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 2025-08-31 18:27:20	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 530 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-08-31 18:27:03	card stringlengths 11 1.01M
dt-and-vanilla-ardt/ardt-vanilla-arrl_sgld_train_halfcheetah_high-2508_0900-99	dt-and-vanilla-ardt	2023-08-25T10:09:44Z	31	0	transformers	[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2023-08-25T08:01:50Z	--- tags: - generated_from_trainer model-index: - name: ardt-vanilla-arrl_sgld_train_halfcheetah_high-2508_0900-99 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. --> # ardt-vanilla-arrl_sgld_train_halfcheetah_high-2508_0900-99 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
bigmorning/whisper_syl_cv12_pad_lob100__0055	bigmorning	2023-08-25T10:03:47Z	59	0	transformers	[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-25T10:03:38Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_syl_cv12_pad_lob100__0055 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. --> # whisper_syl_cv12_pad_lob100__0055 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0017 - Train Accuracy: 0.0362 - Train Wermet: 0.3475 - Validation Loss: 0.6087 - Validation Accuracy: 0.0238 - Validation Wermet: 0.2213 - Epoch: 54 ## 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 \| Train Accuracy \| Train Wermet \| Validation Loss \| Validation Accuracy \| Validation Wermet \| Epoch \| \|:----------:\|:--------------:\|:------------:\|:---------------:\|:-------------------:\|:-----------------:\|:-----:\| \| 5.0233 \| 0.0115 \| 1.6383 \| 3.8616 \| 0.0117 \| 0.9516 \| 0 \| \| 4.4412 \| 0.0127 \| 0.8560 \| 3.5410 \| 0.0125 \| 0.8971 \| 1 \| \| 4.0719 \| 0.0138 \| 0.8366 \| 3.2944 \| 0.0132 \| 0.8706 \| 2 \| \| 3.8091 \| 0.0146 \| 0.8133 \| 3.1691 \| 0.0134 \| 0.8487 \| 3 \| \| 3.6239 \| 0.0152 \| 0.7866 \| 3.0647 \| 0.0136 \| 0.8282 \| 4 \| \| 3.4749 \| 0.0156 \| 0.7589 \| 2.9835 \| 0.0139 \| 0.8049 \| 5 \| \| 3.3444 \| 0.0161 \| 0.7359 \| 2.9351 \| 0.0140 \| 0.7979 \| 6 \| \| 3.2215 \| 0.0165 \| 0.7138 \| 2.8468 \| 0.0145 \| 0.7589 \| 7 \| \| 3.0754 \| 0.0172 \| 0.6873 \| 2.7530 \| 0.0148 \| 0.7413 \| 8 \| \| 2.8713 \| 0.0181 \| 0.6484 \| 2.5226 \| 0.0157 \| 0.7017 \| 9 \| \| 2.5469 \| 0.0197 \| 0.5934 \| 2.1931 \| 0.0168 \| 0.6285 \| 10 \| \| 2.0233 \| 0.0225 \| 0.4997 \| 1.6411 \| 0.0189 \| 0.5215 \| 11 \| \| 1.3808 \| 0.0264 \| 0.3852 \| 1.2401 \| 0.0205 \| 0.4238 \| 12 \| \| 0.9722 \| 0.0290 \| 0.3123 \| 1.0195 \| 0.0215 \| 0.3682 \| 13 \| \| 0.7388 \| 0.0305 \| 0.2828 \| 0.8773 \| 0.0221 \| 0.3322 \| 14 \| \| 0.5787 \| 0.0317 \| 0.2751 \| 0.7970 \| 0.0225 \| 0.3083 \| 15 \| \| 0.4642 \| 0.0325 \| 0.2878 \| 0.7315 \| 0.0227 \| 0.2964 \| 16 \| \| 0.3752 \| 0.0332 \| 0.4217 \| 0.6897 \| 0.0229 \| 0.3297 \| 17 \| \| 0.3042 \| 0.0338 \| 0.7294 \| 0.6572 \| 0.0231 \| 0.4453 \| 18 \| \| 0.2444 \| 0.0343 \| 1.1298 \| 0.6369 \| 0.0232 \| 0.6637 \| 19 \| \| 0.1949 \| 0.0348 \| 1.6370 \| 0.6180 \| 0.0233 \| 1.6119 \| 20 \| \| 0.1544 \| 0.0352 \| 1.6151 \| 0.6149 \| 0.0233 \| 1.6843 \| 21 \| \| 0.1212 \| 0.0355 \| 1.3832 \| 0.6066 \| 0.0233 \| 0.8721 \| 22 \| \| 0.0931 \| 0.0357 \| 1.2799 \| 0.6034 \| 0.0234 \| 0.5109 \| 23 \| \| 0.0725 \| 0.0359 \| 1.0940 \| 0.6102 \| 0.0234 \| 1.0111 \| 24 \| \| 0.0551 \| 0.0361 \| 1.2865 \| 0.6000 \| 0.0234 \| 1.1393 \| 25 \| \| 0.0411 \| 0.0361 \| 1.8511 \| 0.6037 \| 0.0235 \| 2.0574 \| 26 \| \| 0.0311 \| 0.0362 \| 1.7179 \| 0.6018 \| 0.0235 \| 1.4847 \| 27 \| \| 0.0253 \| 0.0362 \| 0.9801 \| 0.6010 \| 0.0235 \| 0.4457 \| 28 \| \| 0.0231 \| 0.0362 \| 0.9376 \| 0.6046 \| 0.0235 \| 0.9247 \| 29 \| \| 0.0196 \| 0.0362 \| 0.6466 \| 0.6078 \| 0.0235 \| 0.5271 \| 30 \| \| 0.0177 \| 0.0362 \| 0.4041 \| 0.6155 \| 0.0235 \| 0.4352 \| 31 \| \| 0.0139 \| 0.0362 \| 0.4202 \| 0.6037 \| 0.0236 \| 0.5585 \| 32 \| \| 0.0137 \| 0.0362 \| 0.8151 \| 0.6015 \| 0.0236 \| 1.8476 \| 33 \| \| 0.0122 \| 0.0362 \| 3.4515 \| 0.6043 \| 0.0236 \| 3.8210 \| 34 \| \| 0.0098 \| 0.0362 \| 1.1787 \| 0.5985 \| 0.0236 \| 0.8094 \| 35 \| \| 0.0071 \| 0.0362 \| 0.9920 \| 0.5992 \| 0.0236 \| 0.8755 \| 36 \| \| 0.0055 \| 0.0362 \| 2.4665 \| 0.6047 \| 0.0236 \| 2.0127 \| 37 \| \| 0.0124 \| 0.0362 \| 4.2468 \| 0.6089 \| 0.0236 \| 2.8886 \| 38 \| \| 0.0109 \| 0.0362 \| 2.0177 \| 0.6097 \| 0.0236 \| 0.3417 \| 39 \| \| 0.0073 \| 0.0362 \| 0.9927 \| 0.6057 \| 0.0237 \| 2.5519 \| 40 \| \| 0.0080 \| 0.0362 \| 1.7341 \| 0.6099 \| 0.0236 \| 1.3119 \| 41 \| \| 0.0063 \| 0.0362 \| 2.4288 \| 0.6058 \| 0.0237 \| 1.3465 \| 42 \| \| 0.0038 \| 0.0362 \| 1.4535 \| 0.6022 \| 0.0237 \| 1.6804 \| 43 \| \| 0.0028 \| 0.0362 \| 2.2629 \| 0.6001 \| 0.0238 \| 3.4388 \| 44 \| \| 0.0021 \| 0.0362 \| 3.5877 \| 0.6018 \| 0.0238 \| 2.6165 \| 45 \| \| 0.0017 \| 0.0362 \| 3.0080 \| 0.6043 \| 0.0238 \| 2.6827 \| 46 \| \| 0.0061 \| 0.0362 \| 2.5182 \| 0.6545 \| 0.0235 \| 0.2316 \| 47 \| \| 0.0126 \| 0.0362 \| 0.2097 \| 0.6206 \| 0.0236 \| 0.6194 \| 48 \| \| 0.0071 \| 0.0362 \| 0.3045 \| 0.6047 \| 0.0237 \| 0.7476 \| 49 \| \| 0.0053 \| 0.0362 \| 1.2045 \| 0.6010 \| 0.0238 \| 0.6553 \| 50 \| \| 0.0040 \| 0.0362 \| 0.2626 \| 0.5964 \| 0.0238 \| 0.7027 \| 51 \| \| 0.0021 \| 0.0362 \| 0.5023 \| 0.5950 \| 0.0238 \| 0.3812 \| 52 \| \| 0.0014 \| 0.0362 \| 0.7108 \| 0.6233 \| 0.0237 \| 1.4647 \| 53 \| \| 0.0017 \| 0.0362 \| 0.3475 \| 0.6087 \| 0.0238 \| 0.2213 \| 54 \| ### Framework versions - Transformers 4.33.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
jjimdark/distilbert-base-uncased-finetuned-cola	jjimdark	2023-08-25T10:02:06Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-24T04:35:29Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5290369945616428 --- <!-- 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-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5219 - Matthews Correlation: 0.5290 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| No log \| 1.0 \| 268 \| 0.5025 \| 0.4154 \| \| 0.4551 \| 2.0 \| 536 \| 0.5071 \| 0.4792 \| \| 0.4551 \| 3.0 \| 804 \| 0.5219 \| 0.5290 \| \| 0.2312 \| 4.0 \| 1072 \| 0.6287 \| 0.5089 \| \| 0.2312 \| 5.0 \| 1340 \| 0.6631 \| 0.5182 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
amritansh/replit_3B	amritansh	2023-08-25T09:56:50Z	5	0	transformers	[ "transformers", "mpt", "text-generation", "custom_code", "license:other", "autotrain_compatible", "region:us" ]	text-generation	2023-08-25T09:51:52Z	--- license: other --- --- This is a [ggml](https://github.com/ggerganov/ggml/) quantized version of [Replit-v2-CodeInstruct-3B](https://huggingface.co/teknium/Replit-v2-CodeInstruct-3B). Quantized to 4bit -> q4_1. To run inference you can use ggml directly or [ctransformers](https://github.com/marella/ctransformers). - Memory usage of model: 2GB~ - Repo to run the model using ctransformers: https://github.com/abacaj/replit-3B-inference
arroyadr/speecht5_finetuned_voxpopuli_it	arroyadr	2023-08-25T09:53:57Z	79	0	transformers	[ "transformers", "pytorch", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	text-to-speech	2023-08-25T08:40:42Z	--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer - text-to-speech datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_it results: - task: name: text-to-speech type: text-to-speech dataset: name: VOXPOPULI type: facebook/voxpopuli config: it split: train args: all metrics: - name: MSE type: mse value: 0.5028 --- <!-- 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. --> # speecht5_finetuned_voxpopuli_it This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.5028 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - 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: 50 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5198 \| 31.37 \| 1000 \| 0.5028 \| ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
softaken/Softaken-OLM-to-PST-Converter	softaken	2023-08-25T09:46:47Z	0	0	null	[ "region:us" ]	null	2023-08-25T09:15:59Z	Softaken OLM to PST Converter Software is the best and most advanced software to convert Windows Mac OLM emails to Outlook PST file format. All users do not need to install other software to convert OLM files to the PST file format. Both professional and non-professional users can also use this tool to convert OLM files to the PST file format. The computer application immediately converts OLM files to Outlook PST file format without taking a long time or losing any single file. Users can load any size file or folder that they want to convert to PST file format without any data limitations. Users can use this tool in any Windows version, such as Windows 11, 10, 8.1, 8, 7, Vista, XP, etc. The app also supports all MS Outlook versions, such as 2002, 2003, 2007, 2010, 2013, 2016, and 2019. The app is fully secure to convert OLM files to PST file format. The computer program has multiple advanced and astonishing features that are helpful for non-technical users who want to convert OLM files to the PST file format. To check out the latest features and capabilities of the software, download the free demo version of this app. Read More: https://www.softaken.com/olm-to-pst-converter/
bigmorning/whisper_syl_cv12_pad_lob100__0045	bigmorning	2023-08-25T09:37:20Z	59	0	transformers	[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-25T09:37:12Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_syl_cv12_pad_lob100__0045 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. --> # whisper_syl_cv12_pad_lob100__0045 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0028 - Train Accuracy: 0.0362 - Train Wermet: 2.2629 - Validation Loss: 0.6001 - Validation Accuracy: 0.0238 - Validation Wermet: 3.4388 - Epoch: 44 ## 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 \| Train Accuracy \| Train Wermet \| Validation Loss \| Validation Accuracy \| Validation Wermet \| Epoch \| \|:----------:\|:--------------:\|:------------:\|:---------------:\|:-------------------:\|:-----------------:\|:-----:\| \| 5.0233 \| 0.0115 \| 1.6383 \| 3.8616 \| 0.0117 \| 0.9516 \| 0 \| \| 4.4412 \| 0.0127 \| 0.8560 \| 3.5410 \| 0.0125 \| 0.8971 \| 1 \| \| 4.0719 \| 0.0138 \| 0.8366 \| 3.2944 \| 0.0132 \| 0.8706 \| 2 \| \| 3.8091 \| 0.0146 \| 0.8133 \| 3.1691 \| 0.0134 \| 0.8487 \| 3 \| \| 3.6239 \| 0.0152 \| 0.7866 \| 3.0647 \| 0.0136 \| 0.8282 \| 4 \| \| 3.4749 \| 0.0156 \| 0.7589 \| 2.9835 \| 0.0139 \| 0.8049 \| 5 \| \| 3.3444 \| 0.0161 \| 0.7359 \| 2.9351 \| 0.0140 \| 0.7979 \| 6 \| \| 3.2215 \| 0.0165 \| 0.7138 \| 2.8468 \| 0.0145 \| 0.7589 \| 7 \| \| 3.0754 \| 0.0172 \| 0.6873 \| 2.7530 \| 0.0148 \| 0.7413 \| 8 \| \| 2.8713 \| 0.0181 \| 0.6484 \| 2.5226 \| 0.0157 \| 0.7017 \| 9 \| \| 2.5469 \| 0.0197 \| 0.5934 \| 2.1931 \| 0.0168 \| 0.6285 \| 10 \| \| 2.0233 \| 0.0225 \| 0.4997 \| 1.6411 \| 0.0189 \| 0.5215 \| 11 \| \| 1.3808 \| 0.0264 \| 0.3852 \| 1.2401 \| 0.0205 \| 0.4238 \| 12 \| \| 0.9722 \| 0.0290 \| 0.3123 \| 1.0195 \| 0.0215 \| 0.3682 \| 13 \| \| 0.7388 \| 0.0305 \| 0.2828 \| 0.8773 \| 0.0221 \| 0.3322 \| 14 \| \| 0.5787 \| 0.0317 \| 0.2751 \| 0.7970 \| 0.0225 \| 0.3083 \| 15 \| \| 0.4642 \| 0.0325 \| 0.2878 \| 0.7315 \| 0.0227 \| 0.2964 \| 16 \| \| 0.3752 \| 0.0332 \| 0.4217 \| 0.6897 \| 0.0229 \| 0.3297 \| 17 \| \| 0.3042 \| 0.0338 \| 0.7294 \| 0.6572 \| 0.0231 \| 0.4453 \| 18 \| \| 0.2444 \| 0.0343 \| 1.1298 \| 0.6369 \| 0.0232 \| 0.6637 \| 19 \| \| 0.1949 \| 0.0348 \| 1.6370 \| 0.6180 \| 0.0233 \| 1.6119 \| 20 \| \| 0.1544 \| 0.0352 \| 1.6151 \| 0.6149 \| 0.0233 \| 1.6843 \| 21 \| \| 0.1212 \| 0.0355 \| 1.3832 \| 0.6066 \| 0.0233 \| 0.8721 \| 22 \| \| 0.0931 \| 0.0357 \| 1.2799 \| 0.6034 \| 0.0234 \| 0.5109 \| 23 \| \| 0.0725 \| 0.0359 \| 1.0940 \| 0.6102 \| 0.0234 \| 1.0111 \| 24 \| \| 0.0551 \| 0.0361 \| 1.2865 \| 0.6000 \| 0.0234 \| 1.1393 \| 25 \| \| 0.0411 \| 0.0361 \| 1.8511 \| 0.6037 \| 0.0235 \| 2.0574 \| 26 \| \| 0.0311 \| 0.0362 \| 1.7179 \| 0.6018 \| 0.0235 \| 1.4847 \| 27 \| \| 0.0253 \| 0.0362 \| 0.9801 \| 0.6010 \| 0.0235 \| 0.4457 \| 28 \| \| 0.0231 \| 0.0362 \| 0.9376 \| 0.6046 \| 0.0235 \| 0.9247 \| 29 \| \| 0.0196 \| 0.0362 \| 0.6466 \| 0.6078 \| 0.0235 \| 0.5271 \| 30 \| \| 0.0177 \| 0.0362 \| 0.4041 \| 0.6155 \| 0.0235 \| 0.4352 \| 31 \| \| 0.0139 \| 0.0362 \| 0.4202 \| 0.6037 \| 0.0236 \| 0.5585 \| 32 \| \| 0.0137 \| 0.0362 \| 0.8151 \| 0.6015 \| 0.0236 \| 1.8476 \| 33 \| \| 0.0122 \| 0.0362 \| 3.4515 \| 0.6043 \| 0.0236 \| 3.8210 \| 34 \| \| 0.0098 \| 0.0362 \| 1.1787 \| 0.5985 \| 0.0236 \| 0.8094 \| 35 \| \| 0.0071 \| 0.0362 \| 0.9920 \| 0.5992 \| 0.0236 \| 0.8755 \| 36 \| \| 0.0055 \| 0.0362 \| 2.4665 \| 0.6047 \| 0.0236 \| 2.0127 \| 37 \| \| 0.0124 \| 0.0362 \| 4.2468 \| 0.6089 \| 0.0236 \| 2.8886 \| 38 \| \| 0.0109 \| 0.0362 \| 2.0177 \| 0.6097 \| 0.0236 \| 0.3417 \| 39 \| \| 0.0073 \| 0.0362 \| 0.9927 \| 0.6057 \| 0.0237 \| 2.5519 \| 40 \| \| 0.0080 \| 0.0362 \| 1.7341 \| 0.6099 \| 0.0236 \| 1.3119 \| 41 \| \| 0.0063 \| 0.0362 \| 2.4288 \| 0.6058 \| 0.0237 \| 1.3465 \| 42 \| \| 0.0038 \| 0.0362 \| 1.4535 \| 0.6022 \| 0.0237 \| 1.6804 \| 43 \| \| 0.0028 \| 0.0362 \| 2.2629 \| 0.6001 \| 0.0238 \| 3.4388 \| 44 \| ### Framework versions - Transformers 4.33.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
gvolovskiy/ppo-LunarLander-v2	gvolovskiy	2023-08-25T09:35:34Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-25T09:35:11Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: ppo results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 253.05 +/- 21.23 name: mean_reward verified: false --- # 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 ... ```
larabe/testt	larabe	2023-08-25T09:21:37Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-08-23T22:01:20Z	--- license: mit tags: - generated_from_trainer model-index: - name: testt 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. --> # testt This model is a fine-tuned version of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2) 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: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.0 - Tokenizers 0.13.3
64FC/whisper-tiny-en	64FC	2023-08-25T09:20:10Z	76	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-25T08:36:01Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-en results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.36186540731995276 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-tiny-en This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.7102 - Wer Ortho: 0.3646 - Wer: 0.3619 ## 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: 23 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\| \| 0.0005 \| 35.71 \| 500 \| 0.7102 \| 0.3646 \| 0.3619 \| ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
AbeShinzo0708/Voicevox_SugaYoshihide	AbeShinzo0708	2023-08-25T09:18:05Z	0	1	null	[ "菅義偉", "Former Japanese Prime Minister", "Suga", "SugaYoshihide", "Yoshihide", "ja", "license:openrail", "region:us" ]	null	2023-03-18T09:38:23Z	--- license: openrail language: - ja tags: - 菅義偉 - Former Japanese Prime Minister - Suga - SugaYoshihide - Yoshihide ---
caffeinatedwoof/Llama-2-7b-chat-hf-mental_health_counseling_conversations_peft	caffeinatedwoof	2023-08-25T09:10:37Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-24T07:27:26Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0
