcruise</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 Tom Cruise. \| Data \| Tom Cruise \| \| --- \| --- \| \| Tweets downloaded \| 3036 \| \| Retweets \| 1055 \| \| Short tweets \| 88 \| \| Tweets kept \| 1893 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ppnkvd5o/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 @tomcruise's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2q772s43) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2q772s43/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/tomcruise') 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)

waboucay/camembert-large-finetuned-xnli_fr_3_classes-finetuned-rua_wl_3_classes

waboucay

2022-06-20T09:34:17Z

6

0

transformers

[ "transformers", "pytorch", "camembert", "text-classification", "nli", "fr", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-20T09:23:44Z

--- language: - fr tags: - nli metrics: - f1 --- ## Eval results We obtain the following results on ```validation``` and ```test``` sets: | Set | F1<sub>micro</sub> | F1<sub>macro</sub> | |------------|--------------------|--------------------| | validation | 72.4 | 72.2 | | test | 72.8 | 72.5 |

qgrantq/bert-finetuned-squad

qgrantq

2022-06-20T08:03:46Z

3

0

transformers

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

question-answering

2022-06-20T05:30:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bert-finetuned-squad results: [] ---  # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) 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 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

jacobbieker/dgmr

jacobbieker

2022-06-20T07:43:41Z

4

1

transformers

[ "transformers", "pytorch", "nowcasting", "forecasting", "timeseries", "remote-sensing", "gan", "license:mit", "endpoints_compatible", "region:us" ]

null

2022-06-20T07:44:17Z

--- license: mit tags: - nowcasting - forecasting - timeseries - remote-sensing - gan --- # DGMR ## Model description [More information needed] ## Intended uses & limitations [More information needed] ## How to use [More information needed] ## Limitations and bias [More information needed] ## Training data [More information needed] ## Training procedure [More information needed] ## Evaluation results [More information needed]

Hausax/albert-xxlarge-v2-finetuned-Poems

Hausax

2022-06-20T07:19:43Z

6

0

transformers

[ "transformers", "pytorch", "tensorboard", "albert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-19T10:02:39Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: albert-xxlarge-v2-finetuned-Poems results: [] ---  # albert-xxlarge-v2-finetuned-Poems This model is a fine-tuned version of [albert-xxlarge-v2](https://huggingface.co/albert-xxlarge-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1923 ## 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-07 - 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 | |:-------------:|:-----:|:-----:|:---------------:| | 2.482 | 1.0 | 19375 | 2.2959 | | 2.258 | 2.0 | 38750 | 2.2357 | | 2.2146 | 3.0 | 58125 | 2.2085 | | 2.1975 | 4.0 | 77500 | 2.1929 | | 2.1893 | 5.0 | 96875 | 2.1863 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

KM4STfulltext/CSSCI_ABS_roberta_wwm

KM4STfulltext

2022-06-20T07:06:48Z

4

0

transformers

[ "transformers", "pytorch", "bert", "fill-mask", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-15T15:33:54Z

--- license: apache-2.0 --- # Pre-trained Language Model for the Humanities and Social Sciences in Chinese ## Introduction The research for social science texts in Chinese needs the support natural language processing tools. The pre-trained language model has greatly improved the accuracy of text mining in general texts. At present, there is an urgent need for a pre-trained language model specifically for the automatic processing of scientific texts in Chinese social science. We used the abstract of social science research as the training set. Based on the deep language model framework of BERT, we constructed CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm pre-training language models by [transformers/run_mlm.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py) and [transformers/mlm_wwm](https://github.com/huggingface/transformers/tree/main/examples/research_projects/mlm_wwm). We designed four downstream tasks of Text Classification on different Chinese social scientific article corpus to verify the performance of the model. - CSSCI_ABS_BERT , CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm are trained on the abstract of articles published in CSSCI journals. The training set involved in the experiment included a total of `510,956,094 words`. - Based on the idea of Domain-Adaptive Pretraining, `CSSCI_ABS_BERT` and `CSSCI_ABS_roberta` combine a large amount of abstracts of scientific articles in Chinese based on the BERT structure, and continue to train the BERT and Chinese-RoBERTa models respectively to obtain pre-training models for the automatic processing of Chinese Social science research texts. ## News - 2022-06-15 : CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm has been put forward for the first time. ## How to use ### Huggingface Transformers The `from_pretrained` method based on [Huggingface Transformers](https://github.com/huggingface/transformers) can directly obtain CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm models online. - CSSCI_ABS_BERT ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_BERT") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_BERT") ``` - CSSCI_ABS_roberta ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta") ``` - CSSCI_ABS_roberta-wwm ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta_wwm") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta_wwm") ``` ### Download Models - The version of the model we provide is `PyTorch`. ### From Huggingface - Download directly through Huggingface's official website. - [KM4STfulltext/CSSCI_ABS_BERT](https://huggingface.co/KM4STfulltext/CSSCI_ABS_BERT) - [KM4STfulltext/CSSCI_ABS_roberta](https://huggingface.co/KM4STfulltext/CSSCI_ABS_roberta) - [KM4STfulltext/CSSCI_ABS_roberta_wwm](https://huggingface.co/KM4STfulltext/CSSCI_ABS_roberta_wwm) ## Evaluation & Results - We useCSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm to perform Text Classificationon different social science research corpus. The experimental results are as follows. #### Discipline classification experiments of articles published in CSSCI journals https://github.com/S-T-Full-Text-Knowledge-Mining/CSSCI-BERT #### Movement recognition experiments for data analysis and knowledge discovery abstract | Tag | bert-base-Chinese | chinese-roberta-wwm,ext | CSSCI_ABS_BERT | CSSCI_ABS_roberta | CSSCI_ABS_roberta_wwm | support | | ------------ | ----------------- | ----------------------- | -------------- | ----------------- | --------------------- | ------- | | Abstract | 55.23 | 62.44 | 56.8 | 57.96 | 58.26 | 223 | | Location | 61.61 | 54.38 | 61.83 | 61.4 | 61.94 | 2866 | | Metric | 45.08 | 41 | 45.27 | 46.74 | 47.13 | 622 | | Organization | 46.85 | 35.29 | 45.72 | 45.44 | 44.65 | 327 | | Person | 88.66 | 82.79 | 88.21 | 88.29 | 88.51 | 4850 | | Thing | 71.68 | 65.34 | 71.88 | 71.68 | 71.81 | 5993 | | Time | 65.35 | 60.38 | 64.15 | 65.26 | 66.03 | 1272 | | avg | 72.69 | 66.62 | 72.59 | 72.61 | 72.89 | 16153 | #### Chinese literary entity recognition | Tag | bert-base-Chinese | chinese-roberta-wwm,ext | CSSCI_ABS_BERT | CSSCI_ABS_roberta | CSSCI_ABS_roberta_wwm | support | | ------------ | ----------------- | ----------------------- | -------------- | ----------------- | --------------------- | ------- | | Abstract | 55.23 | 62.44 | 56.8 | 57.96 | 58.26 | 223 | | Location | 61.61 | 54.38 | 61.83 | 61.4 | 61.94 | 2866 | | Metric | 45.08 | 41 | 45.27 | 46.74 | 47.13 | 622 | | Organization | 46.85 | 35.29 | 45.72 | 45.44 | 44.65 | 327 | | Person | 88.66 | 82.79 | 88.21 | 88.29 | 88.51 | 4850 | | Thing | 71.68 | 65.34 | 71.88 | 71.68 | 71.81 | 5993 | | Time | 65.35 | 60.38 | 64.15 | 65.26 | 66.03 | 1272 | | avg | 72.69 | 66.62 | 72.59 | 72.61 | 72.89 | 16153 | ## Cited - If our content is helpful for your research work, please quote our research in your article. - If you want to quote our research, you can use this url [S-T-Full-Text-Knowledge-Mining/CSSCI-BERT (github.com)](https://github.com/S-T-Full-Text-Knowledge-Mining/CSSCI-BERT) as an alternative before our paper is published. ## Disclaimer - The experimental results presented in the report only show the performance under a specific data set and hyperparameter combination, and cannot represent the essence of each model. The experimental results may change due to random number seeds and computing equipment. - **Users can use the model arbitrarily within the scope of the license, but we are not responsible for the direct or indirect losses caused by using the content of the project.** ## Acknowledgment - CSSCI_ABS_BERT was trained based on [BERT-Base-Chinese]([google-research/bert: TensorFlow code and pre-trained models for BERT (github.com)](https://github.com/google-research/bert)). - CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm was trained based on [RoBERTa-wwm-ext, Chinese]([ymcui/Chinese-BERT-wwm: Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型） (github.com)](https://github.com/ymcui/Chinese-BERT-wwm)).

anas-awadalla/prompt-tuned-t5-small-num-tokens-100-squad

anas-awadalla

2022-06-20T04:47:43Z

6

0

transformers

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

null

2022-06-20T00:50:38Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: prompt-tuned-t5-small-num-tokens-100-squad results: [] ---  # prompt-tuned-t5-small-num-tokens-100-squad This model is a fine-tuned version of [google/t5-small-lm-adapt](https://huggingface.co/google/t5-small-lm-adapt) 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: 0.3 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 30000 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6

huggingtweets/bartoszmilewski

huggingtweets

2022-06-20T02:35:22Z

3

0

transformers

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

text-generation

2022-06-20T02:33:39Z

--- language: en thumbnail: http://www.huggingtweets.com/bartoszmilewski/1655692518288/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/1000136690/IslandBartosz_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Bartosz Milewski</div> <div style="text-align: center; font-size: 14px;">@bartoszmilewski</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 Bartosz Milewski. | Data | Bartosz Milewski | | --- | --- | | Tweets downloaded | 3248 | | Retweets | 79 | | Short tweets | 778 | | Tweets kept | 2391 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2689vaqz/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 @bartoszmilewski's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1f1jpc3z) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1f1jpc3z/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/bartoszmilewski') 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)

ali2066/DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_french_second_train_set_french_False

ali2066

2022-06-20T01:54:34Z

3

0

transformers

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

text-classification

2022-05-02T14:07:53Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: _ctxSentence_TRAIN_all_TEST_french_second_train_set_french_False results: [] ---  # _ctxSentence_TRAIN_all_TEST_french_second_train_set_french_False This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4936 - Precision: 0.8189 - Recall: 0.9811 - F1: 0.8927 - Accuracy: 0.8120 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 13 | 0.5150 | 0.7447 | 1.0 | 0.8537 | 0.7447 | | No log | 2.0 | 26 | 0.5565 | 0.7447 | 1.0 | 0.8537 | 0.7447 | | No log | 3.0 | 39 | 0.5438 | 0.7778 | 1.0 | 0.8750 | 0.7872 | | No log | 4.0 | 52 | 0.5495 | 0.7778 | 1.0 | 0.8750 | 0.7872 | | No log | 5.0 | 65 | 0.5936 | 0.7778 | 1.0 | 0.8750 | 0.7872 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

huggingtweets/borisjohnson-elonmusk-majornelson

huggingtweets

2022-06-19T22:42:51Z

5

0

transformers

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

text-generation

2022-06-19T22:42:06Z

--- language: en thumbnail: http://www.huggingtweets.com/borisjohnson-elonmusk-majornelson/1655678567047/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/1529956155937759233/Nyn1HZWF_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/1519703427240013824/FOED2v9N_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/1500170386520129536/Rr2G6A-N_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 & Larry Hryb 🇺🇦 & Boris Johnson</div> <div style="text-align: center; font-size: 14px;">@borisjohnson-elonmusk-majornelson</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 & Larry Hryb 🇺🇦 & Boris Johnson. | Data | Elon Musk | Larry Hryb 🇺🇦 | Boris Johnson | | --- | --- | --- | --- | | Tweets downloaded | 3250 | 3250 | 3248 | | Retweets | 147 | 736 | 653 | | Short tweets | 985 | 86 | 17 | | Tweets kept | 2118 | 2428 | 2578 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/22m356ew/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 @borisjohnson-elonmusk-majornelson's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/316f3w9h) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/316f3w9h/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/borisjohnson-elonmusk-majornelson') 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)

sevlabr/unit-1-PPO-LunarLander-v2

sevlabr

2022-06-19T21:52:28Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-19T21:51:58Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 222.00 +/- 55.66 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** 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 ... ```

chradden/generation_xyz

chradden

2022-06-19T21:33:52Z

54

1

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-06-19T21:33:37Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: generation_xyz results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.5504587292671204 --- # generation_xyz Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### Baby Boomers ![Baby Boomers](images/Baby_Boomers.jpg) #### Generation Alpha ![Generation Alpha](images/Generation_Alpha.jpg) #### Generation X ![Generation X](images/Generation_X.jpg) #### Generation Z ![Generation Z](images/Generation_Z.jpg) #### Millennials ![Millennials](images/Millennials.jpg)

voleg44/dqn-SpaceInvadersNoFrameskip-v4

voleg44

2022-06-19T20:06:30Z

3

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-19T20:05:54Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 434.50 +/- 143.59 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga voleg44 -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga voleg44 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

anas-awadalla/prophetnet-large-squad

anas-awadalla

2022-06-19T19:16:54Z

6

0

transformers

[ "transformers", "pytorch", "tensorboard", "prophetnet", "text2text-generation", "generated_from_trainer", "dataset:squad", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-06-19T18:12:00Z