Isotonic/flan-t5-base-trading_candles	Isotonic	2023-08-25T09:10:33Z	126	11	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "dataset:0xMaka/trading-candles-subset-qa-format", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-08-22T16:32:50Z	--- base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-base-trading_candles results: [] datasets: - 0xMaka/trading-candles-subset-qa-format widget: - text: "Context: -30811302.00,464.00,-156202.00,309984.00,276.00,7664.00,4174.00,824467.00,19741.12,19798.04,19860.18,19567.9 Question: identify candle" - text: "Context: 867553.00,-4282049.00,6306.00,4440418.00,13.00,50962.00,101.00,59152496.00,39512.71,39477.49,39512.71,39380.74 Question: identify candle" - text: "Context: -206.00,626162.00,-35917428.00,-49739.00,6669939.00,64.00,19988.00,7094559.00,17752.71,17752.71,17752.71,17752.71 Question: find candle: Four Price Doji" pipeline_tag: text2text-generation --- <!-- 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. --> # flan-t5-base-trading_candles This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on [0xMaka/trading-candles-subset-qa-format](https://huggingface.co/datasets/0xMaka/trading-candles-subset-qa-format) dataset. It achieves the following results on the evaluation set: - Loss: 0.0061 - Rouge1: 88.3665 - Rouge2: 86.86 - Rougel: 88.3651 - Rougelsum: 88.3665 - Gen Len: 18.9025 ## 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: 4 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 0.019 \| 1.0 \| 70009 \| 0.0089 \| 88.0774 \| 86.4734 \| 88.0734 \| 88.0748 \| 18.9022 \| \| 0.0095 \| 2.0 \| 140018 \| 0.0069 \| 88.3636 \| 86.8542 \| 88.3612 \| 88.3625 \| 18.9016 \| \| 0.0071 \| 3.0 \| 210027 \| 0.0061 \| 88.3665 \| 86.86 \| 88.3651 \| 88.3665 \| 18.9025 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
nishant-glance/model-sd-1-4-priorp-unet-1200	nishant-glance	2023-08-25T08:55:23Z	1	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-25T08:22:30Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - nishant-glance/model-sd-1-4-priorp-unet-1200 This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks person using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: True.
diana9m/falcon-7b-sharded-bf16-finetuned-mental-health-NUNA_reevaluated	diana9m	2023-08-25T08:47:26Z	0	0	null	[ "generated_from_trainer", "base_model:ybelkada/falcon-7b-sharded-bf16", "base_model:finetune:ybelkada/falcon-7b-sharded-bf16", "region:us" ]	null	2023-08-24T13:06:28Z	--- base_model: ybelkada/falcon-7b-sharded-bf16 tags: - generated_from_trainer model-index: - name: falcon-7b-sharded-bf16-finetuned-mental-health-NUNA_reevaluated 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. --> # falcon-7b-sharded-bf16-finetuned-mental-health-NUNA_reevaluated This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 320 ### Training results ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
bigmorning/whisper_syl_cv12_pad_lob100__0025	bigmorning	2023-08-25T08:44:26Z	59	0	transformers	[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-25T08:44:18Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_syl_cv12_pad_lob100__0025 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. --> # whisper_syl_cv12_pad_lob100__0025 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0725 - Train Accuracy: 0.0359 - Train Wermet: 1.0940 - Validation Loss: 0.6102 - Validation Accuracy: 0.0234 - Validation Wermet: 1.0111 - Epoch: 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: - 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 \| Train Accuracy \| Train Wermet \| Validation Loss \| Validation Accuracy \| Validation Wermet \| Epoch \| \|:----------:\|:--------------:\|:------------:\|:---------------:\|:-------------------:\|:-----------------:\|:-----:\| \| 5.0233 \| 0.0115 \| 1.6383 \| 3.8616 \| 0.0117 \| 0.9516 \| 0 \| \| 4.4412 \| 0.0127 \| 0.8560 \| 3.5410 \| 0.0125 \| 0.8971 \| 1 \| \| 4.0719 \| 0.0138 \| 0.8366 \| 3.2944 \| 0.0132 \| 0.8706 \| 2 \| \| 3.8091 \| 0.0146 \| 0.8133 \| 3.1691 \| 0.0134 \| 0.8487 \| 3 \| \| 3.6239 \| 0.0152 \| 0.7866 \| 3.0647 \| 0.0136 \| 0.8282 \| 4 \| \| 3.4749 \| 0.0156 \| 0.7589 \| 2.9835 \| 0.0139 \| 0.8049 \| 5 \| \| 3.3444 \| 0.0161 \| 0.7359 \| 2.9351 \| 0.0140 \| 0.7979 \| 6 \| \| 3.2215 \| 0.0165 \| 0.7138 \| 2.8468 \| 0.0145 \| 0.7589 \| 7 \| \| 3.0754 \| 0.0172 \| 0.6873 \| 2.7530 \| 0.0148 \| 0.7413 \| 8 \| \| 2.8713 \| 0.0181 \| 0.6484 \| 2.5226 \| 0.0157 \| 0.7017 \| 9 \| \| 2.5469 \| 0.0197 \| 0.5934 \| 2.1931 \| 0.0168 \| 0.6285 \| 10 \| \| 2.0233 \| 0.0225 \| 0.4997 \| 1.6411 \| 0.0189 \| 0.5215 \| 11 \| \| 1.3808 \| 0.0264 \| 0.3852 \| 1.2401 \| 0.0205 \| 0.4238 \| 12 \| \| 0.9722 \| 0.0290 \| 0.3123 \| 1.0195 \| 0.0215 \| 0.3682 \| 13 \| \| 0.7388 \| 0.0305 \| 0.2828 \| 0.8773 \| 0.0221 \| 0.3322 \| 14 \| \| 0.5787 \| 0.0317 \| 0.2751 \| 0.7970 \| 0.0225 \| 0.3083 \| 15 \| \| 0.4642 \| 0.0325 \| 0.2878 \| 0.7315 \| 0.0227 \| 0.2964 \| 16 \| \| 0.3752 \| 0.0332 \| 0.4217 \| 0.6897 \| 0.0229 \| 0.3297 \| 17 \| \| 0.3042 \| 0.0338 \| 0.7294 \| 0.6572 \| 0.0231 \| 0.4453 \| 18 \| \| 0.2444 \| 0.0343 \| 1.1298 \| 0.6369 \| 0.0232 \| 0.6637 \| 19 \| \| 0.1949 \| 0.0348 \| 1.6370 \| 0.6180 \| 0.0233 \| 1.6119 \| 20 \| \| 0.1544 \| 0.0352 \| 1.6151 \| 0.6149 \| 0.0233 \| 1.6843 \| 21 \| \| 0.1212 \| 0.0355 \| 1.3832 \| 0.6066 \| 0.0233 \| 0.8721 \| 22 \| \| 0.0931 \| 0.0357 \| 1.2799 \| 0.6034 \| 0.0234 \| 0.5109 \| 23 \| \| 0.0725 \| 0.0359 \| 1.0940 \| 0.6102 \| 0.0234 \| 1.0111 \| 24 \| ### Framework versions - Transformers 4.33.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
libin46/llama2-qlora-finetunined-french	libin46	2023-08-25T08:40:12Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-25T08:40:04Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0
Donnaphat/bloomz-560m_PROMPT_TUNING_CAUSAL_LM	Donnaphat	2023-08-25T08:36:44Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-25T08:36:41Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
feliciamj/ppo-Huggy	feliciamj	2023-08-25T08:32:29Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-08-25T08:32:23Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: feliciamj/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Vertti/TuumaPEFTDialogue01	Vertti	2023-08-25T08:29:37Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-25T08:29:18Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
arroyadr/speecht5_finetuned_voxpopuli_nl	arroyadr	2023-08-25T08:26:45Z	75	0	transformers	[ "transformers", "pytorch", "speecht5", "text-to-audio", "generated_from_trainer", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2023-08-24T21:59:06Z	--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl 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. --> # speecht5_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.5937 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - 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: 50 - training_steps: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6482 \| 3.14 \| 100 \| 0.5937 \| ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
bigmorning/whisper_syl_cv12_pad_lob100__0015	bigmorning	2023-08-25T08:18:04Z	59	0	transformers	[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-25T08:17:55Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_syl_cv12_pad_lob100__0015 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. --> # whisper_syl_cv12_pad_lob100__0015 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7388 - Train Accuracy: 0.0305 - Train Wermet: 0.2828 - Validation Loss: 0.8773 - Validation Accuracy: 0.0221 - Validation Wermet: 0.3322 - Epoch: 14 ## 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 \| Train Accuracy \| Train Wermet \| Validation Loss \| Validation Accuracy \| Validation Wermet \| Epoch \| \|:----------:\|:--------------:\|:------------:\|:---------------:\|:-------------------:\|:-----------------:\|:-----:\| \| 5.0233 \| 0.0115 \| 1.6383 \| 3.8616 \| 0.0117 \| 0.9516 \| 0 \| \| 4.4412 \| 0.0127 \| 0.8560 \| 3.5410 \| 0.0125 \| 0.8971 \| 1 \| \| 4.0719 \| 0.0138 \| 0.8366 \| 3.2944 \| 0.0132 \| 0.8706 \| 2 \| \| 3.8091 \| 0.0146 \| 0.8133 \| 3.1691 \| 0.0134 \| 0.8487 \| 3 \| \| 3.6239 \| 0.0152 \| 0.7866 \| 3.0647 \| 0.0136 \| 0.8282 \| 4 \| \| 3.4749 \| 0.0156 \| 0.7589 \| 2.9835 \| 0.0139 \| 0.8049 \| 5 \| \| 3.3444 \| 0.0161 \| 0.7359 \| 2.9351 \| 0.0140 \| 0.7979 \| 6 \| \| 3.2215 \| 0.0165 \| 0.7138 \| 2.8468 \| 0.0145 \| 0.7589 \| 7 \| \| 3.0754 \| 0.0172 \| 0.6873 \| 2.7530 \| 0.0148 \| 0.7413 \| 8 \| \| 2.8713 \| 0.0181 \| 0.6484 \| 2.5226 \| 0.0157 \| 0.7017 \| 9 \| \| 2.5469 \| 0.0197 \| 0.5934 \| 2.1931 \| 0.0168 \| 0.6285 \| 10 \| \| 2.0233 \| 0.0225 \| 0.4997 \| 1.6411 \| 0.0189 \| 0.5215 \| 11 \| \| 1.3808 \| 0.0264 \| 0.3852 \| 1.2401 \| 0.0205 \| 0.4238 \| 12 \| \| 0.9722 \| 0.0290 \| 0.3123 \| 1.0195 \| 0.0215 \| 0.3682 \| 13 \| \| 0.7388 \| 0.0305 \| 0.2828 \| 0.8773 \| 0.0221 \| 0.3322 \| 14 \| ### Framework versions - Transformers 4.33.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
zimhe/controlnet-wall-constrained-floorplan	zimhe	2023-08-25T08:06:23Z	3	0	diffusers	[ "diffusers", "safetensors", "dataset:zimhe/wall-constrained-floorplans-10k", "region:us" ]	null	2023-08-23T09:05:20Z	--- datasets: - zimhe/wall-constrained-floorplans-10k ---
dt-and-vanilla-ardt/ardt-vanilla-arrl_sgld_train_halfcheetah_high-2508_0648-66	dt-and-vanilla-ardt	2023-08-25T08:00:01Z	31	0	transformers	[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2023-08-25T05:49:57Z	--- tags: - generated_from_trainer model-index: - name: ardt-vanilla-arrl_sgld_train_halfcheetah_high-2508_0648-66 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. --> # ardt-vanilla-arrl_sgld_train_halfcheetah_high-2508_0648-66 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
LibrAI/longformer-action-ro	LibrAI	2023-08-25T07:58:30Z	2,940	0	transformers	[ "transformers", "pytorch", "longformer", "text-classification", "generated_from_trainer", "base_model:allenai/longformer-base-4096", "base_model:finetune:allenai/longformer-base-4096", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-24T12:33:56Z	--- license: apache-2.0 base_model: allenai/longformer-base-4096 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: longformer-action-ro 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. --> # longformer-action-ro This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1084 - Accuracy: 0.964 - Precision: 0.961 - Recall: 0.936 - F1: 0.946 ## 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: 64 - eval_batch_size: 128 - 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 \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:-----:\| \| No log \| 1.0 \| 89 \| 0.2301 \| 0.926 \| 0.933 \| 0.861 \| 0.883 \| \| No log \| 2.0 \| 178 \| 0.1487 \| 0.964 \| 0.968 \| 0.915 \| 0.937 \| \| No log \| 3.0 \| 267 \| 0.1084 \| 0.964 \| 0.961 \| 0.936 \| 0.946 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
Koantek/dolly_llama-v4	Koantek	2023-08-25T07:56:08Z	3	0	peft	[ "peft", "region:us" ]	null	2023-08-25T07:56:05Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
Intel/whisper-base-int8-dynamic-inc	Intel	2023-08-25T07:55:37Z	4	1	transformers	[ "transformers", "onnx", "whisper", "automatic-speech-recognition", "int8", "ONNX", "PostTrainingDynamic", "Intel® Neural Compressor", "neural-compressor", "dataset:librispeech_asr", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-25T07:50:51Z	--- license: apache-2.0 datasets: - librispeech_asr metrics: - wer pipeline_tag: automatic-speech-recognition tags: - automatic-speech-recognition - int8 - ONNX - PostTrainingDynamic - Intel® Neural Compressor - neural-compressor library_name: transformers --- ## Model Details: INT8 Whisper base Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. This int8 ONNX model is generated by [neural-compressor](https://github.com/intel/neural-compressor) and the fp32 model can be exported with below command: ```shell optimum-cli export onnx --model openai/whisper-base whisper-base-with-past/ --task automatic-speech-recognition-with-past --opset 13 ``` \| Model Detail \| Description \| \| ----------- \| ----------- \| \| Model Authors - Company \| Intel \| \| Date \| August 25, 2023 \| \| Version \| 1 \| \| Type \| Speech Recognition \| \| Paper or Other Resources \| - \| \| License \| Apache 2.0 \| \| Questions or Comments \| [Community Tab](https://huggingface.co/Intel/whisper-base-int8-dynamic/discussions)\| \| Intended Use \| Description \| \| ----------- \| ----------- \| \| Primary intended uses \| You can use the raw model for automatic speech recognition inference \| \| Primary intended users \| Anyone doing automatic speech recognition inference \| \| Out-of-scope uses \| This model in most cases will need to be fine-tuned for your particular task. The model should not be used to intentionally create hostile or alienating environments for people.\| ### How to use Download the model by cloning the repository: ```shell git clone https://huggingface.co/Intel/whisper-base-int8-dynamic ``` Evaluate the model with below code: ```python import os from evaluate import load from datasets import load_dataset from transformers import WhisperForConditionalGeneration, WhisperProcessor, AutoConfig model_name = 'openai/whisper-base' model_path = 'whisper-base-int8-dynamic' processor = WhisperProcessor.from_pretrained(model_name) model = WhisperForConditionalGeneration.from_pretrained(model_name) config = AutoConfig.from_pretrained(model_name) wer = load("wer") librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test") from optimum.onnxruntime import ORTModelForSpeechSeq2Seq from transformers import PretrainedConfig model_config = PretrainedConfig.from_pretrained(model_name) predictions = [] references = [] sessions = ORTModelForSpeechSeq2Seq.load_model( os.path.join(model_path, 'encoder_model.onnx'), os.path.join(model_path, 'decoder_model.onnx'), os.path.join(model_path, 'decoder_with_past_model.onnx')) model = ORTModelForSpeechSeq2Seq(sessions[0], sessions[1], model_config, model_path, sessions[2]) for idx, batch in enumerate(librispeech_test_clean): audio = batch["audio"] input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features reference = processor.tokenizer._normalize(batch['text']) references.append(reference) predicted_ids = model.generate(input_features)[0] transcription = processor.decode(predicted_ids) prediction = processor.tokenizer._normalize(transcription) predictions.append(prediction) wer_result = wer.compute(references=references, predictions=predictions) print(f"Result wer: {wer_result * 100}") accuracy = 1 - wer_result print("Accuracy: %.5f" % accuracy) ``` ## Metrics (Model Performance): \| Model \| Model Size (GB) \| wer \| \|---\|:---:\|:---:\| \| FP32 \|0.95\|5.04\| \| INT8 \|0.17\|5.31\|
chrisrtt/gbert-multi-class-german-hate	chrisrtt	2023-08-25T07:53:41Z	645	2	transformers	[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-25T07:39:21Z	# Model Card for German Hate Speech Classifier ## Model Details ### Introduction This model was developed to explore the potential of German language models in multi-class classification of hate speech in German online journals. It is a fine-tuned version of the GBERT model from (Chan, Schweter, and Möller, 2020). ### Dataset The dataset used for training is a consolidation of three pre-existing German hate speech datasets: - RP (Assenmacher et al., 2021) - DeTox (Demus et al., 2022) - Twitter dataset (Glasenbach, 2022) The combined dataset underwent cleaning to minimize biases and remove redundant data. ## Performance Our experiments delivered promising results, with the model reliably classifying comments into: - No Hate Speech - Other Hate Speech (Threat, Insult, Profanity) - Political Hate Speech - Racist Hate Speech - Sexist Hate Speech The model achieved a macro F1-score of 0.775. However, to further reduce misclassifications, improvements are essential. Short comments are overproportionally classified as Sexist Hate Speech.