--- tags: - generated_from_trainer datasets: - squad model-index: - name: prophetnet-large-squad results: [] ---  # prophetnet-large-squad This model is a fine-tuned version of [microsoft/prophetnet-large-uncased](https://huggingface.co/microsoft/prophetnet-large-uncased) 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: 3e-05 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 256 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6

martin-ha/text_encoder_in_dual

martin-ha

2022-06-19T19:11:19Z

0

keras

[ "keras", "tf-keras", "region:us" ]

null

2022-06-19T19:10:58Z

--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training Metrics Model history needed ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>

martin-ha/vision_encoder_in_dual

martin-ha

2022-06-19T19:07:22Z

0

keras

[ "keras", "tf-keras", "region:us" ]

null

2022-06-19T19:06:52Z

--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training Metrics Model history needed ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>

diversifix/diversiformer

diversifix

2022-06-19T16:44:04Z

6

3

transformers

[ "transformers", "tf", "t5", "text2text-generation", "de", "arxiv:2010.11934", "license:gpl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-06-19T12:44:02Z

--- language: - de license: gpl widget: - text: "Ersetze \"Lehrer\" durch \"Lehrerin oder Lehrer\": Ein promovierter Mathelehrer ist noch nie im Unterricht eingeschlafen." example_title: "Example 1" - text: "Ersetze \"Student\" durch \"studierende Person\": Maria ist kein Student." example_title: "Example 2" inference: parameters: max_length: 500 --- # Diversiformer 🤗 🏳️‍🌈 🇩🇪 _Work in progress._ Language model for inclusive language in German, fine-tuned on [mT5](https://arxiv.org/abs/2010.11934). An experimental model version is released [on Huggingface](https://huggingface.co/diversifix/diversiformer). Source code for fine-tuning is available [on GitHub](https://github.com/diversifix/diversiformer). ## Tasks - **DETECT**: Recognizes instances of the generic masculine, and of other exclusive language. To do. - **SUGGEST**: Suggest inclusive alternatives to masculine and exclusive words. To do. - **REPLACE**: Replace one phrase by another, while preserving grammatical coherence. Work in progress. - ▶️ `Ersetze "Schüler" durch "Schülerin oder Schüler": Die Schüler kamen zu spät.` ◀️ `Die Schülerinnen und Schüler kamen zu spät.` - ▶️ `Ersetze "Lehrer" durch "Kollegium": Die wartenden Lehrer wunderten sich.` ◀️ `Das wartende Kollegium wunderte sich.` ## Usage ```python >>> from transformers import pipeline >>> generator = pipeline("text2text-generation", model="diversifix/diversiformer") >>> generator('Ersetze "Schüler" durch "Schülerin oder Schüler": Die Schüler kamen zu spät.', max_length=500) ``` ## License Diversiformer. Transformer model for inclusive language. Copyright (C) 2022 [Diversifix e. V.](mailto:vorstand@diversifix.org) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

anjankumar/mbart-large-50-finetuned-en-to-te

anjankumar

2022-06-19T16:32:07Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "generated_from_trainer", "dataset:kde4", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-06-07T07:02:05Z

--- tags: - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: mbart-large-50-finetuned-en-to-te results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 args: en-te metrics: - name: Bleu type: bleu value: 0.7152 ---  # mbart-large-50-finetuned-en-to-te This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 13.8521 - Bleu: 0.7152 - Gen Len: 20.5 ## 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 7 | 13.8521 | 0.7152 | 20.5 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

thaidv96/lead-reliability-scoring

thaidv96

2022-06-19T16:15:46Z

6

1

transformers

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

text-classification

2022-06-19T15:44:25Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: lead-reliability-scoring results: [] ---  # lead-reliability-scoring This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0123 - F1: 0.9937 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 50 | 0.3866 | 0.5761 | | No log | 2.0 | 100 | 0.3352 | 0.6538 | | No log | 3.0 | 150 | 0.1786 | 0.8283 | | No log | 4.0 | 200 | 0.1862 | 0.8345 | | No log | 5.0 | 250 | 0.1367 | 0.8736 | | No log | 6.0 | 300 | 0.0642 | 0.9477 | | No log | 7.0 | 350 | 0.0343 | 0.9748 | | No log | 8.0 | 400 | 0.0190 | 0.9874 | | No log | 9.0 | 450 | 0.0123 | 0.9937 | | 0.2051 | 10.0 | 500 | 0.0058 | 0.9937 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

waboucay/camembert-large-xnli

waboucay

2022-06-19T14:38:51Z

6

0

transformers

[ "transformers", "pytorch", "camembert", "text-classification", "nli", "fr", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-19T14:35:57Z

--- language: - fr tags: - nli metrics: - f1 --- ## Eval results We obtain the following results on ```validation``` and ```test``` sets: | Set | F1<sub>micro</sub> | F1<sub>macro</sub> | |------------|--------------------|--------------------| | validation | 85.8 | 85.9 | | test | 84.2 | 84.3 |

waboucay/camembert-large-finetuned-rua_wl_3_classes

waboucay

2022-06-19T14:35:04Z

5

0

transformers

[ "transformers", "pytorch", "camembert", "text-classification", "nli", "fr", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-19T14:31:32Z

--- language: - fr tags: - nli metrics: - f1 --- ## Eval results We obtain the following results on ```validation``` and ```test``` sets: | Set | F1<sub>micro</sub> | F1<sub>macro</sub> | |------------|--------------------|--------------------| | validation | 75.3 | 74.9 | | test | 75.8 | 75.3 |

waboucay/camembert-large-finetuned-repnum_wl_3_classes

waboucay

2022-06-19T14:30:19Z

5

0

transformers

[ "transformers", "pytorch", "camembert", "text-classification", "nli", "fr", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-19T14:22:13Z

--- language: - fr tags: - nli metrics: - f1 --- ## Eval results We obtain the following results on ```validation``` and ```test``` sets: | Set | F1<sub>micro</sub> | F1<sub>macro</sub> | |------------|--------------------|--------------------| | validation | 79.4 | 79.4 | | test | 80.6 | 80.6 |

ctoraman/RoBERTweetTurkCovid

ctoraman

2022-06-19T14:25:58Z

5

0

transformers

[ "transformers", "pytorch", "roberta", "fill-mask", "tr", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-08T11:59:09Z

--- language: - tr tags: - roberta license: cc-by-nc-sa-4.0 --- # RoBERTweetTurkCovid (uncased) Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. The pretrained corpus is a Turkish tweets collection related to COVID-19. Model architecture is similar to RoBERTa-base (12 layers, 12 heads, and 768 hidden size). Tokenization algorithm is WordPiece. Vocabulary size is 30k. The details of pretraining can be found at this paper: ```bibtex @InProceedings{clef-checkthat:2022:task1:oguzhan, author = {Cagri Toraman and Oguzhan Ozcelik and Furkan Şahinuç and Umitcan Sahin}, title = "{ARC-NLP at CheckThat! 2022:} Contradiction for Harmful Tweet Detection", year = {2022}, booktitle = "Working Notes of {CLEF} 2022 - Conference and Labs of the Evaluation Forum", editor = {Faggioli, Guglielmo andd Ferro, Nicola and Hanbury, Allan and Potthast, Martin}, series = {CLEF~'2022}, address = {Bologna, Italy}, } ``` The following code can be used for model loading and tokenization, example max length (768) can be changed: ``` model = AutoModel.from_pretrained([model_path]) #for sequence classification: #model = AutoModelForSequenceClassification.from_pretrained([model_path], num_labels=[num_classes]) tokenizer = PreTrainedTokenizerFast(tokenizer_file=[file_path]) tokenizer.mask_token = "[MASK]" tokenizer.cls_token = "[CLS]" tokenizer.sep_token = "[SEP]" tokenizer.pad_token = "[PAD]" tokenizer.unk_token = "[UNK]" tokenizer.bos_token = "[CLS]" tokenizer.eos_token = "[SEP]" tokenizer.model_max_length = 768 ``` ### BibTeX entry and citation info ```bibtex @InProceedings{clef-checkthat:2022:task1:oguzhan, author = {Cagri Toraman and Oguzhan Ozcelik and Furkan Şahinuç and Umitcan Sahin}, title = "{ARC-NLP at CheckThat! 2022:} Contradiction for Harmful Tweet Detection", year = {2022}, booktitle = "Working Notes of {CLEF} 2022 - Conference and Labs of the Evaluation Forum", editor = {Faggioli, Guglielmo andd Ferro, Nicola and Hanbury, Allan and Potthast, Martin}, series = {CLEF~'2022}, address = {Bologna, Italy}, } ```

rajistics/dqn-SpaceInvadersNoFrameskip-v4

rajistics

2022-06-19T13:48:15Z

2

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-19T13:47:41Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 435.50 +/- 129.62 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rajistics -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga rajistics ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

Classroom-workshop/assignment2-llama

Classroom-workshop

2022-06-19T13:46:40Z

7

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-02T15:27:20Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 200.68 +/- 7.11 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code

huggingtweets/david_lynch

huggingtweets

2022-06-19T13:12:27Z

3

0

transformers

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

text-generation

2022-05-27T11:00:06Z

--- language: en thumbnail: http://www.huggingtweets.com/david_lynch/1655644342827/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/63730229/DL_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">David Lynch</div> <div style="text-align: center; font-size: 14px;">@david_lynch</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 David Lynch. | Data | David Lynch | | --- | --- | | Tweets downloaded | 912 | | Retweets | 29 | | Short tweets | 21 | | Tweets kept | 862 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/do5yghsd/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 @david_lynch's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ddgwjhcj) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ddgwjhcj/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/david_lynch') 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)

gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v2

gary109

2022-06-19T12:14:27Z

3

1

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-06-19T00:34:26Z

--- tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer model-index: - name: ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v2 results: [] ---  # ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v2 This model is a fine-tuned version of [gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v1](https://huggingface.co/gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v1) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING dataset. It achieves the following results on the evaluation set: - Loss: 0.4313 - Wer: 0.1645 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.148 | 1.0 | 552 | 0.4313 | 0.1645 | | 0.1301 | 2.0 | 1104 | 0.4365 | 0.1618 | | 0.1237 | 3.0 | 1656 | 0.4470 | 0.1595 | | 0.1063 | 4.0 | 2208 | 0.4593 | 0.1576 | | 0.128 | 5.0 | 2760 | 0.4525 | 0.1601 | | 0.1099 | 6.0 | 3312 | 0.4593 | 0.1567 | | 0.0969 | 7.0 | 3864 | 0.4625 | 0.1550 | | 0.0994 | 8.0 | 4416 | 0.4672 | 0.1543 | | 0.125 | 9.0 | 4968 | 0.4636 | 0.1544 | | 0.0887 | 10.0 | 5520 | 0.4601 | 0.1538 | ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1

ShannonAI/ChineseBERT-large

ShannonAI

2022-06-19T12:07:31Z

23

5

transformers

[ "transformers", "pytorch", "arxiv:2106.16038", "endpoints_compatible", "region:us" ]

null

2022-03-02T23:29:05Z

# ChineseBERT-large This repository contains code, model, dataset for **ChineseBERT** at ACL2021. paper: **[ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://arxiv.org/abs/2106.16038)** *Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu and Jiwei Li* code: [ChineseBERT github link](https://github.com/ShannonAI/ChineseBert) ## Model description We propose ChineseBERT, which incorporates both the glyph and pinyin information of Chinese characters into language model pretraining. First, for each Chinese character, we get three kind of embedding. - **Char Embedding:** the same as origin BERT token embedding. - **Glyph Embedding:** capture visual features based on different fonts of a Chinese character. - **Pinyin Embedding:** capture phonetic feature from the pinyin sequence ot a Chinese Character. Then, char embedding, glyph embedding and pinyin embedding are first concatenated, and mapped to a D-dimensional embedding through a fully connected layer to form the fusion embedding. Finally, the fusion embedding is added with the position embedding, which is fed as input to the BERT model. The following image shows an overview architecture of ChineseBERT model. ![MODEL](https://raw.githubusercontent.com/ShannonAI/ChineseBert/main/images/ChineseBERT.png) ChineseBERT leverages the glyph and pinyin information of Chinese characters to enhance the model's ability of capturing context semantics from surface character forms and disambiguating polyphonic characters in Chinese.

dibsondivya/ernie-phmtweets-sutd

dibsondivya

2022-06-19T11:38:29Z

14

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "ernie", "health", "tweet", "dataset:custom-phm-tweets", "arxiv:1802.09130", "arxiv:1907.12412", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-19T11:20:14Z

--- tags: - ernie - health - tweet datasets: - custom-phm-tweets metrics: - accuracy model-index: - name: ernie-phmtweets-sutd results: - task: name: Text Classification type: text-classification dataset: name: custom-phm-tweets type: labelled metrics: - name: Accuracy type: accuracy value: 0.885 --- # ernie-phmtweets-sutd This model is a fine-tuned version of [ernie-2.0-en](https://huggingface.co/nghuyong/ernie-2.0-en) for text classification to identify public health events through tweets. The project was based on an [Emory University Study on Detection of Personal Health Mentions in Social Media paper](https://arxiv.org/pdf/1802.09130v2.pdf), that worked with this [custom dataset](https://github.com/emory-irlab/PHM2017). It achieves the following results on the evaluation set: - Accuracy: 0.885 ## Usage ```Python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dibsondivya/ernie-phmtweets-sutd") model = AutoModelForSequenceClassification.from_pretrained("dibsondivya/ernie-phmtweets-sutd") ``` ### Model Evaluation Results With Validation Set - Accuracy: 0.889763779527559 With Test Set - Accuracy: 0.884643644379133 ## References for ERNIE 2.0 Model ```bibtex @article{sun2019ernie20, title={ERNIE 2.0: A Continual Pre-training Framework for Language Understanding}, author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:1907.12412}, year={2019} } ```

levgil2/stam-finetuned-imdb

levgil2

2022-06-19T11:26:02Z

4

0

transformers

[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-19T11:21:46Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: levgil2/stam-finetuned-imdb results: [] ---  # levgil2/stam-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8517 - Validation Loss: 2.5705 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -687, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, '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 | |:----------:|:---------------:|:-----:| | 2.8517 | 2.5705 | 0 | ### Framework versions - Transformers 4.20.0 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

zakria/NLP_Project

zakria

2022-06-19T09:55:56Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-06-19T07:49:04Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: NLP_Project results: [] ---  # NLP_Project This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5308 - Wer: 0.3428 ## 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: 0.0001 - 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_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5939 | 1.0 | 500 | 2.1356 | 1.0014 | | 0.9126 | 2.01 | 1000 | 0.5469 | 0.5354 | | 0.4491 | 3.01 | 1500 | 0.4636 | 0.4503 | | 0.3008 | 4.02 | 2000 | 0.4269 | 0.4330 | | 0.2229 | 5.02 | 2500 | 0.4164 | 0.4073 | | 0.188 | 6.02 | 3000 | 0.4717 | 0.4107 | | 0.1739 | 7.03 | 3500 | 0.4306 | 0.4031 | | 0.159 | 8.03 | 4000 | 0.4394 | 0.3993 | | 0.1342 | 9.04 | 4500 | 0.4462 | 0.3904 | | 0.1093 | 10.04 | 5000 | 0.4387 | 0.3759 | | 0.1005 | 11.04 | 5500 | 0.5033 | 0.3847 | | 0.0857 | 12.05 | 6000 | 0.4805 | 0.3876 | | 0.0779 | 13.05 | 6500 | 0.5269 | 0.3810 | | 0.072 | 14.06 | 7000 | 0.5109 | 0.3710 | | 0.0641 | 15.06 | 7500 | 0.4865 | 0.3638 | | 0.0584 | 16.06 | 8000 | 0.5041 | 0.3646 | | 0.0552 | 17.07 | 8500 | 0.4987 | 0.3537 | | 0.0535 | 18.07 | 9000 | 0.4947 | 0.3586 | | 0.0475 | 19.08 | 9500 | 0.5237 | 0.3647 | | 0.042 | 20.08 | 10000 | 0.5338 | 0.3561 | | 0.0416 | 21.08 | 10500 | 0.5068 | 0.3483 | | 0.0358 | 22.09 | 11000 | 0.5126 | 0.3532 | | 0.0334 | 23.09 | 11500 | 0.5213 | 0.3536 | | 0.0331 | 24.1 | 12000 | 0.5378 | 0.3496 | | 0.03 | 25.1 | 12500 | 0.5167 | 0.3470 | | 0.0254 | 26.1 | 13000 | 0.5245 | 0.3418 | | 0.0233 | 27.11 | 13500 | 0.5393 | 0.3456 | | 0.0232 | 28.11 | 14000 | 0.5279 | 0.3425 | | 0.022 | 29.12 | 14500 | 0.5308 | 0.3428 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

sun1638650145/q-Taxi-v3

sun1638650145

2022-06-19T09:00:38Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-06-19T09:00:26Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.54 +/- 2.73 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # 使用**Q-Learning**智能体来玩**Taxi-v3** 这是一个使用**Q-Learning**训练有素的模型玩**Taxi-v3**. ## 用法 ```python model = load_from_hub(repo_id='sun1638650145/q-Taxi-v3', filename='q-learning.pkl') # 不要忘记检查是否需要添加额外的参数(例如is_slippery=False) env = gym.make(model['env_id']) evaluate_agent(env, model['max_steps'], model['n_eval_episodes'], model['qtable'], model['eval_seed']) ```

ShannonAI/ChineseBERT-base

ShannonAI

2022-06-19T08:14:46Z

109

20

transformers

[ "transformers", "pytorch", "arxiv:2106.16038", "endpoints_compatible", "region:us" ]

null

2022-03-02T23:29:05Z

# ChineseBERT-base This repository contains code, model, dataset for **ChineseBERT** at ACL2021. paper: **[ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://arxiv.org/abs/2106.16038)** *Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu and Jiwei Li* code: [ChineseBERT github link](https://github.com/ShannonAI/ChineseBert) ## Model description We propose ChineseBERT, which incorporates both the glyph and pinyin information of Chinese characters into language model pretraining. First, for each Chinese character, we get three kind of embedding. - **Char Embedding:** the same as origin BERT token embedding. - **Glyph Embedding:** capture visual features based on different fonts of a Chinese character. - **Pinyin Embedding:** capture phonetic feature from the pinyin sequence ot a Chinese Character. Then, char embedding, glyph embedding and pinyin embedding are first concatenated, and mapped to a D-dimensional embedding through a fully connected layer to form the fusion embedding. Finally, the fusion embedding is added with the position embedding, which is fed as input to the BERT model. The following image shows an overview architecture of ChineseBERT model. ![MODEL](https://raw.githubusercontent.com/ShannonAI/ChineseBert/main/images/ChineseBERT.png) ChineseBERT leverages the glyph and pinyin information of Chinese characters to enhance the model's ability of capturing context semantics from surface character forms and disambiguating polyphonic characters in Chinese.

botika/checkpoint-124500-finetuned-squad

botika

2022-06-19T05:53:11Z

7

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]

question-answering

2022-06-17T07:41:58Z

--- tags: - generated_from_trainer model-index: - name: checkpoint-124500-finetuned-squad results: [] ---  # checkpoint-124500-finetuned-squad This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 14.9594 ## 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: 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 3.9975 | 1.0 | 3289 | 3.8405 | | 3.7311 | 2.0 | 6578 | 3.7114 | | 3.5681 | 3.0 | 9867 | 3.6829 | | 3.4101 | 4.0 | 13156 | 3.6368 | | 3.2487 | 5.0 | 16445 | 3.6526 | | 3.1143 | 6.0 | 19734 | 3.7567 | | 2.9783 | 7.0 | 23023 | 3.8469 | | 2.8295 | 8.0 | 26312 | 4.0040 | | 2.6912 | 9.0 | 29601 | 4.1996 | | 2.5424 | 10.0 | 32890 | 4.3387 | | 2.4161 | 11.0 | 36179 | 4.4988 | | 2.2713 | 12.0 | 39468 | 4.7861 | | 2.1413 | 13.0 | 42757 | 4.9276 | | 2.0125 | 14.0 | 46046 | 5.0598 | | 1.8798 | 15.0 | 49335 | 5.3347 | | 1.726 | 16.0 | 52624 | 5.5869 | | 1.5994 | 17.0 | 55913 | 5.7161 | | 1.4643 | 18.0 | 59202 | 6.0174 | | 1.3237 | 19.0 | 62491 | 6.4926 | | 1.2155 | 20.0 | 65780 | 6.4882 | | 1.1029 | 21.0 | 69069 | 6.9922 | | 0.9948 | 22.0 | 72358 | 7.1357 | | 0.9038 | 23.0 | 75647 | 7.3676 | | 0.8099 | 24.0 | 78936 | 7.4180 | | 0.7254 | 25.0 | 82225 | 7.7753 | | 0.6598 | 26.0 | 85514 | 7.8643 | | 0.5723 | 27.0 | 88803 | 8.1798 | | 0.5337 | 28.0 | 92092 | 8.3053 | | 0.4643 | 29.0 | 95381 | 8.8597 | | 0.4241 | 30.0 | 98670 | 8.9849 | | 0.3763 | 31.0 | 101959 | 8.8406 | | 0.3479 | 32.0 | 105248 | 9.1517 | | 0.3271 | 33.0 | 108537 | 9.3659 | | 0.2911 | 34.0 | 111826 | 9.4813 | | 0.2836 | 35.0 | 115115 | 9.5746 | | 0.2528 | 36.0 | 118404 | 9.7027 | | 0.2345 | 37.0 | 121693 | 9.7515 | | 0.2184 | 38.0 | 124982 | 9.9729 | | 0.2067 | 39.0 | 128271 | 10.0828 | | 0.2077 | 40.0 | 131560 | 10.0878 | | 0.1876 | 41.0 | 134849 | 10.2974 | | 0.1719 | 42.0 | 138138 | 10.2712 | | 0.1637 | 43.0 | 141427 | 10.5788 | | 0.1482 | 44.0 | 144716 | 10.7465 | | 0.1509 | 45.0 | 148005 | 10.4603 | | 0.1358 | 46.0 | 151294 | 10.7665 | | 0.1316 | 47.0 | 154583 | 10.7724 | | 0.1223 | 48.0 | 157872 | 11.1766 | | 0.1205 | 49.0 | 161161 | 11.1870 | | 0.1203 | 50.0 | 164450 | 11.1053 | | 0.1081 | 51.0 | 167739 | 10.9696 | | 0.103 | 52.0 | 171028 | 11.2010 | | 0.0938 | 53.0 | 174317 | 11.6728 | | 0.0924 | 54.0 | 177606 | 11.1423 | | 0.0922 | 55.0 | 180895 | 11.7409 | | 0.0827 | 56.0 | 184184 | 11.7850 | | 0.0829 | 57.0 | 187473 | 11.8956 | | 0.073 | 58.0 | 190762 | 11.8915 | | 0.0788 | 59.0 | 194051 | 12.1617 | | 0.0734 | 60.0 | 197340 | 12.2007 | | 0.0729 | 61.0 | 200629 | 12.2388 | | 0.0663 | 62.0 | 203918 | 12.2471 | | 0.0662 | 63.0 | 207207 | 12.5830 | | 0.064 | 64.0 | 210496 | 12.6105 | | 0.0599 | 65.0 | 213785 | 12.3712 | | 0.0604 | 66.0 | 217074 | 12.9249 | | 0.0574 | 67.0 | 220363 | 12.7309 | | 0.0538 | 68.0 | 223652 | 12.8068 | | 0.0526 | 69.0 | 226941 | 13.4368 | | 0.0471 | 70.0 | 230230 | 13.5148 | | 0.0436 | 71.0 | 233519 | 13.3391 | | 0.0448 | 72.0 | 236808 | 13.4100 | | 0.0428 | 73.0 | 240097 | 13.5617 | | 0.0401 | 74.0 | 243386 | 13.8674 | | 0.035 | 75.0 | 246675 | 13.5746 | | 0.0342 | 76.0 | 249964 | 13.5042 | | 0.0344 | 77.0 | 253253 | 14.2085 | | 0.0365 | 78.0 | 256542 | 13.6393 | | 0.0306 | 79.0 | 259831 | 13.9807 | | 0.0311 | 80.0 | 263120 | 13.9768 | | 0.0353 | 81.0 | 266409 | 14.5245 | | 0.0299 | 82.0 | 269698 | 13.9471 | | 0.0263 | 83.0 | 272987 | 13.7899 | | 0.0254 | 84.0 | 276276 | 14.3786 | | 0.0267 | 85.0 | 279565 | 14.5611 | | 0.022 | 86.0 | 282854 | 14.2658 | | 0.0198 | 87.0 | 286143 | 14.9215 | | 0.0193 | 88.0 | 289432 | 14.5650 | | 0.0228 | 89.0 | 292721 | 14.7014 | | 0.0184 | 90.0 | 296010 | 14.6946 | | 0.0182 | 91.0 | 299299 | 14.6614 | | 0.0188 | 92.0 | 302588 | 14.6915 | | 0.0196 | 93.0 | 305877 | 14.7262 | | 0.0138 | 94.0 | 309166 | 14.7625 | | 0.0201 | 95.0 | 312455 | 15.0442 | | 0.0189 | 96.0 | 315744 | 14.8832 | | 0.0148 | 97.0 | 319033 | 14.8995 | | 0.0129 | 98.0 | 322322 | 14.8974 | | 0.0132 | 99.0 | 325611 | 14.9813 | | 0.0139 | 100.0 | 328900 | 14.9594 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu102 - Datasets 2.2.2 - Tokenizers 0.12.1