bigmorning/whisper_syl_cv12_pad_lob100__0005	bigmorning	2023-08-25T07:51:33Z	59	0	transformers	[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-25T07:51:23Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_syl_cv12_pad_lob100__0005 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. --> # whisper_syl_cv12_pad_lob100__0005 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.6239 - Train Accuracy: 0.0152 - Train Wermet: 0.7866 - Validation Loss: 3.0647 - Validation Accuracy: 0.0136 - Validation Wermet: 0.8282 - Epoch: 4 ## 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 \| Train Accuracy \| Train Wermet \| Validation Loss \| Validation Accuracy \| Validation Wermet \| Epoch \| \|:----------:\|:--------------:\|:------------:\|:---------------:\|:-------------------:\|:-----------------:\|:-----:\| \| 5.0233 \| 0.0115 \| 1.6383 \| 3.8616 \| 0.0117 \| 0.9516 \| 0 \| \| 4.4412 \| 0.0127 \| 0.8560 \| 3.5410 \| 0.0125 \| 0.8971 \| 1 \| \| 4.0719 \| 0.0138 \| 0.8366 \| 3.2944 \| 0.0132 \| 0.8706 \| 2 \| \| 3.8091 \| 0.0146 \| 0.8133 \| 3.1691 \| 0.0134 \| 0.8487 \| 3 \| \| 3.6239 \| 0.0152 \| 0.7866 \| 3.0647 \| 0.0136 \| 0.8282 \| 4 \| ### Framework versions - Transformers 4.33.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
JennnDexter/Translation	JennnDexter	2023-08-25T07:40:49Z	103	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "en", "fr", "dataset:opus_books", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-08-25T07:12:41Z	--- language: - en - fr license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - opus_books model-index: - name: Translation 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. --> # Translation This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books en-fr 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: 16 - eval_batch_size: 16 - 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 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
LibrAI/bert-action-ro	LibrAI	2023-08-25T07:39:45Z	113	1	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-24T12:10:09Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: bert-action-ro 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-action-ro This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1567 - Accuracy: 0.958 - Precision: 0.949 - Recall: 0.941 - F1: 0.944 ## 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: 64 - eval_batch_size: 128 - 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 \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:-----:\| \| No log \| 1.0 \| 89 \| 0.3700 \| 0.876 \| 0.836 \| 0.809 \| 0.815 \| \| No log \| 2.0 \| 178 \| 0.2057 \| 0.936 \| 0.927 \| 0.924 \| 0.924 \| \| No log \| 3.0 \| 267 \| 0.1567 \| 0.958 \| 0.949 \| 0.941 \| 0.944 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
natsusakiyomi/AsagaoMix	natsusakiyomi	2023-08-25T07:32:49Z	13	7	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "ja", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-07-29T03:42:53Z	--- license: creativeml-openrail-m language: - ja pipeline_tag: text-to-image tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image library_name: diffusers --- --- <h4>📄 ライセンス / License</h4> <div class="px-2"> <table class="table-fixed border mt-0 text-xs"> <tbody> <tr> <td class="px-4 text-base" colspan="2"> <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license"> 修正 CreativeML OpenRAIL-M ライセンス / Modified CreativeML OpenRAIL-M license </a> </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span class="text-green-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" /> </svg> </span> </td> <td> このモデルのクレジットを入れずに使用する<br> Use the model without crediting the creator </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span class="text-green-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" /> </svg> </span> </td> <td> このモデルで生成した画像を商用利用する<br> Sell images they generate </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span class="text-red-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" /> </svg> </span> </td> <td> このモデルを商用の画像生成サービスで利用する</br> Run on services that generate images for money </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span class="text-green-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" /> </svg> </span> </td> <td> このモデルを使用したマージモデルを共有する<br> Share merges using this model </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span class="text-red-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" /> </svg> </span> </td> <td> このモデル、またはこのモデルをマージしたモデルを販売する</br> Sell this model or merges using this model </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span class="text-red-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" /> </svg> </span> </td> <td> このモデルをマージしたモデルに異なる権限を設定する</br> Have different permissions when sharing merges </td> </tr> </tbody> </table> </div>
MateiCv/spa-eng-pos-tagging-v6	MateiCv	2023-08-25T07:27:55Z	180	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-multilingual-cased", "base_model:finetune:distilbert/distilbert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-25T07:27:22Z	--- license: apache-2.0 base_model: distilbert-base-multilingual-cased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: spa-eng-pos-tagging-v6 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. --> # spa-eng-pos-tagging-v6 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3128 - Accuracy: 0.9056 - Precision: 0.9032 - Recall: 0.8293 - F1: 0.8345 - Hamming Loss: 0.0944 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 14 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| Hamming Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\|:------------:\| \| 1.0141 \| 1.0 \| 1744 \| 0.7804 \| 0.7158 \| 0.7328 \| 0.6183 \| 0.6345 \| 0.2842 \| \| 0.6292 \| 2.0 \| 3488 \| 0.5384 \| 0.7973 \| 0.8111 \| 0.7029 \| 0.7213 \| 0.2027 \| \| 0.4438 \| 3.0 \| 5232 \| 0.4236 \| 0.8462 \| 0.8346 \| 0.7762 \| 0.7732 \| 0.1538 \| \| 0.3626 \| 4.0 \| 6976 \| 0.3856 \| 0.8651 \| 0.8524 \| 0.7933 \| 0.7903 \| 0.1349 \| \| 0.3141 \| 5.0 \| 8720 \| 0.3697 \| 0.8712 \| 0.8688 \| 0.7998 \| 0.8028 \| 0.1288 \| \| 0.2575 \| 6.0 \| 10464 \| 0.3689 \| 0.8751 \| 0.8758 \| 0.8003 \| 0.8058 \| 0.1249 \| \| 0.2117 \| 7.0 \| 12208 \| 0.3329 \| 0.8890 \| 0.8832 \| 0.8169 \| 0.8184 \| 0.1110 \| \| 0.1864 \| 8.0 \| 13952 \| 0.3235 \| 0.9010 \| 0.8946 \| 0.8278 \| 0.8293 \| 0.0990 \| \| 0.1555 \| 9.0 \| 15696 \| 0.3128 \| 0.9056 \| 0.9032 \| 0.8293 \| 0.8345 \| 0.0944 \| \| 0.1322 \| 10.0 \| 17440 \| 0.3311 \| 0.9088 \| 0.9010 \| 0.8376 \| 0.8377 \| 0.0912 \| \| 0.1111 \| 11.0 \| 19184 \| 0.3394 \| 0.9101 \| 0.9081 \| 0.8319 \| 0.8383 \| 0.0899 \| \| 0.0874 \| 12.0 \| 20928 \| 0.3472 \| 0.9148 \| 0.9100 \| 0.8407 \| 0.8440 \| 0.0852 \| \| 0.0659 \| 13.0 \| 22672 \| 0.3635 \| 0.9131 \| 0.9072 \| 0.8400 \| 0.8422 \| 0.0869 \| \| 0.0608 \| 14.0 \| 24416 \| 0.3560 \| 0.9187 \| 0.9140 \| 0.8452 \| 0.8482 \| 0.0813 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Tokenizers 0.13.3
Datactive/BERT_sud_queries_classification	Datactive	2023-08-25T07:14:42Z	61	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-24T17:02:17Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Datactive/BERT_sud_queries_classification 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. --> # Datactive/BERT_sud_queries_classification This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0277 - Validation Loss: 0.0188 - Train F1: 0.9958 - 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1419, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train F1 \| Epoch \| \|:----------:\|:---------------:\|:--------:\|:-----:\| \| 0.0277 \| 0.0188 \| 0.9958 \| 0 \| ### Framework versions - Transformers 4.29.0.dev0 - TensorFlow 2.12.0 - Datasets 2.11.0 - Tokenizers 0.13.3
raygx/BERT-NepSA-domainAdapt	raygx	2023-08-25T06:55:43Z	62	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:raygx/BertClassifier4NepaliNews", "base_model:finetune:raygx/BertClassifier4NepaliNews", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-24T14:47:00Z	--- license: mit base_model: raygx/BertClassifier4NepaliNews tags: - generated_from_keras_callback model-index: - name: BERT-NepSA-domainAdapt 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-NepSA-domainAdapt This model is a fine-tuned version of [raygx/BertClassifier4NepaliNews](https://huggingface.co/raygx/BertClassifier4NepaliNews) 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: {'name': 'AdamWeightDecay', 'learning_rate': 9.99e-06, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.001} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.32.0 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3
bogeumkim/polyglot-1.3b-qlora-emotion-classification	bogeumkim	2023-08-25T06:35:41Z	2	0	peft	[ "peft", "region:us" ]	null	2023-08-25T06:23:51Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0
rachit221195/rachit-trained-xl-colab	rachit221195	2023-08-25T06:27:16Z	6	1	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2023-08-25T06:04:55Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks human tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - rachit221195/rachit-trained-xl-colab These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks human using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
nishant-glance/model-sd-1-4-priorp-lowlr-unet	nishant-glance	2023-08-25T06:19:48Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-25T05:41:42Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - nishant-glance/model-sd-1-4-priorp-lowlr-unet This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks person using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: True.
eunbi-jeong/gpt2	eunbi-jeong	2023-08-25T06:19:07Z	0	0	null	[ "translation", "en", "dataset:hellaswag", "region:us" ]	translation	2023-08-25T06:17:58Z	--- datasets: - hellaswag language: - en pipeline_tag: translation ---
VkStyle/roma	VkStyle	2023-08-25T06:18:05Z	0	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2023-08-22T20:25:11Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
54data/xlm-roberta-base-finetuned-panx-de-fr	54data	2023-08-25T06:16:07Z	101	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-25T06:03:26Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr 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. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1658 - F1: 0.8588 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2908 \| 1.0 \| 715 \| 0.1909 \| 0.8125 \| \| 0.1466 \| 2.0 \| 1430 \| 0.1613 \| 0.8492 \| \| 0.0945 \| 3.0 \| 2145 \| 0.1658 \| 0.8588 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
nithiroj/wav2vec2-base-finetuned-gtzan	nithiroj	2023-08-25T06:07:33Z	159	0	transformers	[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-08-25T03:44:15Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.81 --- <!-- 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-finetuned-gtzan This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.6608 - Accuracy: 0.81 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.9578 \| 1.0 \| 113 \| 1.8537 \| 0.28 \| \| 1.4644 \| 2.0 \| 226 \| 1.5867 \| 0.5 \| \| 0.9624 \| 3.0 \| 339 \| 1.1706 \| 0.66 \| \| 0.8329 \| 4.0 \| 452 \| 0.8807 \| 0.76 \| \| 0.5047 \| 5.0 \| 565 \| 0.9421 \| 0.73 \| \| 0.4525 \| 6.0 \| 678 \| 0.7879 \| 0.73 \| \| 0.5111 \| 7.0 \| 791 \| 0.6493 \| 0.79 \| \| 0.1836 \| 8.0 \| 904 \| 0.5938 \| 0.85 \| \| 0.1806 \| 9.0 \| 1017 \| 0.5787 \| 0.84 \| \| 0.1338 \| 10.0 \| 1130 \| 0.6608 \| 0.81 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
hihisu1231/mbti_230825_newdata	hihisu1231	2023-08-25T06:03:01Z	20	0	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-25T04:08:40Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: polyglot-1.3b-koalpaca-v1.1a-rtx3090_0825_newdata 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. --> # polyglot-1.3b-koalpaca-v1.1a-rtx3090_0825_newdata This model is a fine-tuned version of [EleutherAI/polyglot-ko-1.3b](https://huggingface.co/EleutherAI/polyglot-ko-1.3b) 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1000.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
cx-olquinjica/angXLMR	cx-olquinjica	2023-08-25T05:47:00Z	106	0	transformers	[ "transformers", "pytorch", "safetensors", "xlm-roberta", "fill-mask", "umb", "lua", "cjk", "kmb", "kg", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-08-25T03:21:00Z	--- language: - umb - lua - cjk - kmb - kg --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
darkbloodevil/bloomz-560m_PROMPT_TUNING_CAUSAL_LM	darkbloodevil	2023-08-25T05:43:48Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-25T05:43:45Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
64FC/whisper-small-dv	64FC	2023-08-25T05:30:50Z	84	1	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dv", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-25T03:35:28Z	--- language: - dv license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Small Dv - Francois Chaumet results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 config: dv split: test args: dv metrics: - name: Wer type: wer value: 11.703237472615363 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Dv - Francois Chaumet This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. It achieves the following results on the evaluation set: - Loss: 0.1658 - Wer Ortho: 59.2103 - Wer: 11.7032 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.1231 \| 1.63 \| 500 \| 0.1689 \| 61.9054 \| 13.0142 \| \| 0.046 \| 3.26 \| 1000 \| 0.1658 \| 59.2103 \| 11.7032 \| ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
dkimds/ppo-LunarLander-v2	dkimds	2023-08-25T05:17:39Z	2	0	transformers	[ "transformers", "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "endpoints_compatible", "region:us" ]	reinforcement-learning	2023-08-01T04:24:12Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -132.01 +/- 71.74 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'dkimds/ppo-LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
ccore/smart-gpt2-test	ccore	2023-08-25T04:46:08Z	0	3	null	[ "license:gpl-3.0", "region:us" ]	null	2023-08-21T20:05:01Z	--- license: gpl-3.0 --- ./gpt-2 -m ggml-model.bin -p "[INSTRUCTION] your prompt [RESPONSE]" -n 1000 --top_p 1 GGML model - more information about framework - https://github.com/ggerganov/ggml/tree/master/examples/gpt-2
vodkaslime/codellama-7b-hf	vodkaslime	2023-08-25T04:09:00Z	7	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "llama-2", "code", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-25T04:03:43Z	--- language: - code pipeline_tag: text-generation tags: - llama-2 license: llama2 --- # Code Llama Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the base 7B version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom. \| \| Base Model \| Python \| Instruct \| \| --- \| ----------------------------------------------------------------------------- \| ------------------------------------------------------------------------------------------- \| ----------------------------------------------------------------------------------------------- \| \| 7B \| [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) \| [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) \| [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) \| \| 13B \| [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) \| [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) \| [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) \| \| 34B \| [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) \| [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) \| [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) \| Make sure to be using this temporary branch of transformers unit support is fully merged and released. ```bash pip install git+https://github.com/huggingface/transformers.git@refs/pull/25740/head accelerate ``` ```python from transformers import AutoTokenizer import transformers import torch model = "codellama/CodeLlama-7b-hf" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( 'import socket\n\ndef ping_exponential_backoff(host: str):', do_sample=True, top_k=10, temperature=0.1, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Model Details Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs). Model Developers* Meta Variations Code Llama comes in three model sizes, and three variants: * Code Llama: base models designed for general code synthesis and understanding * Code Llama - Python: designed specifically for Python * Code Llama - Instruct: for instruction following and safer deployment =All variants are available in sizes of 7B, 13B and 34B parameters. This repository contains the base model of 7B parameters. Input Models input text only. Output Models generate text only. Model Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture. Model Dates Code Llama and its variants have been trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) Research Paper More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)". ## Intended Use Intended Use Cases Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications. Out-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants. ## Hardware and Software Training Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster. Carbon Footprint In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program. ## Training Data All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details). ## Evaluation Results See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper. ## Ethical Considerations and Limitations Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-user-guide](https://ai.meta.com/llama/responsible-user-guide).