eslamxm/AraT5-base-title-generation-finetune-ar-xlsum

eslamxm

2022-06-19T05:23:32Z

28

1

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "summarization", "Arat5-base", "abstractive summarization", "ar", "xlsum", "generated_from_trainer", "dataset:xlsum", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

summarization

2022-06-18T19:19:57Z

--- tags: - summarization - Arat5-base - abstractive summarization - ar - xlsum - generated_from_trainer datasets: - xlsum model-index: - name: AraT5-base-title-generation-finetune-ar-xlsum results: [] ---  # AraT5-base-title-generation-finetune-ar-xlsum This model is a fine-tuned version of [UBC-NLP/AraT5-base-title-generation](https://huggingface.co/UBC-NLP/AraT5-base-title-generation) on the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 4.2837 - Rouge-1: 32.46 - Rouge-2: 15.15 - Rouge-l: 28.38 - Gen Len: 18.48 - Bertscore: 74.24 ## 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: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 10 - label_smoothing_factor: 0.1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:| | 5.815 | 1.0 | 293 | 4.7437 | 27.05 | 10.49 | 23.56 | 18.03 | 72.56 | | 5.0818 | 2.0 | 586 | 4.5004 | 28.92 | 11.97 | 25.09 | 18.61 | 73.08 | | 4.7855 | 3.0 | 879 | 4.3910 | 29.66 | 12.57 | 25.79 | 18.58 | 73.3 | | 4.588 | 4.0 | 1172 | 4.3469 | 30.22 | 13.05 | 26.36 | 18.59 | 73.61 | | 4.4388 | 5.0 | 1465 | 4.3226 | 30.88 | 13.81 | 27.01 | 18.65 | 73.78 | | 4.3162 | 6.0 | 1758 | 4.2990 | 30.9 | 13.6 | 26.92 | 18.68 | 73.78 | | 4.2178 | 7.0 | 2051 | 4.2869 | 31.35 | 14.01 | 27.41 | 18.57 | 73.96 | | 4.1387 | 8.0 | 2344 | 4.2794 | 31.28 | 13.98 | 27.34 | 18.6 | 73.87 | | 4.0787 | 9.0 | 2637 | 4.2806 | 31.45 | 14.17 | 27.46 | 18.66 | 73.97 | | 4.0371 | 10.0 | 2930 | 4.2837 | 31.55 | 14.19 | 27.52 | 18.65 | 74.0 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

Klinsc/q-FrozenLake-v1-4x4-noSlippery

Klinsc

2022-06-19T04:46:57Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-06-19T04:43:46Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="Klinsc/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

Tstarshak/q-FrozenLake-v1-4x4-noSlippery

Tstarshak

2022-06-19T04:15:20Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-06-19T04:15:13Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="Tstarshak/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

huggingtweets/shxtou

huggingtweets

2022-06-19T03:58:13Z

3

0

transformers

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

text-generation

2022-06-19T03:56:41Z

--- language: en thumbnail: http://www.huggingtweets.com/shxtou/1655611088443/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/1419320614205198350/gHkqH6YI_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Shoto 🗡️</div> <div style="text-align: center; font-size: 14px;">@shxtou</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 Shoto 🗡️. | Data | Shoto 🗡️ | | --- | --- | | Tweets downloaded | 3248 | | Retweets | 617 | | Short tweets | 533 | | Tweets kept | 2098 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2mdmjop6/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 @shxtou's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1pdig81x) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1pdig81x/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/shxtou') 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)

huggingtweets/mysta_rias

huggingtweets

2022-06-19T03:40:55Z

3

0

transformers

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

text-generation

2022-06-19T03:05:09Z

--- language: en thumbnail: http://www.huggingtweets.com/mysta_rias/1655610050415/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/1533221230102433792/Dz_O5gZ7_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Mysta Rias 🕵️‍♂️🦊 NIJISANJI EN</div> <div style="text-align: center; font-size: 14px;">@mysta_rias</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 Mysta Rias 🕵️‍♂️🦊 NIJISANJI EN. | Data | Mysta Rias 🕵️‍♂️🦊 NIJISANJI EN | | --- | --- | | Tweets downloaded | 3245 | | Retweets | 296 | | Short tweets | 1005 | | Tweets kept | 1944 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3r8af65s/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 @mysta_rias's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/zqhadryd) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/zqhadryd/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/mysta_rias') 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)

nicolasfeyer/t5-small-finetuned-la-to-en

nicolasfeyer

2022-06-19T02:21:23Z

3

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

2022-06-18T23:08:33Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: t5-small-finetuned-la-to-en results: [] ---  # t5-small-finetuned-la-to-en 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: 2.2297 - Bleu: 5.8915 - Gen Len: 16.2252 ## 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 | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:| | 3.0883 | 1.0 | 4384 | 2.7499 | 2.8172 | 16.4068 | | 2.8854 | 2.0 | 8768 | 2.5664 | 3.8141 | 16.4581 | | 2.746 | 3.0 | 13152 | 2.4524 | 4.3903 | 16.3977 | | 2.6617 | 4.0 | 17536 | 2.3761 | 4.7858 | 16.3473 | | 2.6185 | 5.0 | 21920 | 2.3205 | 5.2502 | 16.3161 | | 2.573 | 6.0 | 26304 | 2.2763 | 5.4374 | 16.2916 | | 2.5285 | 7.0 | 30688 | 2.2489 | 5.628 | 16.2875 | | 2.4944 | 8.0 | 35072 | 2.2276 | 5.7201 | 16.291 | | 2.4749 | 9.0 | 39456 | 2.2164 | 5.8387 | 16.2795 | | 2.4741 | 10.0 | 43840 | 2.2129 | 5.8654 | 16.2789 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

raedinkhaled/swin-tiny-patch4-window7-224-finetuned-mri

raedinkhaled

2022-06-19T00:13:22Z

80

1

transformers

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

image-classification

2022-06-18T16:25:22Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-mri results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder args: default metrics: - name: Accuracy type: accuracy value: 0.9806603773584905 ---  # swin-tiny-patch4-window7-224-finetuned-mri This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0608 - Accuracy: 0.9807 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0592 | 1.0 | 447 | 0.0823 | 0.9695 | | 0.0196 | 2.0 | 894 | 0.0761 | 0.9739 | | 0.0058 | 3.0 | 1341 | 0.0608 | 0.9807 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

kjunelee/pegasus-samsum

kjunelee

2022-06-18T22:35:27Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "pegasus", "text2text-generation", "generated_from_trainer", "dataset:samsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-06-18T08:01:44Z

--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum results: [] ---  # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0 - Datasets 2.2.3.dev0 - Tokenizers 0.12.1

biu-nlp/superpal

biu-nlp

2022-06-18T22:15:17Z

54

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "arxiv:2009.00590", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-02T23:29:05Z

--- widget: - text: "Prime Minister Hun Sen insisted that talks take place in Cambodia. </s><s> Cambodian leader Hun Sen rejected opposition parties' demands for talks outside the country." --- # SuperPAL model Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline Ori Ernst, Ori Shapira, Ramakanth Pasunuru, Michael Lepioshkin, Jacob Goldberger, Mohit Bansal, Ido Dagan, 2021. [PDF](https://arxiv.org/pdf/2009.00590) **How to use?** ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("biu-nlp/superpal") model = AutoModelForSequenceClassification.from_pretrained("biu-nlp/superpal") ``` The original repo is [here](https://github.com/oriern/SuperPAL). If you find our work useful, please cite the paper as: ```python @inproceedings{ernst-etal-2021-summary, title = "Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline", author = "Ernst, Ori and Shapira, Ori and Pasunuru, Ramakanth and Lepioshkin, Michael and Goldberger, Jacob and Bansal, Mohit and Dagan, Ido", booktitle = "Proceedings of the 25th Conference on Computational Natural Language Learning", month = nov, year = "2021", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.conll-1.25", pages = "310--322" } ```

BeardedJohn/bert-finetuned-seq-classification-fake-news

BeardedJohn

2022-06-18T21:03:42Z

4

0

transformers

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

text-classification

2022-06-17T16:58:33Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: BeardedJohn/bert-finetuned-seq-classification-fake-news results: [] ---  # BeardedJohn/bert-finetuned-seq-classification-fake-news 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: - Train Loss: 0.0719 - Validation Loss: 0.0214 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 332, '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: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.0719 | 0.0214 | 0 | ### Framework versions - Transformers 4.20.0 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

zakria/Project_NLP

zakria

2022-06-18T20:44:06Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-06-18T18:43:39Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: Project_NLP results: [] ---  # Project_NLP This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5324 - Wer: 0.3355 ## 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: 0.0001 - 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_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5697 | 1.0 | 500 | 2.1035 | 0.9979 | | 0.8932 | 2.01 | 1000 | 0.5649 | 0.5621 | | 0.4363 | 3.01 | 1500 | 0.4326 | 0.4612 | | 0.3035 | 4.02 | 2000 | 0.4120 | 0.4191 | | 0.2343 | 5.02 | 2500 | 0.4199 | 0.3985 | | 0.1921 | 6.02 | 3000 | 0.4380 | 0.4043 | | 0.1549 | 7.03 | 3500 | 0.4456 | 0.3925 | | 0.1385 | 8.03 | 4000 | 0.4264 | 0.3871 | | 0.1217 | 9.04 | 4500 | 0.4744 | 0.3774 | | 0.1041 | 10.04 | 5000 | 0.4498 | 0.3745 | | 0.0968 | 11.04 | 5500 | 0.4716 | 0.3628 | | 0.0893 | 12.05 | 6000 | 0.4680 | 0.3764 | | 0.078 | 13.05 | 6500 | 0.5100 | 0.3623 | | 0.0704 | 14.06 | 7000 | 0.4893 | 0.3552 | | 0.0659 | 15.06 | 7500 | 0.4956 | 0.3565 | | 0.0578 | 16.06 | 8000 | 0.5450 | 0.3595 | | 0.0563 | 17.07 | 8500 | 0.4891 | 0.3614 | | 0.0557 | 18.07 | 9000 | 0.5307 | 0.3548 | | 0.0447 | 19.08 | 9500 | 0.4923 | 0.3493 | | 0.0456 | 20.08 | 10000 | 0.5156 | 0.3479 | | 0.0407 | 21.08 | 10500 | 0.4979 | 0.3389 | | 0.0354 | 22.09 | 11000 | 0.5549 | 0.3462 | | 0.0322 | 23.09 | 11500 | 0.5601 | 0.3439 | | 0.0342 | 24.1 | 12000 | 0.5131 | 0.3451 | | 0.0276 | 25.1 | 12500 | 0.5206 | 0.3392 | | 0.0245 | 26.1 | 13000 | 0.5337 | 0.3373 | | 0.0226 | 27.11 | 13500 | 0.5311 | 0.3353 | | 0.0229 | 28.11 | 14000 | 0.5375 | 0.3373 | | 0.0225 | 29.12 | 14500 | 0.5324 | 0.3355 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

nutjung/dqn-SpaceInvadersNoFrameskip-v4

nutjung

2022-06-18T20:39:23Z

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-18T20:38:40Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 610.00 +/- 170.21 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga nutjung -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga nutjung ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

huggingtweets/svelounsegreto

huggingtweets

2022-06-18T18:31:10Z

3

0

transformers

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

text-generation

2022-06-18T18:29:46Z

--- language: en thumbnail: http://www.huggingtweets.com/svelounsegreto/1655577065862/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/1532495934944432147/fnWmG59I_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">TiSveloUnSegreto</div> <div style="text-align: center; font-size: 14px;">@svelounsegreto</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 TiSveloUnSegreto. | Data | TiSveloUnSegreto | | --- | --- | | Tweets downloaded | 233 | | Retweets | 0 | | Short tweets | 0 | | Tweets kept | 233 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2dufvfue/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 @svelounsegreto's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/16tsvbvd) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/16tsvbvd/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/svelounsegreto') 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)

theojolliffe/bart-cnn-science-v3-e2-v4-e2-manual

theojolliffe

2022-06-18T18:01:15Z

4

0

transformers

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

text2text-generation

2022-06-18T17:39:12Z

--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-cnn-science-v3-e2-v4-e2-manual results: [] ---  # bart-cnn-science-v3-e2-v4-e2-manual This model is a fine-tuned version of [theojolliffe/bart-cnn-science-v3-e2](https://huggingface.co/theojolliffe/bart-cnn-science-v3-e2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9189 - Rouge1: 55.982 - Rouge2: 36.9147 - Rougel: 39.1563 - Rougelsum: 53.5959 - Gen Len: 142.0 ## 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: 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: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 42 | 0.9365 | 53.4332 | 34.0477 | 36.9735 | 51.1918 | 142.0 | | No log | 2.0 | 84 | 0.9189 | 55.982 | 36.9147 | 39.1563 | 53.5959 | 142.0 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

vai6hav/wav2vec2-large-xls-r-300m-hindi-epochs15-colab

vai6hav

2022-06-18T17:42:12Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-06-18T16:56:04Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-hindi-epochs15-colab results: [] ---  # wav2vec2-large-xls-r-300m-hindi-epochs15-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 3.5705 - Wer: 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 20.2764 | 5.53 | 50 | 8.1197 | 1.0 | | 5.2964 | 11.11 | 100 | 3.5705 | 1.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

theojolliffe/bart-cnn-science-v3-e1-v4-e6-manual

theojolliffe

2022-06-18T17:37:36Z

5

0

transformers

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

text2text-generation

2022-06-18T17:14:13Z

--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-cnn-science-v3-e1-v4-e6-manual results: [] ---  # bart-cnn-science-v3-e1-v4-e6-manual This model is a fine-tuned version of [theojolliffe/bart-cnn-science-v3-e1](https://huggingface.co/theojolliffe/bart-cnn-science-v3-e1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4513 - Rouge1: 51.4471 - Rouge2: 31.5595 - Rougel: 31.7717 - Rougelsum: 49.4999 - Gen Len: 142.0 ## 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: 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: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 42 | 1.0691 | 51.1883 | 31.2479 | 33.7004 | 48.9571 | 142.0 | | No log | 2.0 | 84 | 1.0883 | 51.7634 | 29.8573 | 30.7155 | 49.3378 | 142.0 | | No log | 3.0 | 126 | 1.2355 | 52.9606 | 31.3539 | 33.5131 | 49.9275 | 142.0 | | No log | 4.0 | 168 | 1.3430 | 52.2108 | 32.7896 | 34.65 | 50.4271 | 139.1 | | No log | 5.0 | 210 | 1.3963 | 51.5335 | 30.4157 | 31.5759 | 49.6904 | 142.0 | | No log | 6.0 | 252 | 1.4513 | 51.4471 | 31.5595 | 31.7717 | 49.4999 | 142.0 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

theojolliffe/bart-cnn-science-v3-e1-v4-e4-manual

theojolliffe

2022-06-18T17:13:00Z

3

0

transformers

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

text2text-generation

2022-06-18T16:46:47Z

--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-cnn-science-v3-e1-v4-e4-manual results: [] ---  # bart-cnn-science-v3-e1-v4-e4-manual This model is a fine-tuned version of [theojolliffe/bart-cnn-science-v3-e1](https://huggingface.co/theojolliffe/bart-cnn-science-v3-e1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2615 - Rouge1: 53.36 - Rouge2: 32.0237 - Rougel: 33.2835 - Rougelsum: 50.7455 - Gen Len: 142.0 ## 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: 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: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 42 | 1.0675 | 51.743 | 31.3774 | 34.1939 | 48.7234 | 142.0 | | No log | 2.0 | 84 | 1.0669 | 49.4166 | 28.1438 | 30.188 | 46.0289 | 142.0 | | No log | 3.0 | 126 | 1.1799 | 52.6909 | 31.0174 | 35.441 | 50.0351 | 142.0 | | No log | 4.0 | 168 | 1.2615 | 53.36 | 32.0237 | 33.2835 | 50.7455 | 142.0 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

harryb0905/dqn-MountainCar-v0-1-million

harryb0905

2022-06-18T16:57:28Z

2

0

stable-baselines3

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

reinforcement-learning

2022-06-18T16:57:05Z

--- library_name: stable-baselines3 tags: - MountainCar-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: -200.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: MountainCar-v0 type: MountainCar-v0 --- # **DQN** Agent playing **MountainCar-v0** This is a trained model of a **DQN** agent playing **MountainCar-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 ... ```

theojolliffe/bart-cnn-science-v3-e2-v4-e4-manual

theojolliffe

2022-06-18T16:45:26Z

3

0

transformers

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

text2text-generation

2022-06-18T14:55:43Z

--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-cnn-science-v3-e2-v4-e4-manual results: [] ---  # bart-cnn-science-v3-e2-v4-e4-manual This model is a fine-tuned version of [theojolliffe/bart-cnn-science-v3-e2](https://huggingface.co/theojolliffe/bart-cnn-science-v3-e2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1223 - Rouge1: 50.8519 - Rouge2: 30.3314 - Rougel: 31.5149 - Rougelsum: 48.4389 - Gen Len: 142.0 ## 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: 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: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 42 | 0.9420 | 53.5234 | 33.6131 | 35.8383 | 51.1499 | 142.0 | | No log | 2.0 | 84 | 0.9439 | 52.388 | 32.1451 | 35.2339 | 49.6554 | 142.0 | | No log | 3.0 | 126 | 1.0321 | 56.2765 | 37.671 | 39.2693 | 53.5596 | 142.0 | | No log | 4.0 | 168 | 1.1223 | 50.8519 | 30.3314 | 31.5149 | 48.4389 | 142.0 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

Ambiwlans/PPO-1m-SpaceInvadersNoFrameskip-v4

Ambiwlans

2022-06-18T15:58:34Z

1

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-18T15:57:59Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 273.00 +/- 82.29 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **PPO** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **PPO** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo ppo --env SpaceInvadersNoFrameskip-v4 -orga Ambiwlans -f logs/ python enjoy.py --algo ppo --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo ppo --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo ppo --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Ambiwlans ``` ## Hyperparameters ```python OrderedDict([('batch_size', 256), ('clip_range', 'lin_0.1'), ('ent_coef', 0.01), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('frame_stack', 4), ('learning_rate', 'lin_2.5e-4'), ('n_envs', 8), ('n_epochs', 4), ('n_steps', 128), ('n_timesteps', 1000000.0), ('policy', 'CnnPolicy'), ('vf_coef', 0.5), ('normalize', False)]) ```

anibahug/mt5-small-finetuned-amazon-en-de

anibahug

2022-06-18T15:39:26Z

8

0

transformers

[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "summarization", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2022-06-18T14:20:45Z

--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-finetuned-amazon-en-de results: [] ---  # mt5-small-finetuned-amazon-en-de This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the [Amazon reviews multi](https://huggingface.co/datasets/amazon_reviews_multi) dataset. It achieves the following results on the evaluation set: - Loss: 3.2896 - Rouge1: 14.7163 - Rouge2: 6.6341 - Rougel: 14.2052 - Rougelsum: 14.2318 ## Model description the model can summarize texts for english and deutsch ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure the training was done on google colab ( using it's free GPU) ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 7.2925 | 1.0 | 1276 | 3.5751 | 13.6254 | 6.0527 | 13.109 | 13.1438 | | 4.0677 | 2.0 | 2552 | 3.4031 | 13.5907 | 6.068 | 13.3526 | 13.2471 | | 3.7458 | 3.0 | 3828 | 3.3434 | 14.7229 | 6.8482 | 14.1443 | 14.2218 | | 3.5831 | 4.0 | 5104 | 3.3353 | 14.8696 | 6.6371 | 14.1342 | 14.2907 | | 3.4841 | 5.0 | 6380 | 3.3037 | 14.233 | 6.2318 | 13.9218 | 13.9781 | | 3.4142 | 6.0 | 7656 | 3.2914 | 13.7344 | 5.9446 | 13.5476 | 13.6362 | | 3.3587 | 7.0 | 8932 | 3.2959 | 14.2007 | 6.1905 | 13.5255 | 13.5237 | | 3.3448 | 8.0 | 10208 | 3.2896 | 14.7163 | 6.6341 | 14.2052 | 14.2318 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

anibahug/marian-finetuned-kde4-en-to-ar

anibahug

2022-06-18T15:19:04Z

6

1

transformers

[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:kde4", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2022-06-17T14:21:35Z

--- license: apache-2.0 tags: - translation - generated_from_trainer datasets: - kde4 model-index: - name: marian-finetuned-kde4-en-to-ar results: [] ---  # marian-finetuned-kde4-en-to-ar This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the kde4 dataset. ## Model description if you want to learn about the model used check [Helsinki-NLP Model](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) ## Intended uses & limitations ## Training and evaluation data More information needed ## Training procedure the training was done on google colab. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - 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 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

nutjung/q-Taxi-v3

nutjung

2022-06-18T14:55:53Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-06-18T14:38:07Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="nutjung/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

S2312dal/M6_cross

S2312dal

2022-06-18T14:10:31Z

3

0

transformers

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

text-classification

2022-06-17T19:41:29Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - spearmanr model-index: - name: M6_cross results: [] ---  # M6_cross 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: 0.0084 - Pearson: 0.9811 - Spearmanr: 0.9075 ## 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: 20 - eval_batch_size: 20 - seed: 25 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 6.0 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | 0.0059 | 1.0 | 105 | 0.0158 | 0.9633 | 0.9054 | | 0.001 | 2.0 | 210 | 0.0102 | 0.9770 | 0.9103 | | 0.0008 | 3.0 | 315 | 0.0083 | 0.9805 | 0.9052 | | 0.0011 | 4.0 | 420 | 0.0075 | 0.9812 | 0.9082 | | 0.0017 | 5.0 | 525 | 0.0084 | 0.9811 | 0.9075 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

KoichiYasuoka/deberta-base-japanese-unidic

KoichiYasuoka

2022-06-18T14:02:31Z

7

0

transformers

[ "transformers", "pytorch", "deberta-v2", "fill-mask", "japanese", "masked-lm", "ja", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-08T08:05:32Z

--- language: - "ja" tags: - "japanese" - "masked-lm" license: "cc-by-sa-4.0" pipeline_tag: "fill-mask" mask_token: "[MASK]" widget: - text: "日本に着いたら[MASK]を訪ねなさい。" --- # deberta-base-japanese-unidic ## Model Description This is a DeBERTa(V2) model pre-trained on 青空文庫 texts with BertJapaneseTokenizer. You can fine-tune `deberta-base-japanese-unidic` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-unidic-luw-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-unidic-ud-head), and so on. ## How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-unidic") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/deberta-base-japanese-unidic") ``` [fugashi](https://pypi.org/project/fugashi) and [unidic-lite](https://pypi.org/project/unidic-lite) are required.

tuni/xlm-roberta-large-xnli-finetuned-mnli-SJP-v2

tuni

2022-06-18T13:48:56Z

8

0

transformers

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

text-classification

2022-06-18T11:53:32Z

--- license: mit tags: - generated_from_trainer datasets: - swiss_judgment_prediction metrics: - accuracy model-index: - name: xlm-roberta-large-xnli-finetuned-mnli-SJP-v2 results: - task: name: Text Classification type: text-classification dataset: name: swiss_judgment_prediction type: swiss_judgment_prediction args: all_languages metrics: - name: Accuracy type: accuracy value: 0.5954285714285714 ---  # xlm-roberta-large-xnli-finetuned-mnli-SJP-v2 This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the swiss_judgment_prediction dataset. It achieves the following results on the evaluation set: - Loss: 0.8093 - Accuracy: 0.5954 ## 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: 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 5 | 0.8879 | 0.5191 | | No log | 2.0 | 10 | 0.8093 | 0.5954 | | No log | 3.0 | 15 | 2.4452 | 0.3176 | | No log | 4.0 | 20 | 3.6636 | 0.3084 | | No log | 5.0 | 25 | 3.7687 | 0.3393 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

eslamxm/mbart-finetuned-fa

eslamxm

2022-06-18T13:40:54Z

14

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "summarization", "fa", "Abstractive Summarization", "generated_from_trainer", "dataset:pn_summary", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2022-06-17T00:40:11Z