zhang-yice/spt-absa-bert-10k	zhang-yice	2023-08-25T04:06:13Z	33	3	transformers	[ "transformers", "pytorch", "bert", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	null	2023-05-23T10:36:20Z	--- license: cc-by-4.0 --- ## SPT-ABSA We continue to pre-train BERT-base via Sentiment-enhance pre-training (SPT). - Title: An Empirical Study of Sentiment-Enhanced Pre-Training for Aspect-Based Sentiment Analysis - Author: Yice Zhang, Yifan Yang, Bin Liang, Shiwei Chen, Bing Qin, and Ruifeng Xu - Conference: ACL-2023 Finding (Long) GitHub Repository: https://github.com/HITSZ-HLT/SPT-ABSA ### What Did We Do? Aspect-Based Sentiment Analysis (ABSA) is an important problem in sentiment analysis. Its goal is to recognize opinions and sentiments towards specific aspects from user-generated content. Many research efforts leverage pre-training techniques to learn sentiment-aware representations and achieve significant gains in various ABSA tasks. We conduct an empirical study of SPT-ABSA to systematically investigate and analyze the effectiveness of the existing approaches. We mainly concentrate on the following questions: - (a) what impact do different types of sentiment knowledge have on downstream ABSA tasks?; - (b) which knowledge integration method is most effective?; and - (c) does injecting non-sentiment-specific linguistic knowledge (e.g., part-of-speech tags and syntactic relations) into pre-training have positive impacts? Based on the experimental investigation of these questions, we eventually obtain a powerful sentiment-enhanced pre-trained model. The powerful sentiment-enhanced pre-trained model has two versions, namely [zhang-yice/spt-absa-bert-400k](https://huggingface.co/zhang-yice/spt-absa-bert-400k) and [zhang-yice/spt-absa-bert-10k](https://huggingface.co/zhang-yice/spt-absa-bert-10k), which integrates three types of knowledge: - aspect words: masking aspects' context and predicting them. - review's rating score: rating prediction. - syntax knowledge: - part-of-speech, - dependency direction, - dependency distance. ### Experimental Results <img width="75%" alt="image" src="https://github.com/HITSZ-HLT/SPT-ABSA/assets/9134454/38fc2db0-6ccf-47a7-a93c-cf54667e1a23"> <img width="75%" alt="image" src="https://github.com/HITSZ-HLT/SPT-ABSA/assets/9134454/20c5a976-014e-433f-a2ec-4bb259e5a382">
LarryAIDraw/rosaria1	LarryAIDraw	2023-08-25T03:58:54Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-25T03:54:54Z	--- license: creativeml-openrail-m --- https://civitai.com/models/106421/rosaria-genshin-impact
LarryAIDraw/rosaria	LarryAIDraw	2023-08-25T03:58:15Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-25T03:52:53Z	--- license: creativeml-openrail-m --- https://civitai.com/models/101711/rosaria-genshin-impact-or-goofy-ai
peteryushunli/codeparrot-ds	peteryushunli	2023-08-25T03:45:36Z	143	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-25T02:41:41Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: codeparrot-ds 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. --> # codeparrot-ds This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) 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: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
AdanLee/Reinforce-CartPole-v1	AdanLee	2023-08-25T03:45:01Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-08-25T03:44:49Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
rohn132/ppo-Pyramids	rohn132	2023-08-25T03:40:18Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-08-25T03:37:43Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: rohn132/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
dt-and-vanilla-ardt/ardt-vanilla-arrl_train_halfcheetah_high-2508_0228-99	dt-and-vanilla-ardt	2023-08-25T03:36:57Z	32	0	transformers	[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2023-08-25T01:29:48Z	--- tags: - generated_from_trainer model-index: - name: ardt-vanilla-arrl_train_halfcheetah_high-2508_0228-99 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. --> # ardt-vanilla-arrl_train_halfcheetah_high-2508_0228-99 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
spear1/q-Taxi-v3	spear1	2023-08-25T03:31:57Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-25T03:31:56Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.72 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="spear1/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"]) ```
spear1/q-FrozenLake-v1-4x4-noSlippery	spear1	2023-08-25T03:30:17Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-25T03:30:15Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="spear1/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"]) ```
DataMonke/bert-base-uncased-finetuned-review-sentiment-analysis	DataMonke	2023-08-25T03:27:20Z	4	2	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "en", "dataset:amazon_us_reviews", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-21T14:55:06Z	--- language: - en metrics: - accuracy pipeline_tag: text-classification datasets: - amazon_us_reviews --- # E-Commerce Product Sentiment Analysis This model classifies texts into stars categories ranging from 1 to 5. This model has a BERT base and further finetuned on Amazon and e-commerce clothing product reviews.
AdanLee/dqn-SpaceInvadersNoFrameskip-v4	AdanLee	2023-08-25T03:15:50Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-25T03:15:07Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 690.50 +/- 213.28 name: mean_reward verified: false --- # 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 Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AdanLee -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AdanLee -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga AdanLee ``` ## 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', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
DunnBC22/trocr-base-handwritten-OCR-handwriting_recognition_v2	DunnBC22	2023-08-25T03:15:17Z	487	14	transformers	[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "image-to-text", "en", "endpoints_compatible", "region:us" ]	image-to-text	2023-04-17T00:13:38Z	--- tags: - generated_from_trainer model-index: - name: trocr-base-handwritten-OCR-handwriting_recognition_v2 results: [] language: - en metrics: - cer pipeline_tag: image-to-text --- # trocr-base-handwritten-OCR-handwriting_recognition_v2 This model is a fine-tuned version of [microsoft/trocr-base-handwritten](https://huggingface.co/microsoft/trocr-base-handwritten). It achieves the following results on the evaluation set: - Loss: 0.2470 - CER: 0.0360 ## Model description For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Optical%20Character%20Recognition%20(OCR)/Handwriting%20Recognition/Handwriting%20Recognition_v2/Mini%20Handwriting%20OCR%20Project.ipynb ## Intended uses & limitations This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to test and experiment with this model, but it is at your own risk/peril. ## Training and evaluation data Dataset Source: https://www.kaggle.com/datasets/ssarkar445/handwriting-recognitionocr _Character Length for Training Dataset:_ ![Input Character Length for Training Dataset](https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Optical%20Character%20Recognition%20(OCR)/Handwriting%20Recognition/Images/Input%20Character%20Length%20Distribution%20for%20Training%20Dataset.png) _Character Length for Evaluation Dataset:_ ![Input Character Length for Evaluation Dataset](https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Optical%20Character%20Recognition%20(OCR)/Handwriting%20Recognition/Images/Input%20Characgter%20Length%20Distribution%20for%20Evaluation%20Dataset.png) ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.4292 \| 1.0 \| 2500 \| 0.4332 \| 0.0679 \| \| 0.2521 \| 2.0 \| 5000 \| 0.2767 \| 0.0483 \| \| 0.1049 \| 3.0 \| 7500 \| 0.2470 \| 0.0360 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.12.1 - Datasets 2.8.0 - Tokenizers 0.12.1
DunnBC22/trocr-large-printed-cmc7_tesseract_MICR_ocr	DunnBC22	2023-08-25T03:15:01Z	77	4	transformers	[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "image-to-text", "en", "base_model:microsoft/trocr-large-printed", "base_model:finetune:microsoft/trocr-large-printed", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]	image-to-text	2023-07-23T18:53:50Z	--- base_model: microsoft/trocr-large-printed tags: - generated_from_trainer model-index: - name: trocr-large-printed-cmc7_tesseract_MICR_ocr results: [] license: bsd-3-clause language: - en metrics: - cer pipeline_tag: image-to-text --- # trocr-large-printed-cmc7_tesseract_MICR_ocr This model is a fine-tuned version of [microsoft/trocr-large-printed](https://huggingface.co/microsoft/trocr-large-printed). ## Model description For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Optical%20Character%20Recognition%20(OCR)/Tesseract%20MICR%20(CMC7%20Dataset)/TrOCR_cmc7_tesseractMICR.ipynb ## Intended uses & limitations This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to test and experiment with this model, but it is at your own risk/peril. ## Training and evaluation data Dataset Source: https://github.com/DoubangoTelecom/tesseractMICR/tree/master/datasets/cmc7 Histogram of Label Character Lengths ![Histogram of Label Character Lengths](https://raw.githubusercontent.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/main/Optical%20Character%20Recognition%20(OCR)/Tesseract%20MICR%20(CMC7%20Dataset)/Images/Histogram%20of%20Label%20Character%20Length.png) ## 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: 5 ### Training results The Character Error Rate (CER) for this model is 0.004970720413999727. ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Chirayu/nl2kql	Chirayu	2023-08-25T03:06:43Z	159	0	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "code", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-11T23:00:42Z	--- license: mit tags: - code --- # What does this model do? This model converts the natural language input to Kusto (KQL) query. It is a fine-tuned CodeT5+ 220M. This model is a part of nl2query repository which is present at https://github.com/Chirayu-Tripathi/nl2query You can use this model via the github repository or via following code. More information can be found on the repository. ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import torch model = AutoModelForSeq2SeqLM.from_pretrained("Chirayu/nl2kql") tokenizer = AutoTokenizer.from_pretrained("Chirayu/nl2kql") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = model.to(device) textual_query = '''kusto: find the session ids which have duration greater than 10 and having Manoj Raheja as the owner \| conferencesessions : conference, sessionid, session_title, session_type, owner, participants, URL, level, session_location, starttime, duration, time_and_duration, kusto_affinity''' def generate_query( textual_query: str, num_beams: int = 10, max_length: int = 128, repetition_penalty: int = 2.5, length_penalty: int = 1, early_stopping: bool = True, top_p: int = 0.95, top_k: int = 50, num_return_sequences: int = 1, ) -> str: input_ids = tokenizer.encode( textual_query, return_tensors="pt", add_special_tokens=True ) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") input_ids = input_ids.to(device) generated_ids = model.generate( input_ids=input_ids, num_beams=num_beams, max_length=max_length, repetition_penalty=repetition_penalty, length_penalty=length_penalty, early_stopping=early_stopping, top_p=top_p, top_k=top_k, num_return_sequences=num_return_sequences, ) query = [ tokenizer.decode( generated_id, skip_special_tokens=True, clean_up_tokenization_spaces=True, ) for generated_id in generated_ids ][0] return query ```
tyayoi/xlm-roberta-base-finetuned-panx-all	tyayoi	2023-08-25T03:05:19Z	101	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-21T11:01:34Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all 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. --> # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1761 - F1: 0.8555 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.303 \| 1.0 \| 835 \| 0.1887 \| 0.8212 \| \| 0.1582 \| 2.0 \| 1670 \| 0.1708 \| 0.8409 \| \| 0.1034 \| 3.0 \| 2505 \| 0.1761 \| 0.8555 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.13.3
Nilkanth014/ppo-LunarLander-v2	Nilkanth014	2023-08-25T03:00:07Z	4	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-24T21:16:04Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 282.56 +/- 15.68 name: mean_reward verified: false --- # 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 ... ```
tyayoi/xlm-roberta-base-finetuned-panx-de-fr	tyayoi	2023-08-25T02:53:58Z	104	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-21T09:18:03Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr 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. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1642 - F1: 0.8561 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2932 \| 1.0 \| 715 \| 0.1829 \| 0.8220 \| \| 0.1486 \| 2.0 \| 1430 \| 0.1612 \| 0.8463 \| \| 0.0925 \| 3.0 \| 2145 \| 0.1642 \| 0.8561 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.13.3
rohn132/ppo-SnowballTarget	rohn132	2023-08-25T02:49:11Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-08-25T02:49:03Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: rohn132/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
lukelarue/dqn-SpaceInvadersNoFrameskip-v4	lukelarue	2023-08-25T02:48:59Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-25T02:48:25Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 626.50 +/- 166.78 name: mean_reward verified: false --- # 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 Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga lukelarue -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga lukelarue -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga lukelarue ``` ## 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', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
debadas/dog	debadas	2023-08-25T02:35:28Z	1	1	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-08-25T02:28:07Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - debadas/dog These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.
ad019el/m2m100_418M-finetuned-tq-to-ar-1	ad019el	2023-08-25T02:24:05Z	4	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "generated_from_trainer", "base_model:ad019el/m2m100_418M-finetuned-tq-to-ar", "base_model:finetune:ad019el/m2m100_418M-finetuned-tq-to-ar", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-08-23T02:51:54Z	--- base_model: ad019el/m2m100_418M-finetuned-tq-to-ar tags: - generated_from_trainer metrics: - bleu model-index: - name: m2m100_418M-finetuned-tq-to-ar-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # m2m100_418M-finetuned-tq-to-ar-1 This model is a fine-tuned version of [ad019el/m2m100_418M-finetuned-tq-to-ar](https://huggingface.co/ad019el/m2m100_418M-finetuned-tq-to-ar) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2002 - Bleu: 3.6349 - Gen Len: 35.5271 ## 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-06 - 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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| 2.7537 \| 0.71 \| 500 \| 2.2710 \| 4.2969 \| 35.4312 \| \| 2.6442 \| 1.42 \| 1000 \| 2.2373 \| 4.0784 \| 35.1062 \| \| 2.6329 \| 2.13 \| 1500 \| 2.2257 \| 3.8894 \| 36.225 \| \| 2.564 \| 2.84 \| 2000 \| 2.2210 \| 3.5513 \| 36.076 \| \| 2.5352 \| 3.56 \| 2500 \| 2.2151 \| 3.7339 \| 35.0885 \| \| 2.4991 \| 4.27 \| 3000 \| 2.2078 \| 3.4662 \| 36.3333 \| \| 2.4782 \| 4.98 \| 3500 \| 2.2100 \| 3.3332 \| 36.4062 \| \| 2.4363 \| 5.69 \| 4000 \| 2.2085 \| 3.3587 \| 36.3135 \| \| 2.4411 \| 6.4 \| 4500 \| 2.2034 \| 3.8744 \| 34.5073 \| \| 2.4002 \| 7.11 \| 5000 \| 2.2036 \| 3.6693 \| 36.3448 \| \| 2.3841 \| 7.82 \| 5500 \| 2.2030 \| 3.7486 \| 35.076 \| \| 2.3619 \| 8.53 \| 6000 \| 2.1970 \| 3.5687 \| 35.8271 \| \| 2.3627 \| 9.25 \| 6500 \| 2.2016 \| 3.5394 \| 35.3583 \| \| 2.3451 \| 9.96 \| 7000 \| 2.1996 \| 3.5863 \| 34.9271 \| \| 2.3323 \| 10.67 \| 7500 \| 2.2002 \| 3.6349 \| 35.5271 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
tyayoi/xlm-roberta-base-finetuned-panx-de	tyayoi	2023-08-25T02:13:35Z	135	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-21T09:01:45Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8606487530534567 --- <!-- 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-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1409 - F1: 0.8606 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2572 \| 1.0 \| 525 \| 0.1538 \| 0.8187 \| \| 0.1233 \| 2.0 \| 1050 \| 0.1475 \| 0.8492 \| \| 0.0796 \| 3.0 \| 1575 \| 0.1409 \| 0.8606 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.13.3
TaeLeeKyung/KoSimCSE-roberta-multitask-marketing-lms	TaeLeeKyung	2023-08-25T02:09:51Z	9	0	sentence-transformers	[ "sentence-transformers", "pytorch", "roberta", "sentence-similarity", "marketing", "sts", "nli", "ko", "dataset:TaeLeeKyung/ko_marketing_lms_dataset", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-08-25T01:58:20Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - marketing - sts - nli datasets: - TaeLeeKyung/ko_marketing_lms_dataset language: - ko --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 702 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 4, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 141, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': True}) with Transformer model: RobertaModel (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 -->
Unmand/procare_referrer_org_build2	Unmand	2023-08-25T02:04:44Z	0	0	spacy	[ "spacy", "text-classification", "en", "region:us" ]	text-classification	2023-08-25T01:36:02Z	--- tags: - spacy - text-classification language: - en model-index: - name: en_procare_referrer_organisation results: [] --- \| Feature \| Description \| \| --- \| --- \| \| Name \| `en_procare_referrer_organisation` \| \| Version \| `0.0.0` \| \| spaCy \| `>=3.5.4,<3.6.0` \| \| Default Pipeline \| `textcat_multilabel` \| \| Components \| `textcat_multilabel` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| n/a \| \| License \| n/a \| \| Author \| [n/a]() \| ### Label Scheme <details> <summary>View label scheme (726 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `textcat_multilabel` \| `H D PROJECTS PTY LTD`, `McCabe Curwood`, `Dept of Education, Skills & Employment`, `StateCover Mutual Limited`, `Perth Orthopaedic & Sports Medicine`, `Queensland Child Care Service Pty Ltd Ttee`, `Allianz Australia Insurance Limited c/- Jensen McConaghy Lawyers`, `Catholic Care Diocese of Broken Bay`, `Helping Hand New Aged Care`, `Suncorp Life`, `Qantas Airways Limited`, `Department of Defence`, `Master Builders Association of SA`, `HWL Ebsworth Lawyers`, `Alexander Watson`, `Zoetis`, `RSL Care`, `P&N Bank`, `University of NSW`, `Uber Technologies, Inc.`, `Finlay Plumbing Services Pty Ltd`, `Hays Specialist Recruitment`, `KENNARDS HIRE PTY LIMITED`, `Carer Solutions Australia`, `Unitingcare`, `No. 1 Riverside Quay Proprietary Limited`, `Gallagher Basset`, `Department of the Chief MInister and Cabinet`, `CHEP Australia`, `Minda Incorporated`, `The Star`, `Tas Water`, `Feros Care`, `Roshana Group`, `Atradius Crédito y Caución S.A de Seguros y Reaseguros`, `Services Australia`, `RT Consulting`, `The Australian Electoral Commission`, `Federal Court of Australia`, `NRMA INSURANCE`, `Catholic Education Office`, `Svitzer Australia Pty Ltd`, `QBE acting as the agent of NSW Self Insurance Corporation`, `LAWRENCE & HANSON`, `UnitingCare Queensland`, `LibertyGFG`, `Australian Tax Office`, `Alvaro Transport Pty Ltd`, `GIO Workers Compensation ACT`, `Cso Diocese Of Broken Bay`, `Glencore`, `EASTERN HOSPITAL`, `BOC Limited, a member of the Linde Group`, `INVOCARE AUSTRALIA PTY LIMITED`, `UNITRANS ASIA PACIFIC PTY LTD`, `Services Australia (Dept of Human Services)`, `VEOLIA ENVIRONMENTAL SERVICES (AUSTRALIA) PTY LTD `, `Vickilynn Pty Ltd`, `Coles Team Cover`, `MLC Life Insurance`, `Sparke Helmore Lawyers`, `RSL Lifecare Limited`, `QBE Workers Compensation TAS`, `Kimberley Clark Australia`, `The Personnel Group Ltd`, `Insurance Australia Group`, `Canberra Sand & Gravel`, `Viva Energy Australia Pty Ltd`, `Moran Aged Care Engadine`, `Australian Taxation Office`, `Youis Group Pty Ltd`, `Cleanaway`, `Mosaic Brands (Rockmans)`, `Children Hospital Foundation`, `Civil Aviation Safety Authority`, `QBE Workers Compensation WA`, `United Protestant Association`, `PSC Capital Insurance Brokers`, `Woolworths Group Limited`, `Kilcoy Global Foods`, `American Express Australia Limited`, `Palios Meegan Nicholson`, `Uniting`, `Coles Group Supply Chain Pty Ltd`, `QBE`, `OBE Organic`, `Cyprium Metals Limited`, `Kincare Health Services Pty Ltd`, `StateCover Mutual Ltd`, `FIRE RESCUE VICTORIA`, `N2N Claims Solutions`, `WesFarmers – Group TeamCover`, `NDIS Quality and Safeguards Commission`, `HD Projects Pty Ltd`, `St Finn Barr's Catholic Primary School - Lanceston`, `Power and Water Corporation`, `EML VIC Pty Ltd`, `Wanton Kearney`, `Kmart Australia Ltd`, `Territory Families – Housing & Communities`, `Calvary Community Care`, `Sedgwick`, `Leonora Contracting P/L`, `NSW Health Pathology`, `Kilcoy Pastoral Company Ltd`, `GIO CTP ACT`, `DXC Claims Management Services - VIC`, `Schindler Lifts Australia Pty Ltd`, `Meridian Lawyers`, `GIO Workers Compensation WA`, `AUB Group Limited`, `Coateshire`, `Aurizon`, `JWLand`, `Trusted Support Coordination`, `Gosford Quarries Pty Ltd`, `GIO NSW Workers Compensation`, `DESE`, `Busways Group`, `Gallagher Bassett Workers Compensation NSW`, `Allianz Australia Insurance Limited C/- McInnes Wilson Lawyers`, `oOh!Media`, `West Gate Tunnel Project`, `KOMATSU MARKETING SUPPORT AUST`, `Mills Oakley Lawyers`, `Hall & Wilcox`, `Skybridge Group Pty Limited`, `Retirement Living Business & Financial Services`, `Allianz Workers Compensation NT`, `Environmental Industries Pty Ltd`, `EML Workers Insurance NSW`, `Department of Agriculture, Water and the Environment`, `MS Australia`, `CSIRO`, `Orange Health Service`, `AHI Insurance`, `Bupa`, `Allianz Australia Workers Compensation (Victoria) Ltd`, `Cappello Civil Contracting Services Pty Ltd`, `LAF Group`, `RTozerconsulting`, `St Michaels College`, `Gallagher Bassett for Opal Healthcare`, `Department of Families, Fairness and Housing`, `WESTHAVEN LIMITED`, `Integrity Care`, `GPC Asia Pacific`, `Department of Primary Industries`, `Mosaic Brands Limited`, `QBE Workers Compensation NT`, `Coredev`, `South Western Sydney Local Health District`, `CGU Workers Compensation ACT`, `Tas Prison Service`, `Sonic Healthcare`, `Workcover C/BT Lawyers`, `PSC WCS`, `CPB Contractors Pty Ltd`, `Cookie Steelfixing and Construction`, `Warner Bros`, `CGU Workers Compensation NT`, `CMET`, `AnglicareSA`, `St Vincent’s Care Services Carseldine`, `Tasmanian Catholic Education Office`, `Allianz Australia Insurance Ltd`, `Roussos Legal Advisory`, `BGIS Technical Services`, `AAMI NSW CTP`, `Wotton Kearney`, `Galllgher Bassett Workers Compensation VIC`, `Brisbane Fire Pty Ltd`, `QBE Workers Compensation NSW`, `Sunshine Coast Hospital and Health Service`, `Oaks Hotels & Resorts Limited - 9004`, `Ausgrid`, `Boral Limited`, `Aerison Pty Ltd`, `Cooper Grace Ward Lawyers`, `Hsswa Pty Ltd`, `Weir Minerals Australia Ltd`, `Labour Force Pty Ltd`, `Barry Nilsson Lawyers`, `Liberty Oil Australia Pty Ltd`, `ABPhillips`, `Austral Risk`, `AAI Limited trading as GIO - Agent for the Workers Compensation Nominal Insurer`, `OCEAN GARDENS INC`, `Roshana Group Pty Ltd`, `GIO CTP NSW`, `Lachlan Shire Council`, `Allianz Workers Compensation WA`, `United Equipment Pty Ltd`, `PFD FOOD SERVICES PTY LTD`, `Phoenix Insurance Brokers`, `Blumers`, `Department of Home Affairs`, `Anglo Coal (Grosvenor Management) Pty Ltd c/- Ashurst Australia`, `Anglicare Southern QLD`, `Lifetime Support`, `The Trustee for The Roshana Family Trust`, `Zurich Australian Insurance Ltd`, `Dept of Education & Training - School Cleaners`, `DXC Claims Management Services`, `The Medical Clinic Millicent`, `Melbourne Water`, `COMPASS GROUP AUSTRALIA PTY LTD`, `Andreasens Green NSW Andreasens Green QLD`, `Astridge and Murray`, `EML Plus`, `Philips Electronics P/L`, `ISS Facility Services Australia Ltd`, `Busy Bees Early Learning Australia Pty Ltd`, `Coates Hire`, `Sydney Trains`, `Catholic Schools Parramatta Diocese Limited`, `CGU Workers Compensation TAS`, `Mercer`, `COFFS HARBOUR SUPPORT SERVICES LTD`, `I-MED GROUP`, `One Path`, `Transport Accident Commission`, `Department of Corporate and Digital Development Northern Territory Government`, `Boral Insurance Pty Limited`, `Department of Justice`, `AB Phillips Pty Ltd`, `Irwin & Hartshorn`, `Pacific Labour Facility`, `Suncorp Staff Pty Ltd`, `Vilis Bakery`, `NRMA`, `The Hospitals Contribution Fund Of Australia Ltd`, `SCE Group`, `Our Lady of Mercy College Parramatta`, `DOSER Freight Forwarding`, `Employers Mutual NSW Limited`, `Cappello Hydraulics & Civil Pty Ltd`, `Buderim Kindergarten`, `ACT Recycling Pty Ltd`, `Bupa Medical Visa Services`, `Allianz CTP SA`, `Auspost`, `Liverpool Plains Shire Council`, `Corporate Services Network Pty Ltd`, `DP World Australia Pty Ltd`, `Complete Personnel Recruitment`, `DXC Integrated Services`, `QBE Workers Compensation - ACT`, `BINGO PTY LTD`, `The Arnott’s Group`, `EML Agent for icare Workers Insurance`, `IHG Irwin Hartshorn Group`, `Civilmart`, `ORAMS Agencies`, `Liberty GFG`, `QBE NSW Treasury Managed Fund`, `EML (NSW Treasury Managed Fund)`, `Hays Recruitment`, `Mosaic Group Ltd Pty`, `BlueCare`, `Gallagher Bassett Services`, `Ernst & Young (EY)`, `Cootharinga North Queensland`, `BUPA AGED CARE AUSTRALIA P/L`, `Toll Self Insurance`, `Corporate Services Network`, `ACT GOV`, `SA Health Northern Adelaide Local Health Network`, `Inghams Enterprises Pty Ltd`, `Centrewest Insurance Brokers`, `Department of Foreign Affairs and Trade (DFAT)`, `RSL Life Care`, `Star of the Sea School`, `Chubb`, `Suncorp CTP QLD`, `JACANA ENERGY`, `Toll Group`, `Corporeal Health`, `Mosaic Brands (Noni B Limited)`, `QBE CTP Insurance`, `Q Super`, `Bartier Perry Lawyers`, `Queensland Government`, `Department of Health and Human Services Tasmania`, `Hall and Wilcox Lawyers`, `Griffin Coal`, `Cappello Commercial Hydraulics and Civil Pty Ltd`, `Bolton Clarke`, `Australian Unity`, `Gallagher Bassett Services Pty Ltd`, `St John Ambulance Western Australia Ltd`, `Geocon Group Pty Ltd`, `Allianz Australia Insurance Limited c/ Jensen McConaghy Lawyers`, `UAA Pty Ltd`, `Tamex Transport Services Pty Ltd`, `WFI Insurance Limited`, `Programmed Skilled Workforce Limited`, `Bartier Perry`, `Australian Competition & Consumer Commission`, `Queensland Health`, `Holcim (Australia) Pty Ltd`, `Southern NSW Local Health District`, `Blue Care`, `Gallagher Bassett Workers Compensation VIC`, `Point Insurance`, `Workers Compensation & Risk Specialists (WCRS) services render for Philips electronics P/L`, `Country Wide Insurance Brokers (CWIB)`, `Allianz Australia Insurance Ltd C/ - Moray and Agnew Lawyers`, `CHUBB AUSTRALASIA`, `Sirius Support & Industrious People`, `BORG MANUFACTURING P/L`, `Department of Climate Change, Energy, the Environment and Water`, `Hireup Pty. Ltd.