--- tags: - summarization - fa - mbart - Abstractive Summarization - generated_from_trainer datasets: - pn_summary model-index: - name: mbart-finetuned-fa results: [] ---  # mbart-finetuned-fa This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the pn_summary dataset. It achieves the following results on the evaluation set: - Loss: 3.2877 - Rouge-1: 44.07 - Rouge-2: 25.81 - Rouge-l: 38.96 - Gen Len: 41.7 - Bertscore: 78.95 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 5 - label_smoothing_factor: 0.1 ### Training results ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

Muennighoff/SGPT-2.7B-weightedmean-nli-bitfit

Muennighoff

2022-06-18T13:11:04Z

7

1

sentence-transformers

[ "sentence-transformers", "pytorch", "gpt_neo", "feature-extraction", "sentence-similarity", "arxiv:2202.08904", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-03-02T23:29:04Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # SGPT-2.7B-weightedmean-nli-bitfit ## Usage For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt ## Evaluation Results For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904 ## Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 70456 with parameters: ``` {'batch_size': 8} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 7045, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 0.0002 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 7046, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: GPTNeoModel (1): Pooling({'word_embedding_dimension': 2560, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors ```bibtex @article{muennighoff2022sgpt, title={SGPT: GPT Sentence Embeddings for Semantic Search}, author={Muennighoff, Niklas}, journal={arXiv preprint arXiv:2202.08904}, year={2022} } ```

Muennighoff/SGPT-1.3B-weightedmean-nli-bitfit

Muennighoff

2022-06-18T13:04:47Z

387

0

sentence-transformers

[ "sentence-transformers", "pytorch", "gpt_neo", "feature-extraction", "sentence-similarity", "arxiv:2202.08904", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-03-02T23:29:04Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # SGPT-1.3B-weightedmean-nli-bitfit ## Usage For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt ## Evaluation Results For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904 ## Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 93941 with parameters: ``` {'batch_size': 6} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 9394, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 0.0001 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 9395, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: GPTNeoModel (1): Pooling({'word_embedding_dimension': 2048, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors ```bibtex @article{muennighoff2022sgpt, title={SGPT: GPT Sentence Embeddings for Semantic Search}, author={Muennighoff, Niklas}, journal={arXiv preprint arXiv:2202.08904}, year={2022} } ```

huggingtweets/joejoinerr

huggingtweets

2022-06-18T12:02:03Z

243

0

transformers

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

text-generation

2022-05-03T12:31:27Z

--- language: en thumbnail: http://www.huggingtweets.com/joejoinerr/1655553718810/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/1477268531561517057/MhgifvbO_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Joe 🍞</div> <div style="text-align: center; font-size: 14px;">@joejoinerr</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 Joe 🍞. | Data | Joe 🍞 | | --- | --- | | Tweets downloaded | 3176 | | Retweets | 611 | | Short tweets | 281 | | Tweets kept | 2284 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3f3589ez/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 @joejoinerr's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/35u823qi) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/35u823qi/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/joejoinerr') 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)

nestoralvaro/mt5-small-test-amazon

nestoralvaro

2022-06-18T11:51:27Z

8

0

transformers

[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "summarization", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2022-06-18T11:05:22Z

--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-test-amazon results: [] ---  # mt5-small-test-amazon 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: - Loss: 1.9515 - Rouge1: 30.3066 - Rouge2: 3.3019 - Rougel: 30.1887 - Rougelsum: 30.0314 ## 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: 5.6e-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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 10.0147 | 1.0 | 1004 | 2.9904 | 7.3703 | 0.2358 | 7.3703 | 7.4292 | | 3.4892 | 2.0 | 2008 | 2.4061 | 23.4178 | 2.4764 | 23.2901 | 23.3097 | | 2.724 | 3.0 | 3012 | 2.1630 | 26.6706 | 2.8302 | 26.6509 | 26.5723 | | 2.4395 | 4.0 | 4016 | 2.0815 | 26.7296 | 2.9481 | 26.6313 | 26.533 | | 2.2881 | 5.0 | 5020 | 2.0048 | 30.1887 | 3.3019 | 30.0708 | 29.9135 | | 2.1946 | 6.0 | 6024 | 1.9712 | 29.4811 | 2.9481 | 29.4025 | 29.3042 | | 2.1458 | 7.0 | 7028 | 1.9545 | 29.8153 | 3.3019 | 29.717 | 29.5204 | | 2.1069 | 8.0 | 8032 | 1.9515 | 30.3066 | 3.3019 | 30.1887 | 30.0314 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

Willy/bert-base-spanish-wwm-cased-finetuned-NLP-IE-2

Willy

2022-06-18T10:07:25Z

3

0

transformers

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

text-classification

2022-06-18T05:31:54Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-base-spanish-wwm-cased-finetuned-NLP-IE-2 results: [] ---  # bert-base-spanish-wwm-cased-finetuned-NLP-IE-2 This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5279 - Accuracy: 0.7836 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6008 | 1.0 | 9 | 0.5243 | 0.7836 | | 0.6014 | 2.0 | 18 | 0.5279 | 0.7836 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

c17hawke/bert-fine-tuned-cola_2

c17hawke

2022-06-18T09:40:26Z

6

0

transformers

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

text-classification

2022-06-18T09:20:05Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: bert-fine-tuned-cola_2 results: [] ---  # bert-fine-tuned-cola_2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3078 - Validation Loss: 0.4072 - Epoch: 1 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.4976 | 0.4236 | 0 | | 0.3078 | 0.4072 | 1 | ### Framework versions - Transformers 4.20.0 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

S2312dal/M4_MLM_cross

S2312dal

2022-06-18T08:48:02Z

5

0

transformers

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

text-classification

2022-06-18T08:13:26Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - spearmanr model-index: - name: M4_MLM_cross results: [] ---  # M4_MLM_cross This model is a fine-tuned version of [S2312dal/M4_MLM](https://huggingface.co/S2312dal/M4_MLM) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0222 - Pearson: 0.9472 - Spearmanr: 0.8983 ## 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: 25 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 8.0 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | 0.0353 | 1.0 | 131 | 0.0590 | 0.8326 | 0.8225 | | 0.0478 | 2.0 | 262 | 0.0368 | 0.9234 | 0.8894 | | 0.0256 | 3.0 | 393 | 0.0222 | 0.9472 | 0.8983 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

janeel/muppet-roberta-base-finetuned-squad

janeel

2022-06-18T07:57:35Z

15

2

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "dataset:squad_v2", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2022-06-18T04:37:07Z

--- license: mit tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: muppet-roberta-base-finetuned-squad results: [] ---  # muppet-roberta-base-finetuned-squad This model is a fine-tuned version of [facebook/muppet-roberta-base](https://huggingface.co/facebook/muppet-roberta-base) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.9017 ## 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 | |:-------------:|:-----:|:-----:|:---------------:| | 0.7007 | 1.0 | 8239 | 0.7905 | | 0.4719 | 2.0 | 16478 | 0.9017 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

ouiame/bert2gpt2frenchSumm

ouiame

2022-06-18T06:31:16Z

4

1

transformers

[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "autotrain", "unk", "dataset:ouiame/autotrain-data-orangesum", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-06-17T23:10:00Z

--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - ouiame/autotrain-data-orangesum co2_eq_emissions: 999.838587232387 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 1000833138 - CO2 Emissions (in grams): 999.838587232387 ## Validation Metrics - Loss: 2.4244203567504883 - Rouge1: 25.7023 - Rouge2: 8.5872 - RougeL: 18.6776 - RougeLsum: 19.821 - Gen Len: 39.732 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/ouiame/autotrain-orangesum-1000833138 ```

HHHHHHHHHHHHHHHHHHHHHHHHH/Fart

HHHHHHHHHHHHHHHHHHHHHHHHH

2022-06-18T00:49:05Z

0

null

[ "region:us" ]

null

2022-06-18T00:48:25Z

license: afl-3.0 it makes fart noise

kornosk/polibertweet-political-twitter-roberta-mlm

kornosk

2022-06-17T23:45:14Z

496

2

transformers

[ "transformers", "pytorch", "roberta", "fill-mask", "twitter", "masked-token-prediction", "bertweet", "election2020", "politics", "en", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-05-02T22:20:16Z

--- language: "en" tags: - twitter - masked-token-prediction - bertweet - election2020 - politics license: "gpl-3.0" --- # Pre-trained BERT on Twitter US Political Election 2020 Pre-trained weights for PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter, LREC 2022. Please see the [official repository](https://github.com/GU-DataLab/PoliBERTweet) for more detail. We use the initialized weights from [BERTweet](https://huggingface.co/vinai/bertweet-base) or `vinai/bertweet-base`. # Training Data This model is pre-trained on over 83 million English tweets about the 2020 US Presidential Election. # Training Objective This model is initialized with BERTweet and trained with an MLM objective. # Usage This pre-trained language model **can be fine-tunned to any downstream task (e.g. classification)**. ```python from transformers import AutoModel, AutoTokenizer, pipeline import torch # choose GPU if available device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # select mode path here pretrained_LM_path = "kornosk/polibertweet-mlm" # load model tokenizer = AutoTokenizer.from_pretrained(pretrained_LM_path) model = AutoModel.from_pretrained(pretrained_LM_path) # fill mask example = "Trump is the <mask> of USA" fill_mask = pipeline('fill-mask', model=pretrained_LM_path, tokenizer=tokenizer) outputs = fill_mask(example) print(outputs) # see embeddings inputs = tokenizer(example, return_tensors="pt") outputs = model(**inputs) print(outputs) # OR you can use this model to train on your downstream task! # please consider citing our paper if you feel this is useful :) ``` # Reference - [PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter](XXX), LREC 2022. # Citation ```bibtex @inproceedings{kawintiranon2022polibertweet, title = {PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter}, author = {Kawintiranon, Kornraphop and Singh, Lisa}, booktitle = {Proceedings of the Language Resources and Evaluation Conference}, year = {2022}, publisher = {European Language Resources Association} } ```

gemasphi/laprador_f

gemasphi

2022-06-17T21:11:03Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-06-17T21:10:48Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # gemasphi/laprador_f 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('gemasphi/laprador_f') 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('gemasphi/laprador_f') model = AutoModel.from_pretrained('gemasphi/laprador_f') # 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=gemasphi/laprador_f) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel (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

Mahmoud1816Yasser/tmp_trainer

Mahmoud1816Yasser

2022-06-17T21:10:28Z

4

0

transformers

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

audio-classification

2022-06-17T21:05:23Z

--- tags: - generated_from_trainer model-index: - name: tmp_trainer results: [] ---  # tmp_trainer This model was trained from scratch 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 - num_epochs: 3.0 ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

huggingtweets/itsamedevdev

huggingtweets

2022-06-17T20:01:28Z

3

0

transformers

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

text-generation

2022-06-17T20:01:21Z

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true 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/1502217816421941249/jOIqVIE2_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">ItAMeDevDev</div> <div style="text-align: center; font-size: 14px;">@itsamedevdev</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 ItAMeDevDev. | Data | ItAMeDevDev | | --- | --- | | Tweets downloaded | 2842 | | Retweets | 1052 | | Short tweets | 474 | | Tweets kept | 1316 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/lr4yyk0f/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 @itsamedevdev's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2advtlvo) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2advtlvo/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/itsamedevdev') 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)

huggingtweets/pdchina

huggingtweets

2022-06-17T18:03:08Z

3

0

transformers

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

text-generation

2022-06-17T18:01:23Z

--- language: en thumbnail: http://www.huggingtweets.com/pdchina/1655488982839/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/1246469365089939456/jAjE_fKB_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">People's Daily, China</div> <div style="text-align: center; font-size: 14px;">@pdchina</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 People's Daily, China. | Data | People's Daily, China | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 20 | | Short tweets | 2 | | Tweets kept | 3228 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3b8is5jg/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 @pdchina's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3rg0kmkg) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3rg0kmkg/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/pdchina') 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)

eslamxm/MBart-finetuned-ur-xlsum

eslamxm

2022-06-17T14:59:58Z

30

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "summarization", "ur", "seq2seq", "Abstractive Summarization", "generated_from_trainer", "dataset:xlsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2022-06-16T23:41:23Z