`, `Workcover QLD`, `Greenham Tasmania `, `Fantastic Furniture Ltd`, `CGU Workers Compensation VIC`, `Lawson Risk Management Services Pty Ltd`, `SGP Civil`, `Moray & Agnew`, `Edwards Michael Lawyers`, `Jensen McConarchy`, `Cyprium Metals`, `Hunter New England Local Health District`, `EML TMF, Insurance for NSW`, `RACQ Insurance`, `Blue Care ATF The Uniting Church in Aust. Property Trust (Q)`, `ENERGYAUSTRALIA SERVICES P/L`, `AAMI CTP`, `Bupa Asia Pacific`, `The Good Shepherd Home`, `Department of Corporate and Digital Development`, `Allianz CTP Claims NSW`, `Sedgwick Australia`, `Racing NSW`, `GCI Group`, `Australia Post`, `Coles Group Limited`, `Minter Ellison`, `MCCOLL'S OPERATIONS P/L`, `Apprenticeship Support Australia`, `AIA Australia Limited`, `Ernst & Young Services Pty Limited`, `North Metropolitan Health Service`, `St Vincent de Paul Society Canberra/Goulburn (Inc)`, `DP WORLD AUSTRALIA FREMANTLE TERMINAL`, `Moray and Agnew`, `Mosaic Group`, `Ovato`, `ACT Formwork Pty Ltd`, `DORMAKABA AUSTRALIA PTY LTD`, `Jones Harley Toole`, `QBE Accident and Health`, `Crawford Legal`, `REA Group Ltd`, `Amadeus IT Pacific Pty Ltd`, `DXC Integrated Services Victoria Pty Ltd`, `Vellex Pty Ltd`, `3M Australia`, `RTC Consulting`, `Somerset College Ltd`, `Bupa Care Services`, `IKEA North Lakes`, `Australian Criminal Intelligence Commission`, `McInnes Wilson Lawyers`, `UnitingCare Queensland `, `Anglican Community Care Incorporated (trading as ac.care)`, `Electrolux Home Products Pty Ltd`, `Gen Leads`, `FUSE RECRUITMENT MELBOURNE P/L`, `Zurich Financial Services Australia Limited`, `Wesfarmers Group TeamCover`, `Connect Infrastructure`, `Oji Fibre Solutions (Aus) Pty Ltd`, `Quality Bakers Australia Pty Limited`, `Workers Compensation & Risk Specialists`, `Civil Aviation Safety Authority (CASA)`, `Endeavour Foundation`, `The Territory Boundless Possible`, `Territory Families – Housing & Communities`, `Ampol Australia Petroleum Pty Ltd`, `Seven Network (Operations) Ltd`, `HopgoodGanim Lawyers`, `Coal Mines Insurance`, `QBE Insurance Australia`, `UGL Limited`, `QBE Accident and Health `, `C.INC`, `Ikea Logan`, `VERO`, `Geodis Australia`, `McCabes Lawyers`, `Programmed`, `UNSW Canberra`, `EML, Agent for ReturnToWorkSA`, `TEST ORG 2. EML Workers Insurance NSW`, `Kings Group`, `Maney Transport`, `South Western Sydney Lhd`, `Force Fire and Safety Pty Ltd`, `Astridge & Murray Solicitors `, `Rankin Ellison Lawyers`, `EML Insurance`, `ACCC/AER`, `Facilities First`, `Turks Legal`, `Jenson McConaghy Lawyers`, `CGU Insurance`, `AAI Limited trading as GIO`, `BP Australia Limited C/ Collin Biggers & Paisley Lawyers`, `O’Neill & Brown Electrical Services Pty Ltd`, `St Kilda PCYC`, `Justice Services Pty Ltd`, `American Express International Inc`, `Gillis Delaney Lawyers`, `Cabra Dominican College Ltd.`, `Trident Services Cleaning Pty Ltd`, `Hicksons Lawyers`, `Healthscope Operations Pty Ltd`, `GSK CX Healthcare Pty Ltd`, `ACT Government`, `AJ Bush & Sons Pty Ltd`, `OMB Solicitors`, `EML Self Insurance`, `Cooper Grace Ward`, `GC Legal`, `Centacare Catholic Family Services`, `Etex Australia Pty Ltd`, `Allianz Australia Ltd`, `Envirolab Service`, `Ikea `, `Allianz Australia Insurance Limited`, `WorkCover Queensland`, `Allianz Workers Compensation ACT`, `GIO Workers Compensation NSW`, `GenesisCare`, `Rocklea Pressed Metal Pty Ltd `, `Australian Digital Health Agency`, `HWL Ebsworth`, `Museum and Art Gallery Northern Territory (MAGNT)`, `CSR`, `Connell`, `4cRisk`, `HBA Legal`, `Coles Supermarkets Australia Pty Ltd`, `The University of Queensland`, `VENTIA SERVICES GROUP P/L,VENT`, `Point Underwriting Agency Pty Ltd`, `Youi CTP SA`, `Allianz Workers Compensation NSW`, `Detmold Packaging Pty Ltd`, `KENNARDS HIRE PTY LTD`, `QBE CTP QLD`, `Insurance House Group`, `Kilcoy Pastoral Company Limited`, `SRG Global Mining (Australia) Pty Ltd`, `Hunter Imaging Group`, `Park Hyatt Melbourne`, `Enviro Lab`, `QBE Australia Insurance Limited`, `EML c/o Moray`, `Catholic Church Insurance Limited`, `NV EMPLOYMENT PTY LTD`, `IP Australia`, `Qantas`, `Wesfarmer Limited`, `Melton City Council`, `Workcover Employer For Special Policies`, `Allianz Australia Workers Compensation (NSW) Ltd.`, `Uniting Care Health`, `Staff Australia Payroll Services Pty Ltd`, `WN Group`, `Infrabuild`, `Western NSW Local Health District`, `APS Group`, `DXC Claims Management Services - VIC`, `GIO`, `Northern Adelaide Local Health Network `, `Austbrokers Canberra`, `Department of Treasury and Finance Northern Territory Government`, `PSC Workers Compensation & Consulting`, `Alinta Energy`, `Sunline ACT Pty Ltd`, `Allianz Australia Workers' Compensation (Victoria)`, `Suncorp`, `JW Land Construction`, `Comcare - VIC`, `IKEA Pty Limited`, `KENNARDS HIRE`, `IRI Worldwide`, `RFI Technology Solutions`, `Engage TSS Internal Resources`, `St Vincent’s Care Services Mitchelton`, `Cappello Concreting Services Pty Ltd`, `Correct Care Australasia P/L`, `Coal Services`, `VELLA TRANSPORT ADMINISTRATION PTY LTD`, `CGU Workers Compensation WA`, `CORPORATE SERVICE NETWORK`, `BGIS`, `SCENTRE LIMITED`, `Employers Mutual Limited`, `RAPE & DOMESTIC VIOLENCE SERVICES AUSTRALIA`, `PSC Insurance`, `Allianz Australia Insurance Ltd ACT`, `Big W`, `Coverforce Pty Ltd`, `AAMI SA CTP Claims`, `EML Workers Insurance`, `Emjay Insurance Brokers`, `EML Victoria`, `WorkSafe Claims and Recovery Support team`, `Adcor`, `Territory Families, Housing and Communities (TFHC)`, `Nazareth Catholic Community`, `Gallagher Bassett Workers Compensation SA`, `INVOCARE AUSTRALIA P/L`, `Hardman Risk Management`, `The Sydney Childrens Hospital Network`, `The Junction Works Limited`, `PEM DEMO`, `Queensland Ambulance Service`, `Fel Child Care Centres 1 Pty Ltd`, `Allianz CTP QLD`, `Moray & Agnew Lawyers`, `Programmed Maintenance Services Ltd (Self Insured)`, `iag`, `Barnardos`, `eReports `, `Youi Pty Ltd`, `HM Focus Pty Ltd`, `Allianz Workers Compensation VIC`, `iCare Workers Insurance`, `Procare Group`, `Kemp & Co Lawyers`, `AAMI Insurance`, `Combined Insurance`, `STAWELL GOLD MINES P/L`, `QBE CTP NSW`, `SA Health`, `Gilshenan & Luton Legal Practice`, `Genesis Care`, `SOUTH AUSTRALIA POLICE`, `Wollongong City Council`, `TUTT BRYANT GROUP LTD`, `Endeavour Energy`, `Tasmanian Health Service`, `IC Formwork Services Pty Ltd`, `Humdrum`, `Comcare`, `The Gowrie (Qld) Inc`, `Australian Government Department of Education, Skills and Employment`, `Gair Legal`, `Dept of Territory Families, Housing and Communities`, `McArthur River Mining PTY Ltd`, `Kincare Management Pty Ltd`, `CFA`, `Department of Territory Families, Housing and Communities Division Library & Archives NT`, `Department for Education and Child Development`, `Core Building Group Pty Ltd`, `ACH Group`, `Busy Bees Australia Operations Pty Ltd.`, `Wesfarmers Ltd`, `JBC Corporate`, `NULL`, `No Employer - ADL`, `BT Lawyers`, `InfraBuild Steel Centre`, `Kimberly-Clark`, `Tas TAFE`, `EML National Self Insurance`, `National Disability Insurance Agency`, `Colin Biggers & Paisley Pty`, `DP World Brisbane Pty Ltd`, `Australian Trade and Investment Commission (Austrade)`, `Allianz Australia Limited c/- McInnes Wilson Lawyers`, `Community Solutions`, `RFI`, `RACQ Insurance Limited ABN 50 009 704 152`, `AAI Limited trading as GIO`, `Gallagher Bassett Services Workers Compensation Vic Pty Ltd`, `Department of Infrastructure, Transport and Regional Development`, `PSC Insurance Group`, `Allianz CTP NSW`, `CSR Limited`, `Kimberly-Clark Australia P/L`, `Hall and Willcox Lawyers`, `Page Seager Lawyers`, `Iconic Hotels Management`, `St John Medical Centre`, `Department of Veterans Affairs`, `Allianz QLD CTP`, `Morgan & Agnew Lawyers`, `Bureau of Meteorology`, `Forest Coach Lines Pty / Ltd`, `Shaw's Darwin Transport Pty Ltd`, `Dynamic Diesel Mechanical Services Pty Ltd`, `Hall & Wilcox Lawyers`, `Moran Aged Care`, `DJarvis@shepelectrical.com.au`, `Gallagher Bassett Self Insurance NSW`, `EML as agent for icare Workers Insurance NSW`, `Minter Ellison Lawyers`, `Lee Legal Group`, `Child and Adolescent Health Service (CAHS)`, `Holman Webb Lawyers`, `Dept of Home Affairs`, `QSuper`, `TIO Motor Accidents Compensation `, `Allianz Australia Workers' Compensation (Victoria) Limited`, `Perpetual Limited`, `Barwang Pty Ltd`, `CTP QLD Claims Division`, `InvoCare`, `Australian Border Force`, `I MED Radiology Network`, `Ensure Pty Ltd`, `CITY OF PALMERSTON`, `AKUBRA HATS PTY LTD`, `Secom Australia`, `GIO Workers Compensation NT`, `Pialligo Estate`, `Berry Buddle Wilkins`, `Department of Infrastructure, Transport, Regional Development and Communications`, `Aussie Skip Bins Services P/L`, `BGIS Pty Ltd`, `NSW Police Force`, `GIO Workers Compensation TAS`, `Eighteen33 Pty Ltd`, `Crown Law`, `Paramatta Council`, `Northern Territory Government`, `Australian Electoral Commission`, `Department of Health`, `Hunt & Hunt Lawyers`, `Batemans Bay Soldiers Club`, `Allianz Workers Compensation Tasmania`, `SMK Lawyers`, `Envirolab Group`, `WorkSafe Victoria`, `Allianz Australia Insurance Limited, c/- Moray & Agnew`, `Allianz Australia Insurance Limited ABN 15 000 122 850, c/- Moray & Agnew`, `City of Parramatta`, `UES International Pty Ltd`, `Westpac Group`, `Logistics & Stores (Mailroom, Stores & Transport) Services CHW`, `Device Technologies Australia Pty Ltd`, `Willis Towers Watson`, `Hsswa Pty Ltd & HSS Resources Pty Ltd & Other`, `Kingspan Water & Energy Pty Limited`, `SAPOL`, `Guild Insurance`, `Westpac Banking Group`, `St Hilarion Aged Care`, `AAI Limited trading as GIO - Agent for the Workers Compensation Nominal Insurer ABN 83 564 379 108`, `Roshana Pty Ltd`, `QBE Insurance (Australia) Limited (ABN 78003191035)`, `Service Australia`, `BOC Limited `, `HWLE Lawyers`, `NRMA CTP NSW`, `RACQ Insurance Limited ABN 50009704152/ C- Cooper Grace Ward`, `CALVARY ADMINISTRATION PTY LTD`, `Cappello Group`, `Wesfarmers Limited`, `GIO NSW CTP `, `FK Gardner Services (Qld) Pty Ltd`, `Challenge Implements Holdings`, `Bartier Perry Pty Limited`, `Chubb Insurance Australia Limited`, `EMP Michael Lawyers`, `I-MED RADIOLOGY NETWORK LIMITED`, `Gilchrist Connell Legal`, `Premier Office Relocations`, `Nominal Defendant c/- Jensen McConaghy Lawyers`, `Detmold Mental Health Training`, `EML`, `Premise`, `Balance Rehab`, `Xchanging Workers Compensation - NSW`, `Coogee Chemicals Pty Ltd`, `Safe Work Australia`, `Jensen McConaghy Lawyers`, `Hawkesbury City Council`, `Toll Global Express`, `The Corporation of the Synod of the Diocese of Brisbane`, `NRMA CTP SA`, `Ambulance Victoria`, `APSystems`, `Austbrokers (Finsura)`, `SCENTRE GROUP`, `Ikea Australia`, `Department of Treasury and Finance`, `Gallagher Bassett Services Workers Compensation NSW`, `NONI B HOLDINGS PTY LIMITED`, `QBE Workers Compensation SA`, `The Star Entertainment Group Self Insurance Unit`, `Catholic Care Diocese of Bathurst`, `GAIR LEGAL PTY LIMITED`, `QBE CTP SA`, `Wesfarmers Group`, `Rod Pilon Transport`, `TG Legal`, `Department of the Prime Minister and Cabinet`, `UNSW`, `RACQ Group`, `REMONDIS Australia Pty Ltd`, `Australian Federal Police`, `Marshall & Brougham Constructions `, `Chandler Macleod Group`, `University of Tasmania`, `Goodman Fielder Pty Limited`, `SONIC HEALTHCARE GROUP`, `Hastings Medical Centre`, `Hospitality Employers Mutual`, `HCF`, `Colin Biggers Paisley Lawyers`, `Department Veterans Affairs`, `Maddocks Lawyers`, `SRG Group`, `Australian Personnel Solutions (APS Group)`, `EY Business Solutions Pty Ltd`, `National Indigenous Australians Agency`, `St Catherine's School, Berwick`, `Transport for NSW`, `South Australian Native Titles Services` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `CATS_SCORE` \| 32.28 \| \| `CATS_MICRO_P` \| 71.89 \| \| `CATS_MICRO_R` \| 23.49 \| \| `CATS_MICRO_F` \| 35.41 \| \| `CATS_MACRO_P` \| 7.06 \| \| `CATS_MACRO_R` \| 3.40 \| \| `CATS_MACRO_F` \| 4.32 \| \| `CATS_MACRO_AUC` \| 32.28 \| \| `TEXTCAT_MULTILABEL_LOSS` \| 7.88 \|
hemlataC/llama-2-7b-hindie2-v4	hemlataC	2023-08-25T02:04:02Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-25T02:02:53Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0
Bugsys0302/CharactersLoRA	Bugsys0302	2023-08-25T01:54:36Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-25T01:54:36Z	--- license: creativeml-openrail-m ---
Vasanth/idefics-mscoco-captioner	Vasanth	2023-08-25T01:49:11Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-25T01:49:08Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: ['lm_head', 'embed_tokens'] - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0
jimmyofdoom/rl_course_vizdoom_health_gathering_supreme	jimmyofdoom	2023-08-25T01:47:33Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-25T01:47:25Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.17 +/- 5.17 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r jimmyofdoom/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
AdanLee/q-Taxi-v3	AdanLee	2023-08-25T01:41:15Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-25T01:30:27Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.70 name: mean_reward verified: false --- # Q-Learning Agent playing Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ``` from urllib.error import HTTPError from huggingface_hub import hf_hub_download def load_from_hub(repo_id: str, filename: str) -> str: """ Download a model from Hugging Face Hub. :param repo_id: id of the model repository from the Hugging Face Hub :param filename: name of the model zip file from the repository """ # Get the model from the Hub, download and cache the model on your local disk pickle_model = hf_hub_download(repo_id=repo_id, filename=filename) with open(pickle_model, "rb") as f: downloaded_model_file = pickle.load(f) return downloaded_model_file model = load_from_hub(repo_id="AdanLee/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"]) ```
dt-and-vanilla-ardt/ardt-vanilla-arrl_train_halfcheetah_high-2508_0016-66	dt-and-vanilla-ardt	2023-08-25T01:27:59Z	32	0	transformers	[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2023-08-24T23:17:55Z	--- tags: - generated_from_trainer model-index: - name: ardt-vanilla-arrl_train_halfcheetah_high-2508_0016-66 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. --> # ardt-vanilla-arrl_train_halfcheetah_high-2508_0016-66 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
Unmand/procare_referrer_organisation	Unmand	2023-08-25T00:49:54Z	2	1	spacy	[ "spacy", "text-classification", "en", "region:us" ]	text-classification	2023-07-11T00:57:04Z	--- tags: - spacy - text-classification language: - en model-index: - name: en_procare_referrer_organisation results: [] --- \| Feature \| Description \| \| --- \| --- \| \| Name \| `en_procare_referrer_organisation` \| \| Version \| `0.0.0` \| \| spaCy \| `>=3.5.4,<3.6.0` \| \| Default Pipeline \| `textcat_multilabel` \| \| Components \| `textcat_multilabel` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| n/a \| \| License \| n/a \| \| Author \| [n/a]() \| ### Label Scheme <details> <summary>View label scheme (726 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `textcat_multilabel` \| `H D PROJECTS PTY LTD`, `McCabe Curwood`, `Dept of Education, Skills & Employment`, `StateCover Mutual Limited`, `Perth Orthopaedic & Sports Medicine`, `Queensland Child Care Service Pty Ltd Ttee`, `Allianz Australia Insurance Limited c/- Jensen McConaghy Lawyers`, `Catholic Care Diocese of Broken Bay`, `Helping Hand New Aged Care`, `Suncorp Life`, `Qantas Airways Limited`, `Department of Defence`, `Master Builders Association of SA`, `HWL Ebsworth Lawyers`, `Alexander Watson`, `Zoetis`, `RSL Care`, `P&N Bank`, `University of NSW`, `Uber Technologies, Inc.`, `Finlay Plumbing Services Pty Ltd`, `Hays Specialist Recruitment`, `KENNARDS HIRE PTY LIMITED`, `Carer Solutions Australia`, `Unitingcare`, `No. 1 Riverside Quay Proprietary Limited`, `Gallagher Basset`, `Department of the Chief MInister and Cabinet`, `CHEP Australia`, `Minda Incorporated`, `The Star`, `Tas Water`, `Feros Care`, `Roshana Group`, `Atradius Crédito y Caución S.A de Seguros y Reaseguros`, `Services Australia`, `RT Consulting`, `The Australian Electoral Commission`, `Federal Court of Australia`, `NRMA INSURANCE`, `Catholic Education Office`, `Svitzer Australia Pty Ltd`, `QBE acting as the agent of NSW Self Insurance Corporation`, `LAWRENCE & HANSON`, `UnitingCare Queensland`, `LibertyGFG`, `Australian Tax Office`, `Alvaro Transport Pty Ltd`, `GIO Workers Compensation ACT`, `Cso Diocese Of Broken Bay`, `Glencore`, `EASTERN HOSPITAL`, `BOC Limited, a member of the Linde Group`, `INVOCARE AUSTRALIA PTY LIMITED`, `UNITRANS ASIA PACIFIC PTY LTD`, `Services Australia (Dept of Human Services)`, `VEOLIA ENVIRONMENTAL SERVICES (AUSTRALIA) PTY LTD `, `Vickilynn Pty Ltd`, `Coles Team Cover`, `MLC Life Insurance`, `Sparke Helmore Lawyers`, `RSL Lifecare Limited`, `QBE Workers Compensation TAS`, `Kimberley Clark Australia`, `The Personnel Group Ltd`, `Insurance Australia Group`, `Canberra Sand & Gravel`, `Viva Energy Australia Pty Ltd`, `Moran Aged Care Engadine`, `Australian Taxation Office`, `Youis Group Pty Ltd`, `Cleanaway`, `Mosaic Brands (Rockmans)`, `Children Hospital Foundation`, `Civil Aviation Safety Authority`, `QBE Workers Compensation WA`, `United Protestant Association`, `PSC Capital Insurance Brokers`, `Woolworths Group Limited`, `Kilcoy Global Foods`, `American Express Australia Limited`, `Palios Meegan Nicholson`, `Uniting`, `Coles Group Supply Chain Pty Ltd`, `QBE`, `OBE Organic`, `Cyprium Metals Limited`, `Kincare Health Services Pty Ltd`, `StateCover Mutual Ltd`, `FIRE RESCUE VICTORIA`, `N2N Claims Solutions`, `WesFarmers – Group TeamCover`, `NDIS Quality and Safeguards Commission`, `HD Projects Pty Ltd`, `St Finn Barr's Catholic Primary School - Lanceston`, `Power and Water Corporation`, `EML VIC Pty Ltd`, `Wanton Kearney`, `Kmart Australia Ltd`, `Territory Families – Housing & Communities`, `Calvary Community Care`, `Sedgwick`, `Leonora Contracting P/L`, `NSW Health Pathology`, `Kilcoy Pastoral Company Ltd`, `GIO CTP ACT`, `DXC Claims Management Services - VIC`, `Schindler Lifts Australia Pty Ltd`, `Meridian Lawyers`, `GIO Workers Compensation WA`, `AUB Group Limited`, `Coateshire`, `Aurizon`, `JWLand`, `Trusted Support Coordination`, `Gosford Quarries Pty Ltd`, `GIO NSW Workers Compensation`, `DESE`, `Busways Group`, `Gallagher Bassett Workers Compensation NSW`, `Allianz Australia Insurance Limited C/- McInnes Wilson Lawyers`, `oOh!Media`, `West Gate Tunnel Project`, `KOMATSU MARKETING SUPPORT AUST`, `Mills Oakley Lawyers`, `Hall & Wilcox`, `Skybridge Group Pty Limited`, `Retirement Living Business & Financial Services`, `Allianz Workers Compensation NT`, `Environmental Industries Pty Ltd`, `EML Workers Insurance NSW`, `Department of Agriculture, Water and the Environment`, `MS Australia`, `CSIRO`, `Orange Health Service`, `AHI Insurance`, `Bupa`, `Allianz Australia Workers Compensation (Victoria) Ltd`, `Cappello Civil Contracting Services Pty Ltd`, `LAF Group`, `RTozerconsulting`, `St Michaels College`, `Gallagher Bassett for Opal Healthcare`, `Department of Families, Fairness and Housing`, `WESTHAVEN LIMITED`, `Integrity Care`, `GPC Asia Pacific`, `Department of Primary Industries`, `Mosaic Brands Limited`, `QBE Workers Compensation NT`, `Coredev`, `South Western Sydney Local Health District`, `CGU Workers Compensation ACT`, `Tas Prison Service`, `Sonic Healthcare`, `Workcover C/BT Lawyers`, `PSC WCS`, `CPB Contractors Pty Ltd`, `Cookie Steelfixing and Construction`, `Warner Bros`, `CGU Workers Compensation NT`, `CMET`, `AnglicareSA`, `St Vincent’s Care Services Carseldine`, `Tasmanian Catholic Education Office`, `Allianz Australia Insurance Ltd`, `Roussos Legal Advisory`, `BGIS Technical Services`, `AAMI NSW CTP`, `Wotton Kearney`, `Galllgher Bassett Workers Compensation VIC`, `Brisbane Fire Pty Ltd`, `QBE Workers Compensation NSW`, `Sunshine Coast Hospital and Health Service`, `Oaks Hotels & Resorts Limited - 9004`, `Ausgrid`, `Boral Limited`, `Aerison Pty Ltd`, `Cooper Grace Ward Lawyers`, `Hsswa Pty Ltd`, `Weir Minerals Australia Ltd`, `Labour Force Pty Ltd`, `Barry Nilsson Lawyers`, `Liberty Oil Australia Pty Ltd`, `ABPhillips`, `Austral Risk`, `AAI Limited trading as GIO - Agent for the Workers Compensation Nominal Insurer`, `OCEAN GARDENS INC`, `Roshana Group Pty Ltd`, `GIO CTP NSW`, `Lachlan Shire Council`, `Allianz Workers Compensation WA`, `United Equipment Pty Ltd`, `PFD FOOD SERVICES PTY LTD`, `Phoenix Insurance Brokers`, `Blumers`, `Department of Home Affairs`, `Anglo Coal (Grosvenor Management) Pty Ltd c/- Ashurst Australia`, `Anglicare Southern QLD`, `Lifetime Support`, `The Trustee for The Roshana Family Trust`, `Zurich Australian Insurance Ltd`, `Dept of Education & Training - School Cleaners`, `DXC Claims Management Services`, `The Medical Clinic Millicent`, `Melbourne Water`, `COMPASS GROUP AUSTRALIA PTY LTD`, `Andreasens Green NSW Andreasens Green QLD`, `Astridge and Murray`, `EML Plus`, `Philips Electronics P/L`, `ISS Facility Services Australia Ltd`, `Busy Bees Early Learning Australia Pty Ltd`, `Coates Hire`, `Sydney Trains`, `Catholic Schools Parramatta Diocese Limited`, `CGU Workers Compensation TAS`, `Mercer`, `COFFS HARBOUR SUPPORT SERVICES LTD`, `I-MED GROUP`, `One Path`, `Transport Accident Commission`, `Department of Corporate and Digital Development Northern Territory Government`, `Boral Insurance Pty Limited`, `Department of Justice`, `AB Phillips Pty Ltd`, `Irwin & Hartshorn`, `Pacific Labour Facility`, `Suncorp Staff Pty Ltd`, `Vilis Bakery`, `NRMA`, `The Hospitals Contribution Fund Of Australia Ltd`, `SCE Group`, `Our Lady of Mercy College Parramatta`, `DOSER Freight Forwarding`, `Employers Mutual NSW Limited`, `Cappello Hydraulics & Civil Pty Ltd`, `Buderim Kindergarten`, `ACT Recycling Pty Ltd`, `Bupa Medical Visa Services`, `Allianz CTP SA`, `Auspost`, `Liverpool Plains Shire Council`, `Corporate Services Network Pty Ltd`, `DP World Australia Pty Ltd`, `Complete Personnel Recruitment`, `DXC Integrated Services`, `QBE Workers Compensation - ACT`, `BINGO PTY LTD`, `The Arnott’s Group`, `EML Agent for icare Workers Insurance`, `IHG Irwin Hartshorn Group`, `Civilmart`, `ORAMS Agencies`, `Liberty GFG`, `QBE NSW Treasury Managed Fund`, `EML (NSW Treasury Managed Fund)`, `Hays Recruitment`, `Mosaic Group Ltd Pty`, `BlueCare`, `Gallagher Bassett Services`, `Ernst & Young (EY)`, `Cootharinga North Queensland`, `BUPA AGED CARE AUSTRALIA P/L`, `Toll Self Insurance`, `Corporate Services Network`, `ACT GOV`, `SA Health Northern Adelaide Local Health Network`, `Inghams Enterprises Pty Ltd`, `Centrewest Insurance Brokers`, `Department of Foreign Affairs and Trade (DFAT)`, `RSL Life Care`, `Star of the Sea School`, `Chubb`, `Suncorp CTP QLD`, `JACANA ENERGY`, `Toll Group`, `Corporeal Health`, `Mosaic Brands (Noni B Limited)`, `QBE CTP Insurance`, `Q Super`, `Bartier Perry Lawyers`, `Queensland Government`, `Department of Health and Human Services Tasmania`, `Hall and Wilcox Lawyers`, `Griffin Coal`, `Cappello Commercial Hydraulics and Civil Pty Ltd`, `Bolton Clarke`, `Australian Unity`, `Gallagher Bassett Services Pty Ltd`, `St John Ambulance Western Australia Ltd`, `Geocon Group Pty Ltd`, `Allianz Australia Insurance Limited c/ Jensen McConaghy Lawyers`, `UAA Pty Ltd`, `Tamex Transport Services Pty Ltd`, `WFI Insurance Limited`, `Programmed Skilled Workforce Limited`, `Bartier Perry`, `Australian Competition & Consumer Commission`, `Queensland Health`, `Holcim (Australia) Pty Ltd`, `Southern NSW Local Health District`, `Blue Care`, `Gallagher Bassett Workers Compensation VIC`, `Point Insurance`, `Workers Compensation & Risk Specialists (WCRS) services render for Philips electronics P/L`, `Country Wide Insurance Brokers (CWIB)`, `Allianz Australia Insurance Ltd C/ - Moray and Agnew Lawyers`, `CHUBB AUSTRALASIA`, `Sirius Support & Industrious People`, `BORG MANUFACTURING P/L`, `Department of Climate Change, Energy, the Environment and Water`, `Hireup Pty. Ltd.`, `Workcover QLD`, `Greenham Tasmania `, `Fantastic Furniture Ltd`, `CGU Workers Compensation VIC`, `Lawson Risk Management Services Pty Ltd`, `SGP Civil`, `Moray & Agnew`, `Edwards Michael Lawyers`, `Jensen McConarchy`, `Cyprium Metals`, `Hunter New England Local Health District`, `EML TMF, Insurance for NSW`, `RACQ Insurance`, `Blue Care ATF The Uniting Church in Aust. Property Trust (Q)`, `ENERGYAUSTRALIA SERVICES P/L`, `AAMI CTP`, `Bupa Asia Pacific`, `The Good Shepherd Home`, `Department of Corporate and Digital Development`, `Allianz CTP Claims NSW`, `Sedgwick Australia`, `Racing NSW`, `GCI Group`, `Australia Post`, `Coles Group Limited`, `Minter Ellison`, `MCCOLL'S OPERATIONS P/L`, `Apprenticeship Support Australia`, `AIA Australia Limited`, `Ernst & Young Services Pty Limited`, `North Metropolitan Health Service`, `St Vincent de Paul Society Canberra/Goulburn (Inc)`, `DP WORLD AUSTRALIA FREMANTLE TERMINAL`, `Moray and Agnew`, `Mosaic Group`, `Ovato`, `ACT Formwork Pty Ltd`, `DORMAKABA AUSTRALIA PTY LTD`, `Jones Harley Toole`, `QBE Accident and Health`, `Crawford Legal`, `REA Group Ltd`, `Amadeus IT Pacific Pty Ltd`, `DXC Integrated Services Victoria Pty Ltd`, `Vellex Pty Ltd`, `3M Australia`, `RTC Consulting`, `Somerset College Ltd`, `Bupa Care Services`, `IKEA North Lakes`, `Australian Criminal Intelligence Commission`, `McInnes Wilson Lawyers`, `UnitingCare Queensland `, `Anglican Community Care Incorporated (trading as ac.care)`, `Electrolux Home Products Pty Ltd`, `Gen Leads`, `FUSE RECRUITMENT MELBOURNE P/L`, `Zurich Financial Services Australia Limited`, `Wesfarmers Group TeamCover`, `Connect Infrastructure`, `Oji Fibre Solutions (Aus) Pty Ltd`, `Quality Bakers Australia Pty Limited`, `Workers Compensation & Risk Specialists`, `Civil Aviation Safety Authority (CASA)`, `Endeavour Foundation`, `The Territory Boundless Possible`, `Territory Families – Housing & Communities`, `Ampol Australia Petroleum Pty Ltd`, `Seven Network (Operations) Ltd`, `HopgoodGanim Lawyers`, `Coal Mines Insurance`, `QBE Insurance Australia`, `UGL Limited`, `QBE Accident and Health `, `C.INC`, `Ikea Logan`, `VERO`, `Geodis Australia`, `McCabes Lawyers`, `Programmed`, `UNSW Canberra`, `EML, Agent for ReturnToWorkSA`, `TEST ORG 2. EML Workers Insurance NSW`, `Kings Group`, `Maney Transport`, `South Western Sydney Lhd`, `Force Fire and Safety Pty Ltd`, `Astridge & Murray Solicitors `, `Rankin Ellison Lawyers`, `EML Insurance`, `ACCC/AER`, `Facilities First`, `Turks Legal`, `Jenson McConaghy Lawyers`, `CGU Insurance`, `AAI Limited trading as GIO`, `BP Australia Limited C/ Collin Biggers & Paisley Lawyers`, `O’Neill & Brown Electrical Services Pty Ltd`, `St Kilda PCYC`, `Justice Services Pty Ltd`, `American Express International Inc`, `Gillis Delaney Lawyers`, `Cabra Dominican College Ltd.