--- tags: - summarization - ur - seq2seq - mbart - Abstractive Summarization - generated_from_trainer datasets: - xlsum model-index: - name: MBart-finetuned-ur-xlsum results: [] ---  # MBart-finetuned-ur-xlsum This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 3.2663 - Rouge-1: 40.6 - Rouge-2: 18.9 - Rouge-l: 34.39 - Gen Len: 37.88 - Bertscore: 77.06 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 5 - label_smoothing_factor: 0.1 ### Training results ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

wiselinjayajos/finetuned-bert-mrpc

wiselinjayajos

2022-06-17T14:58:18Z

6

0

transformers

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

text-classification

2022-06-17T12:08:42Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: finetuned-bert-mrpc results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8455882352941176 - name: F1 type: f1 value: 0.8908145580589255 ---  # finetuned-bert-mrpc This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4755 - Accuracy: 0.8456 - F1: 0.8908 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure Trained on my local laptop and the training time took 3 hours. ### 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5331 | 1.0 | 230 | 0.3837 | 0.8505 | 0.8943 | | 0.3023 | 2.0 | 460 | 0.3934 | 0.8505 | 0.8954 | | 0.1472 | 3.0 | 690 | 0.4755 | 0.8456 | 0.8908 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

efederici/convnext-base-224-22k-1k-orig-cats-vs-dogs

efederici

2022-06-17T14:11:20Z

56

0

transformers

[ "transformers", "pytorch", "tensorboard", "convnext", "image-classification", "vision", "dataset:cats_vs_dogs", "arxiv:2201.03545", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-06-17T09:33:45Z

--- license: apache-2.0 tags: - image-classification - vision datasets: - cats_vs_dogs metrics: - accuracy model-index: - name: convnext-base-224-22k-1k-orig-cats-vs-dogs results: - task: name: Image Classification type: image-classification dataset: name: cats_vs_dogs type: cats_vs_dogs args: default metrics: - name: Accuracy type: accuracy value: 0.9973333333333333 --- # convnext-base-224-22k-1k-orig-cats-vs-dogs This model is a fine-tuned version of [facebook/convnext-base-224-22k-1k](https://huggingface.co/facebook/convnext-base-224-22k-1k) on the cats_vs_dogs dataset. It achieves the following results on the evaluation set: - Loss: 0.0103 - Accuracy: 0.9973 <p align="center"> <img src="https://files.ocula.com/anzax/09/09f77133-7740-4130-a567-84fb56736362_650_544.jpg" width="600"> </br> Jockum Nordström, Cat Dog Cat, 2016 </p> ## Model description The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2201-03545, author = {Zhuang Liu and Hanzi Mao and Chao{-}Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie}, title = {A ConvNet for the 2020s}, journal = {CoRR}, volume = {abs/2201.03545}, year = {2022}, url = {https://arxiv.org/abs/2201.03545}, eprinttype = {arXiv}, eprint = {2201.03545}, timestamp = {Thu, 20 Jan 2022 14:21:35 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

backnotprop/informative-drawings-image-to-opensketch-onnx

backnotprop

2022-06-17T14:08:31Z

0

null

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

null

2022-06-17T14:07:35Z

--- license: mit --- All credit to this repo: https://huggingface.co/spaces/carolineec/informativedrawings

huggingtweets/aiww-bbcworld-elonmusk

huggingtweets

2022-06-17T14:04:23Z

3

0

transformers

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

text-generation

2022-06-17T14:04:15Z

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true 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/1529956155937759233/Nyn1HZWF_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/1529107170448523264/q3VwEx38_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/2972716369/e27a35486a2ec507063cb19c89e3ce82_400x400.jpeg')"> </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 & BBC News (World) & 艾未未 Ai Weiwei</div> <div style="text-align: center; font-size: 14px;">@aiww-bbcworld-elonmusk</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 & BBC News (World) & 艾未未 Ai Weiwei. | Data | Elon Musk | BBC News (World) | 艾未未 Ai Weiwei | | --- | --- | --- | --- | | Tweets downloaded | 3200 | 3250 | 3243 | | Retweets | 145 | 240 | 680 | | Short tweets | 966 | 0 | 2116 | | Tweets kept | 2089 | 3010 | 447 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1xg6gwun/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 @aiww-bbcworld-elonmusk's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3f692l8n) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3f692l8n/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/aiww-bbcworld-elonmusk') 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)

classla/bcms-bertic-parlasent-bcs-bi

classla

2022-06-17T13:51:54Z

11

0

transformers

[ "transformers", "pytorch", "electra", "text-classification", "sentiment-analysis", "hr", "arxiv:2206.00929", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-01T09:10:17Z

--- language: "hr" tags: - text-classification - sentiment-analysis widget: - text: "Poštovani potpredsjedničke Vlade i ministre hrvatskih branitelja, mislite li da ste zapravo iznevjerili svoje suborce s kojima ste 555 dana prosvjedovali u šatoru protiv tadašnjih dužnosnika jer ste zapravo donijeli zakon koji je neprovediv, a birali ste si suradnike koji nemaju etički integritet." --- # bcms-bertic-parlasent-bcs-bi Binary text classification model based on [`classla/bcms-bertic`](https://huggingface.co/classla/bcms-bertic) and fine-tuned on the BCS Political Sentiment dataset (sentence-level data). This classifier classifies text into only two categories: Negative vs. Other. For the ternary classifier (Negative, Neutral, Positive) check [this model](https://huggingface.co/classla/bcms-bertic-parlasent-bcs-ter). For details on the dataset and the finetuning procedure, please see [this paper](https://arxiv.org/abs/2206.00929). ## Fine-tuning hyperparameters Fine-tuning was performed with `simpletransformers`. Beforehand a brief sweep for the optimal number of epochs was performed and the presumed best value was 9. Other arguments were kept default. ```python model_args = { "num_train_epochs": 9 } ``` ## Performance in comparison with ternary classifier | model | average macro F1 | |-------------------------------------------|------------------| | bcms-bertic-parlasent-bcs-ter | 0.7941 ± 0.0101 | | bcms-bertic-parlasent-bcs-bi (this model) | 0.8999 ± 0.012 | ## Use example with `simpletransformers==0.63.7` ```python from simpletransformers.classification import ClassificationModel model = ClassificationModel("electra", "classla/bcms-bertic-parlasent-bcs-bi") predictions, logits = model.predict([ "Đački autobusi moraju da voze svaki dan", "Vi niste normalni" ] ) predictions # Output: array([1, 0]) [model.config.id2label[i] for i in predictions] # Output: ['Other', 'Negative'] ``` ## Citation If you use the model, please cite the following paper on which the original model is based: ``` @inproceedings{ljubesic-lauc-2021-bertic, title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian", author = "Ljube{\v{s}}i{\'c}, Nikola and Lauc, Davor", booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", month = apr, year = "2021", address = "Kiyv, Ukraine", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.bsnlp-1.5", pages = "37--42", } ``` and the paper describing the dataset and methods for the current finetuning: ``` @misc{https://doi.org/10.48550/arxiv.2206.00929, doi = {10.48550/ARXIV.2206.00929}, url = {https://arxiv.org/abs/2206.00929}, author = {Mochtak, Michal and Rupnik, Peter and Ljubešič, Nikola}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {The ParlaSent-BCS dataset of sentiment-annotated parliamentary debates from Bosnia-Herzegovina, Croatia, and Serbia}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution Share Alike 4.0 International} } ```

frollo/word2vec-for-crime-categorization

frollo

2022-06-17T13:49:03Z

0

1

null

[ "license:cc0-1.0", "region:us" ]

null

2022-06-17T13:45:21Z

--- license: cc0-1.0 --- Word2Vec model obtained by training the model of [1] on a dataset of 17,500 Italian news articles related to crime events [1] Di Gennaro G., Buonanno A., Di Girolamo A., Ospedale A., Palmieri F.A.N., Fedele G. (2021) An Analysis of Word2Vec for the Italian Language. In: Esposito A., Faundez-Zanuy M., Morabito F., Pasero E. (eds) Progresses in Artificial Intelligence and Neural Systems. Smart Innovation, Systems and Technologies, vol 184. Springer, Singapore. https://doi.org/10.1007/978-981-15-5093-5_13 **If the dataset is useful, please consider citing paper using the BibTex entry below.** ``` @inproceedings{bonisoli2021fedcsis, author = {Giovanni Bonisoli and Federica Rollo and Laura Po}, editor = {Maria Ganzha and Leszek A. Maciaszek and Marcin Paprzycki and Dominik Slezak}, title = {Using Word Embeddings for Italian Crime News Categorization}, booktitle = {Proceedings of the 16th Conference on Computer Science and Intelligence Systems, Online, September 2-5, 2021}, pages = {461--470}, year = {2021}, url = {https://doi.org/10.15439/2021F118}, doi = {10.15439/2021F118} } ```

joitandr/dqn-SpaceInvadersNoFrameskip-v4

joitandr

2022-06-17T12:56:51Z

2

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-17T12:56:12Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 597.50 +/- 100.68 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga joitandr -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga joitandr ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

Guillaume63/q-Taxi-v3

Guillaume63

2022-06-17T12:24:35Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-06-17T12:24:27Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Guillaume63/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

mosesju/distilbert-base-uncased-finetuned-news

mosesju

2022-06-17T12:14:46Z

12

0

transformers

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

text-classification

2022-06-14T20:16:59Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - ag_news metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-news results: - task: name: Text Classification type: text-classification dataset: name: ag_news type: ag_news args: default metrics: - name: Accuracy type: accuracy value: 0.9388157894736842 - name: F1 type: f1 value: 0.9388275184627893 ---  # distilbert-base-uncased-finetuned-news This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset. It achieves the following results on the evaluation set: - Loss: 0.2117 - Accuracy: 0.9388 - F1: 0.9388 ## Model description This model is intended to categorize news headlines into one of four categories; World, Sports, Science & Technology, or Business ## Intended uses & limitations The model is limited by the training data it used. If you use the model with a news story that falls outside of the four intended categories, it produces quite confused results. ## 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: 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2949 | 1.0 | 3750 | 0.2501 | 0.9262 | 0.9261 | | 0.1569 | 2.0 | 7500 | 0.2117 | 0.9388 | 0.9388 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

huggingtweets/techreview

huggingtweets

2022-06-17T09:38:07Z

3

1

transformers

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

text-generation

2022-06-17T09:28:26Z

--- language: en thumbnail: http://www.huggingtweets.com/techreview/1655458683048/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/1072880528712495106/ahuQUlOb_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">MIT Technology Review</div> <div style="text-align: center; font-size: 14px;">@techreview</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 MIT Technology Review. | Data | MIT Technology Review | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 293 | | Short tweets | 1 | | Tweets kept | 2956 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1zbwqwsb/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 @techreview's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2bzg3pev) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2bzg3pev/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/techreview') 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)

rajendra-ml/q-Taxi-v3

rajendra-ml

2022-06-17T09:22:13Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-06-17T09:22:03Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="rajendra-ml/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

huggingtweets/iantdr

huggingtweets

2022-06-17T09:09:33Z

3

0

transformers

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

text-generation

2022-06-17T09:09:26Z

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true 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/1365703183/YT_Croydon_Flyer_twitter_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">ian anderson</div> <div style="text-align: center; font-size: 14px;">@iantdr</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 ian anderson. | Data | ian anderson | | --- | --- | | Tweets downloaded | 3201 | | Retweets | 2052 | | Short tweets | 316 | | Tweets kept | 833 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1bopfm9o/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 @iantdr's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1papgk0r) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1papgk0r/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/iantdr') 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)

S2312dal/M8_MLM

S2312dal

2022-06-17T08:55:06Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "albert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-17T08:52:59Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: M8_MLM results: [] ---  # M8_MLM This model is a fine-tuned version of [sentence-transformers/paraphrase-albert-small-v2](https://huggingface.co/sentence-transformers/paraphrase-albert-small-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 8.9140 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 9.5021 | 1.0 | 25 | 9.1463 | | 9.0507 | 2.0 | 50 | 8.9504 | | 8.9528 | 3.0 | 75 | 8.9148 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

S2312dal/M7_MLM

S2312dal

2022-06-17T08:49:00Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-17T08:40:50Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: M7_MLM results: [] ---  # M7_MLM This model is a fine-tuned version of [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 8.2304 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 9.2227 | 1.0 | 25 | 8.6091 | | 8.6536 | 2.0 | 50 | 8.2492 | | 8.5065 | 3.0 | 75 | 8.3056 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

S2312dal/M5_MLM

S2312dal

2022-06-17T08:25:48Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "deberta-v2", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-17T08:02:01Z

--- license: mit tags: - generated_from_trainer model-index: - name: M5_MLM results: [] ---  # M5_MLM 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: 7.0447 ## 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: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 9.8279 | 1.0 | 62 | 7.9889 | | 7.7536 | 2.0 | 124 | 7.3750 | | 7.2065 | 3.0 | 186 | 6.8625 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

ArthurZ/tiny-random-bert-sharded

ArthurZ

2022-06-17T08:07:42Z

5,243

0

transformers

[ "transformers", "tf", "bert", "feature-extraction", "generated_from_keras_callback", "endpoints_compatible", "region:us" ]

feature-extraction

2022-06-17T07:49:01Z

--- tags: - generated_from_keras_callback model-index: - name: tiny-random-bert-sharded results: [] ---  # tiny-random-bert-sharded This model was trained from scratch 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 4.21.0.dev0 - TensorFlow 2.9.0 - Datasets 2.2.2 - Tokenizers 0.12.1

marcomameli01/segformer-b0-finetuned-segments-gear2

marcomameli01

2022-06-17T08:03:25Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "segformer", "vision", "gear-segmentation", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2022-06-17T07:37:58Z

--- license: apache-2.0 tags: - vision - gear-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-segments-gear2 results: [] ---  # segformer-b0-finetuned-segments-gear2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the marcomameli01/gear dataset. It achieves the following results on the evaluation set: - Loss: 0.1268 - Mean Iou: 0.1254 - Mean Accuracy: 0.2509 - Overall Accuracy: 0.2509 - Per Category Iou: [0.0, 0.2508641975308642] - Per Category Accuracy: [nan, 0.2508641975308642] ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------:|:--------------------------:| | 0.4614 | 5.0 | 20 | 0.4427 | 0.0741 | 0.1481 | 0.1481 | [0.0, 0.14814814814814814] | [nan, 0.14814814814814814] | | 0.3327 | 10.0 | 40 | 0.2933 | 0.1726 | 0.3453 | 0.3453 | [0.0, 0.34528395061728395] | [nan, 0.34528395061728395] | | 0.2305 | 15.0 | 60 | 0.2244 | 0.0382 | 0.0763 | 0.0763 | [0.0, 0.07634567901234568] | [nan, 0.07634567901234568] | | 0.2011 | 20.0 | 80 | 0.2130 | 0.0374 | 0.0748 | 0.0748 | [0.0, 0.07476543209876543] | [nan, 0.07476543209876543] | | 0.1846 | 25.0 | 100 | 0.1672 | 0.1037 | 0.2073 | 0.2073 | [0.0, 0.20730864197530866] | [nan, 0.20730864197530866] | | 0.1622 | 30.0 | 120 | 0.1532 | 0.0805 | 0.1611 | 0.1611 | [0.0, 0.1610864197530864] | [nan, 0.1610864197530864] | | 0.139 | 35.0 | 140 | 0.1396 | 0.0971 | 0.1942 | 0.1942 | [0.0, 0.19417283950617284] | [nan, 0.19417283950617284] | | 0.1342 | 40.0 | 160 | 0.1283 | 0.0748 | 0.1496 | 0.1496 | [0.0, 0.14962962962962964] | [nan, 0.14962962962962964] | | 0.128 | 45.0 | 180 | 0.1224 | 0.1128 | 0.2256 | 0.2256 | [0.0, 0.22558024691358025] | [nan, 0.22558024691358025] | | 0.1243 | 50.0 | 200 | 0.1268 | 0.1254 | 0.2509 | 0.2509 | [0.0, 0.2508641975308642] | [nan, 0.2508641975308642] | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53

gary109

2022-06-17T07:30:08Z

3

1

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-06-15T08:57:00Z

--- license: apache-2.0 tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer model-index: - name: ai-light-dance_singing_ft_wav2vec2-large-xlsr-53 results: [] ---  # ai-light-dance_singing_ft_wav2vec2-large-xlsr-53 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING dataset. It achieves the following results on the evaluation set: - Loss: 0.4327 - Wer: 0.2043 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.4089 | 1.0 | 552 | 1.4750 | 0.9054 | | 0.7995 | 2.0 | 1104 | 0.9044 | 0.6163 | | 0.6232 | 3.0 | 1656 | 0.6645 | 0.3980 | | 0.5351 | 4.0 | 2208 | 0.5674 | 0.3120 | | 0.472 | 5.0 | 2760 | 0.5167 | 0.2579 | | 0.3913 | 6.0 | 3312 | 0.4553 | 0.2335 | | 0.3306 | 7.0 | 3864 | 0.4476 | 0.2114 | | 0.3028 | 8.0 | 4416 | 0.4327 | 0.2043 | | 0.317 | 9.0 | 4968 | 0.4355 | 0.2033 | | 0.2494 | 10.0 | 5520 | 0.4405 | 0.2022 | ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.1.dev0 - Tokenizers 0.12.1

gary109/wikitext_roberta-base

gary109

2022-06-17T06:44:42Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "dataset:wikitext", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-17T03:50:05Z

--- license: mit tags: - generated_from_trainer datasets: - wikitext metrics: - accuracy model-index: - name: wikitext_roberta-base results: - task: name: Masked Language Modeling type: fill-mask dataset: name: wikitext wikitext-2-raw-v1 type: wikitext args: wikitext-2-raw-v1 metrics: - name: Accuracy type: accuracy value: 0.7371052344006119 ---  # wikitext_roberta-base This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the wikitext wikitext-2-raw-v1 dataset. It achieves the following results on the evaluation set: - Loss: 1.2143 - Accuracy: 0.7371 ## 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4175 | 0.99 | 37 | 1.3355 | 0.7194 | | 1.438 | 1.99 | 74 | 1.2953 | 0.7249 | | 1.4363 | 2.99 | 111 | 1.2759 | 0.7276 | | 1.3391 | 3.99 | 148 | 1.2904 | 0.7252 | | 1.3741 | 4.99 | 185 | 1.2621 | 0.7290 | | 1.2771 | 5.99 | 222 | 1.2312 | 0.7353 | | 1.287 | 6.99 | 259 | 1.2542 | 0.7289 | | 1.29 | 7.99 | 296 | 1.2290 | 0.7345 | | 1.2948 | 8.99 | 333 | 1.2537 | 0.7286 | | 1.2741 | 9.99 | 370 | 1.2199 | 0.7354 | | 1.2342 | 10.99 | 407 | 1.2520 | 0.7309 | | 1.2199 | 11.99 | 444 | 1.2738 | 0.7260 | | 1.206 | 12.99 | 481 | 1.2286 | 0.7335 | | 1.221 | 13.99 | 518 | 1.2421 | 0.7327 | | 1.2062 | 14.99 | 555 | 1.2402 | 0.7328 | | 1.2305 | 15.99 | 592 | 1.2473 | 0.7308 | | 1.2426 | 16.99 | 629 | 1.2250 | 0.7318 | | 1.2096 | 17.99 | 666 | 1.2186 | 0.7353 | | 1.1961 | 18.99 | 703 | 1.2214 | 0.7361 | | 1.2136 | 19.99 | 740 | 1.2506 | 0.7311 | ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1

Sanjeev49/marian-finetuned-kde4-en-to-fr

Sanjeev49

2022-06-17T06:31:10Z

3

0

transformers

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

text2text-generation

2022-06-15T12:07:09Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Sanjeev49/marian-finetuned-kde4-en-to-fr results: [] ---  # Sanjeev49/marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0601 - Validation Loss: 0.8952 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 5912, '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 | |:----------:|:---------------:|:-----:| | 1.0601 | 0.8952 | 0 | ### Framework versions - Transformers 4.20.0 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

anithapappu/wav2vec2-base-timit-google-colab

anithapappu

2022-06-17T03:05:35Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-05-23T19:00:34Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-google-colab results: [] ---  # wav2vec2-base-timit-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5506 - Wer: 0.3355 ## 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: 0.0001 - 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_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.4326 | 1.0 | 500 | 1.5832 | 1.0063 | | 0.8235 | 2.01 | 1000 | 0.5310 | 0.5134 | | 0.4224 | 3.01 | 1500 | 0.4488 | 0.4461 | | 0.2978 | 4.02 | 2000 | 0.4243 | 0.4191 | | 0.232 | 5.02 | 2500 | 0.4532 | 0.4149 | | 0.1902 | 6.02 | 3000 | 0.4732 | 0.3912 | | 0.1628 | 7.03 | 3500 | 0.4807 | 0.3868 | | 0.1437 | 8.03 | 4000 | 0.5295 | 0.3670 | | 0.1241 | 9.04 | 4500 | 0.4602 | 0.3810 | | 0.1206 | 10.04 | 5000 | 0.4691 | 0.3783 | | 0.0984 | 11.04 | 5500 | 0.4500 | 0.3710 | | 0.0929 | 12.05 | 6000 | 0.5247 | 0.3550 | | 0.0914 | 13.05 | 6500 | 0.5546 | 0.3821 | | 0.0742 | 14.06 | 7000 | 0.4874 | 0.3646 | | 0.0729 | 15.06 | 7500 | 0.5327 | 0.3934 | | 0.0663 | 16.06 | 8000 | 0.5769 | 0.3661 | | 0.0575 | 17.07 | 8500 | 0.5191 | 0.3524 | | 0.0588 | 18.07 | 9000 | 0.5155 | 0.3360 | | 0.0456 | 19.08 | 9500 | 0.5135 | 0.3539 | | 0.0444 | 20.08 | 10000 | 0.5380 | 0.3603 | | 0.0419 | 21.08 | 10500 | 0.5275 | 0.3467 | | 0.0366 | 22.09 | 11000 | 0.5072 | 0.3487 | | 0.0331 | 23.09 | 11500 | 0.5450 | 0.3437 | | 0.0345 | 24.1 | 12000 | 0.5138 | 0.3431 | | 0.029 | 25.1 | 12500 | 0.5067 | 0.3413 | | 0.0274 | 26.1 | 13000 | 0.5421 | 0.3422 | | 0.0243 | 27.11 | 13500 | 0.5456 | 0.3392 | | 0.0226 | 28.11 | 14000 | 0.5665 | 0.3368 | | 0.0216 | 29.12 | 14500 | 0.5506 | 0.3355 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 1.13.3 - Tokenizers 0.12.1

sun1638650145/q-FrozenLake-v1-4x4-noSlippery

sun1638650145

2022-06-17T03:02:12Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-06-17T03:02:00Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # 使用**Q-Learning**智能体来玩**FrozenLake-v1** 这是一个使用**Q-Learning**训练有素的模型玩**FrozenLake-v1**. ## 用法 ```python model = load_from_hub(repo_id='sun1638650145/q-FrozenLake-v1-4x4-noSlippery', filename='q-learning.pkl') # 不要忘记检查是否需要添加额外的参数(例如is_slippery=False) env = gym.make(model['env_id']) evaluate_agent(env, model['max_steps'], model['n_eval_episodes'], model['qtable'], model['eval_seed']) ```

ouiame/autotrain-Robertatogpt2-995132944

ouiame

2022-06-17T01:09:06Z

4

0

transformers

[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "autotrain", "unk", "dataset:ouiame/autotrain-data-Robertatogpt2", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-06-16T20:14:06Z

--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - ouiame/autotrain-data-Robertatogpt2 co2_eq_emissions: 611.0958349328379 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 995132944 - CO2 Emissions (in grams): 611.0958349328379 ## Validation Metrics - Loss: 3.8850467205047607 - Rouge1: 16.6344 - Rouge2: 2.9899 - RougeL: 13.5872 - RougeLsum: 13.9042 - Gen Len: 20.0 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/ouiame/autotrain-Robertatogpt2-995132944 ```

huggingtweets/tomcruise

huggingtweets

2022-06-17T01:00:02Z

3

0

transformers

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

text-generation

2022-06-17T00:59:56Z

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true 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/603269306026106880/42CwEF4n_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 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 BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Tom Cruise</div> <div style="text-align: center; font-size: 14px;">@tomcruise</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 Tom Cruise. | Data | Tom Cruise | | --- | --- | | Tweets downloaded | 3036 | | Retweets | 1055 | | Short tweets | 88 | | Tweets kept | 1893 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ppnkvd5o/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 @tomcruise's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2q772s43) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2q772s43/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/tomcruise') 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)