`, `Trident Services Cleaning Pty Ltd`, `Hicksons Lawyers`, `Healthscope Operations Pty Ltd`, `GSK CX Healthcare Pty Ltd`, `ACT Government`, `AJ Bush & Sons Pty Ltd`, `OMB Solicitors`, `EML Self Insurance`, `Cooper Grace Ward`, `GC Legal`, `Centacare Catholic Family Services`, `Etex Australia Pty Ltd`, `Allianz Australia Ltd`, `Envirolab Service`, `Ikea `, `Allianz Australia Insurance Limited`, `WorkCover Queensland`, `Allianz Workers Compensation ACT`, `GIO Workers Compensation NSW`, `GenesisCare`, `Rocklea Pressed Metal Pty Ltd `, `Australian Digital Health Agency`, `HWL Ebsworth`, `Museum and Art Gallery Northern Territory (MAGNT)`, `CSR`, `Connell`, `4cRisk`, `HBA Legal`, `Coles Supermarkets Australia Pty Ltd`, `The University of Queensland`, `VENTIA SERVICES GROUP P/L,VENT`, `Point Underwriting Agency Pty Ltd`, `Youi CTP SA`, `Allianz Workers Compensation NSW`, `Detmold Packaging Pty Ltd`, `KENNARDS HIRE PTY LTD`, `QBE CTP QLD`, `Insurance House Group`, `Kilcoy Pastoral Company Limited`, `SRG Global Mining (Australia) Pty Ltd`, `Hunter Imaging Group`, `Park Hyatt Melbourne`, `Enviro Lab`, `QBE Australia Insurance Limited`, `EML c/o Moray`, `Catholic Church Insurance Limited`, `NV EMPLOYMENT PTY LTD`, `IP Australia`, `Qantas`, `Wesfarmer Limited`, `Melton City Council`, `Workcover Employer For Special Policies`, `Allianz Australia Workers Compensation (NSW) Ltd.`, `Uniting Care Health`, `Staff Australia Payroll Services Pty Ltd`, `WN Group`, `Infrabuild`, `Western NSW Local Health District`, `APS Group`, `DXC Claims Management Services - VIC`, `GIO`, `Northern Adelaide Local Health Network `, `Austbrokers Canberra`, `Department of Treasury and Finance Northern Territory Government`, `PSC Workers Compensation & Consulting`, `Alinta Energy`, `Sunline ACT Pty Ltd`, `Allianz Australia Workers' Compensation (Victoria)`, `Suncorp`, `JW Land Construction`, `Comcare - VIC`, `IKEA Pty Limited`, `KENNARDS HIRE`, `IRI Worldwide`, `RFI Technology Solutions`, `Engage TSS Internal Resources`, `St Vincent’s Care Services Mitchelton`, `Cappello Concreting Services Pty Ltd`, `Correct Care Australasia P/L`, `Coal Services`, `VELLA TRANSPORT ADMINISTRATION PTY LTD`, `CGU Workers Compensation WA`, `CORPORATE SERVICE NETWORK`, `BGIS`, `SCENTRE LIMITED`, `Employers Mutual Limited`, `RAPE & DOMESTIC VIOLENCE SERVICES AUSTRALIA`, `PSC Insurance`, `Allianz Australia Insurance Ltd ACT`, `Big W`, `Coverforce Pty Ltd`, `AAMI SA CTP Claims`, `EML Workers Insurance`, `Emjay Insurance Brokers`, `EML Victoria`, `WorkSafe Claims and Recovery Support team`, `Adcor`, `Territory Families, Housing and Communities (TFHC)`, `Nazareth Catholic Community`, `Gallagher Bassett Workers Compensation SA`, `INVOCARE AUSTRALIA P/L`, `Hardman Risk Management`, `The Sydney Childrens Hospital Network`, `The Junction Works Limited`, `PEM DEMO`, `Queensland Ambulance Service`, `Fel Child Care Centres 1 Pty Ltd`, `Allianz CTP QLD`, `Moray & Agnew Lawyers`, `Programmed Maintenance Services Ltd (Self Insured)`, `iag`, `Barnardos`, `eReports `, `Youi Pty Ltd`, `HM Focus Pty Ltd`, `Allianz Workers Compensation VIC`, `iCare Workers Insurance`, `Procare Group`, `Kemp & Co Lawyers`, `AAMI Insurance`, `Combined Insurance`, `STAWELL GOLD MINES P/L`, `QBE CTP NSW`, `SA Health`, `Gilshenan & Luton Legal Practice`, `Genesis Care`, `SOUTH AUSTRALIA POLICE`, `Wollongong City Council`, `TUTT BRYANT GROUP LTD`, `Endeavour Energy`, `Tasmanian Health Service`, `IC Formwork Services Pty Ltd`, `Humdrum`, `Comcare`, `The Gowrie (Qld) Inc`, `Australian Government Department of Education, Skills and Employment`, `Gair Legal`, `Dept of Territory Families, Housing and Communities`, `McArthur River Mining PTY Ltd`, `Kincare Management Pty Ltd`, `CFA`, `Department of Territory Families, Housing and Communities Division Library & Archives NT`, `Department for Education and Child Development`, `Core Building Group Pty Ltd`, `ACH Group`, `Busy Bees Australia Operations Pty Ltd.`, `Wesfarmers Ltd`, `JBC Corporate`, `NULL`, `No Employer - ADL`, `BT Lawyers`, `InfraBuild Steel Centre`, `Kimberly-Clark`, `Tas TAFE`, `EML National Self Insurance`, `National Disability Insurance Agency`, `Colin Biggers & Paisley Pty`, `DP World Brisbane Pty Ltd`, `Australian Trade and Investment Commission (Austrade)`, `Allianz Australia Limited c/- McInnes Wilson Lawyers`, `Community Solutions`, `RFI`, `RACQ Insurance Limited ABN 50 009 704 152`, `AAI Limited trading as GIO`, `Gallagher Bassett Services Workers Compensation Vic Pty Ltd`, `Department of Infrastructure, Transport and Regional Development`, `PSC Insurance Group`, `Allianz CTP NSW`, `CSR Limited`, `Kimberly-Clark Australia P/L`, `Hall and Willcox Lawyers`, `Page Seager Lawyers`, `Iconic Hotels Management`, `St John Medical Centre`, `Department of Veterans Affairs`, `Allianz QLD CTP`, `Morgan & Agnew Lawyers`, `Bureau of Meteorology`, `Forest Coach Lines Pty / Ltd`, `Shaw's Darwin Transport Pty Ltd`, `Dynamic Diesel Mechanical Services Pty Ltd`, `Hall & Wilcox Lawyers`, `Moran Aged Care`, `DJarvis@shepelectrical.com.au`, `Gallagher Bassett Self Insurance NSW`, `EML as agent for icare Workers Insurance NSW`, `Minter Ellison Lawyers`, `Lee Legal Group`, `Child and Adolescent Health Service (CAHS)`, `Holman Webb Lawyers`, `Dept of Home Affairs`, `QSuper`, `TIO Motor Accidents Compensation `, `Allianz Australia Workers' Compensation (Victoria) Limited`, `Perpetual Limited`, `Barwang Pty Ltd`, `CTP QLD Claims Division`, `InvoCare`, `Australian Border Force`, `I MED Radiology Network`, `Ensure Pty Ltd`, `CITY OF PALMERSTON`, `AKUBRA HATS PTY LTD`, `Secom Australia`, `GIO Workers Compensation NT`, `Pialligo Estate`, `Berry Buddle Wilkins`, `Department of Infrastructure, Transport, Regional Development and Communications`, `Aussie Skip Bins Services P/L`, `BGIS Pty Ltd`, `NSW Police Force`, `GIO Workers Compensation TAS`, `Eighteen33 Pty Ltd`, `Crown Law`, `Paramatta Council`, `Northern Territory Government`, `Australian Electoral Commission`, `Department of Health`, `Hunt & Hunt Lawyers`, `Batemans Bay Soldiers Club`, `Allianz Workers Compensation Tasmania`, `SMK Lawyers`, `Envirolab Group`, `WorkSafe Victoria`, `Allianz Australia Insurance Limited, c/- Moray & Agnew`, `Allianz Australia Insurance Limited ABN 15 000 122 850, c/- Moray & Agnew`, `City of Parramatta`, `UES International Pty Ltd`, `Westpac Group`, `Logistics & Stores (Mailroom, Stores & Transport) Services CHW`, `Device Technologies Australia Pty Ltd`, `Willis Towers Watson`, `Hsswa Pty Ltd & HSS Resources Pty Ltd & Other`, `Kingspan Water & Energy Pty Limited`, `SAPOL`, `Guild Insurance`, `Westpac Banking Group`, `St Hilarion Aged Care`, `AAI Limited trading as GIO - Agent for the Workers Compensation Nominal Insurer ABN 83 564 379 108`, `Roshana Pty Ltd`, `QBE Insurance (Australia) Limited (ABN 78003191035)`, `Service Australia`, `BOC Limited `, `HWLE Lawyers`, `NRMA CTP NSW`, `RACQ Insurance Limited ABN 50009704152/ C- Cooper Grace Ward`, `CALVARY ADMINISTRATION PTY LTD`, `Cappello Group`, `Wesfarmers Limited`, `GIO NSW CTP `, `FK Gardner Services (Qld) Pty Ltd`, `Challenge Implements Holdings`, `Bartier Perry Pty Limited`, `Chubb Insurance Australia Limited`, `EMP Michael Lawyers`, `I-MED RADIOLOGY NETWORK LIMITED`, `Gilchrist Connell Legal`, `Premier Office Relocations`, `Nominal Defendant c/- Jensen McConaghy Lawyers`, `Detmold Mental Health Training`, `EML`, `Premise`, `Balance Rehab`, `Xchanging Workers Compensation - NSW`, `Coogee Chemicals Pty Ltd`, `Safe Work Australia`, `Jensen McConaghy Lawyers`, `Hawkesbury City Council`, `Toll Global Express`, `The Corporation of the Synod of the Diocese of Brisbane`, `NRMA CTP SA`, `Ambulance Victoria`, `APSystems`, `Austbrokers (Finsura)`, `SCENTRE GROUP`, `Ikea Australia`, `Department of Treasury and Finance`, `Gallagher Bassett Services Workers Compensation NSW`, `NONI B HOLDINGS PTY LIMITED`, `QBE Workers Compensation SA`, `The Star Entertainment Group Self Insurance Unit`, `Catholic Care Diocese of Bathurst`, `GAIR LEGAL PTY LIMITED`, `QBE CTP SA`, `Wesfarmers Group`, `Rod Pilon Transport`, `TG Legal`, `Department of the Prime Minister and Cabinet`, `UNSW`, `RACQ Group`, `REMONDIS Australia Pty Ltd`, `Australian Federal Police`, `Marshall & Brougham Constructions `, `Chandler Macleod Group`, `University of Tasmania`, `Goodman Fielder Pty Limited`, `SONIC HEALTHCARE GROUP`, `Hastings Medical Centre`, `Hospitality Employers Mutual`, `HCF`, `Colin Biggers Paisley Lawyers`, `Department Veterans Affairs`, `Maddocks Lawyers`, `SRG Group`, `Australian Personnel Solutions (APS Group)`, `EY Business Solutions Pty Ltd`, `National Indigenous Australians Agency`, `St Catherine's School, Berwick`, `Transport for NSW`, `South Australian Native Titles Services` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `CATS_SCORE` \| 32.28 \| \| `CATS_MICRO_P` \| 71.89 \| \| `CATS_MICRO_R` \| 23.49 \| \| `CATS_MICRO_F` \| 35.41 \| \| `CATS_MACRO_P` \| 7.06 \| \| `CATS_MACRO_R` \| 3.40 \| \| `CATS_MACRO_F` \| 4.32 \| \| `CATS_MACRO_AUC` \| 32.28 \| \| `TEXTCAT_MULTILABEL_LOSS` \| 7.88 \|
dkqjrm/20230825071702	dkqjrm	2023-08-25T00:32:53Z	19	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-24T22:17:21Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230825071702' 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. --> # 20230825071702 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2804 - Accuracy: 0.7617 ## 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: 0.005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 80.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 156 \| 0.6793 \| 0.5307 \| \| No log \| 2.0 \| 312 \| 0.9039 \| 0.4765 \| \| No log \| 3.0 \| 468 \| 0.7107 \| 0.4729 \| \| 0.8982 \| 4.0 \| 624 \| 0.6969 \| 0.5199 \| \| 0.8982 \| 5.0 \| 780 \| 0.5729 \| 0.5560 \| \| 0.8982 \| 6.0 \| 936 \| 0.6447 \| 0.5596 \| \| 0.8495 \| 7.0 \| 1092 \| 0.6093 \| 0.5921 \| \| 0.8495 \| 8.0 \| 1248 \| 0.4289 \| 0.6679 \| \| 0.8495 \| 9.0 \| 1404 \| 0.4954 \| 0.6282 \| \| 0.751 \| 10.0 \| 1560 \| 0.3952 \| 0.6715 \| \| 0.751 \| 11.0 \| 1716 \| 0.6147 \| 0.6462 \| \| 0.751 \| 12.0 \| 1872 \| 0.4183 \| 0.7004 \| \| 0.6407 \| 13.0 \| 2028 \| 0.3743 \| 0.6968 \| \| 0.6407 \| 14.0 \| 2184 \| 0.3907 \| 0.7292 \| \| 0.6407 \| 15.0 \| 2340 \| 0.3409 \| 0.7148 \| \| 0.6407 \| 16.0 \| 2496 \| 0.5288 \| 0.6426 \| \| 0.6476 \| 17.0 \| 2652 \| 0.4492 \| 0.7220 \| \| 0.6476 \| 18.0 \| 2808 \| 0.3312 \| 0.7220 \| \| 0.6476 \| 19.0 \| 2964 \| 0.4062 \| 0.6606 \| \| 0.6425 \| 20.0 \| 3120 \| 0.3715 \| 0.6859 \| \| 0.6425 \| 21.0 \| 3276 \| 0.3305 \| 0.7256 \| \| 0.6425 \| 22.0 \| 3432 \| 0.6557 \| 0.6245 \| \| 0.5658 \| 23.0 \| 3588 \| 0.3943 \| 0.6859 \| \| 0.5658 \| 24.0 \| 3744 \| 0.3394 \| 0.7040 \| \| 0.5658 \| 25.0 \| 3900 \| 0.4640 \| 0.6823 \| \| 0.5333 \| 26.0 \| 4056 \| 0.3419 \| 0.7220 \| \| 0.5333 \| 27.0 \| 4212 \| 0.3646 \| 0.7112 \| \| 0.5333 \| 28.0 \| 4368 \| 0.3626 \| 0.7184 \| \| 0.5164 \| 29.0 \| 4524 \| 0.3215 \| 0.7473 \| \| 0.5164 \| 30.0 \| 4680 \| 0.2941 \| 0.7581 \| \| 0.5164 \| 31.0 \| 4836 \| 0.4957 \| 0.6173 \| \| 0.5164 \| 32.0 \| 4992 \| 0.3362 \| 0.7329 \| \| 0.4676 \| 33.0 \| 5148 \| 0.3116 \| 0.7437 \| \| 0.4676 \| 34.0 \| 5304 \| 0.3344 \| 0.7401 \| \| 0.4676 \| 35.0 \| 5460 \| 0.4769 \| 0.7220 \| \| 0.4443 \| 36.0 \| 5616 \| 0.2822 \| 0.7509 \| \| 0.4443 \| 37.0 \| 5772 \| 0.3748 \| 0.6859 \| \| 0.4443 \| 38.0 \| 5928 \| 0.2989 \| 0.7509 \| \| 0.4179 \| 39.0 \| 6084 \| 0.3193 \| 0.7292 \| \| 0.4179 \| 40.0 \| 6240 \| 0.3725 \| 0.6715 \| \| 0.4179 \| 41.0 \| 6396 \| 0.3336 \| 0.7509 \| \| 0.3974 \| 42.0 \| 6552 \| 0.2967 \| 0.7365 \| \| 0.3974 \| 43.0 \| 6708 \| 0.2908 \| 0.7545 \| \| 0.3974 \| 44.0 \| 6864 \| 0.2887 \| 0.7473 \| \| 0.3774 \| 45.0 \| 7020 \| 0.3012 \| 0.7401 \| \| 0.3774 \| 46.0 \| 7176 \| 0.3437 \| 0.7509 \| \| 0.3774 \| 47.0 \| 7332 \| 0.3390 \| 0.7292 \| \| 0.3774 \| 48.0 \| 7488 \| 0.2952 \| 0.7473 \| \| 0.3419 \| 49.0 \| 7644 \| 0.3116 \| 0.7401 \| \| 0.3419 \| 50.0 \| 7800 \| 0.2856 \| 0.7473 \| \| 0.3419 \| 51.0 \| 7956 \| 0.3227 \| 0.7256 \| \| 0.3275 \| 52.0 \| 8112 \| 0.2861 \| 0.7509 \| \| 0.3275 \| 53.0 \| 8268 \| 0.3534 \| 0.7401 \| \| 0.3275 \| 54.0 \| 8424 \| 0.3395 \| 0.7256 \| \| 0.3225 \| 55.0 \| 8580 \| 0.3113 \| 0.7401 \| \| 0.3225 \| 56.0 \| 8736 \| 0.2932 \| 0.7473 \| \| 0.3225 \| 57.0 \| 8892 \| 0.4312 \| 0.7112 \| \| 0.3104 \| 58.0 \| 9048 \| 0.3085 \| 0.7509 \| \| 0.3104 \| 59.0 \| 9204 \| 0.3164 \| 0.7545 \| \| 0.3104 \| 60.0 \| 9360 \| 0.2758 \| 0.7473 \| \| 0.3164 \| 61.0 \| 9516 \| 0.3183 \| 0.7220 \| \| 0.3164 \| 62.0 \| 9672 \| 0.3571 \| 0.7220 \| \| 0.3164 \| 63.0 \| 9828 \| 0.3156 \| 0.7365 \| \| 0.3164 \| 64.0 \| 9984 \| 0.2756 \| 0.7653 \| \| 0.2939 \| 65.0 \| 10140 \| 0.2859 \| 0.7437 \| \| 0.2939 \| 66.0 \| 10296 \| 0.2934 \| 0.7545 \| \| 0.2939 \| 67.0 \| 10452 \| 0.2977 \| 0.7690 \| \| 0.2826 \| 68.0 \| 10608 \| 0.2871 \| 0.7653 \| \| 0.2826 \| 69.0 \| 10764 \| 0.2903 \| 0.7653 \| \| 0.2826 \| 70.0 \| 10920 \| 0.2974 \| 0.7581 \| \| 0.2663 \| 71.0 \| 11076 \| 0.2778 \| 0.7509 \| \| 0.2663 \| 72.0 \| 11232 \| 0.2849 \| 0.7365 \| \| 0.2663 \| 73.0 \| 11388 \| 0.2970 \| 0.7653 \| \| 0.2637 \| 74.0 \| 11544 \| 0.3025 \| 0.7545 \| \| 0.2637 \| 75.0 \| 11700 \| 0.2793 \| 0.7617 \| \| 0.2637 \| 76.0 \| 11856 \| 0.2778 \| 0.7545 \| \| 0.2699 \| 77.0 \| 12012 \| 0.2861 \| 0.7617 \| \| 0.2699 \| 78.0 \| 12168 \| 0.2857 \| 0.7690 \| \| 0.2699 \| 79.0 \| 12324 \| 0.2774 \| 0.7617 \| \| 0.2699 \| 80.0 \| 12480 \| 0.2804 \| 0.7617 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
dkqjrm/20230825070638	dkqjrm	2023-08-25T00:19:17Z	15	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:super_glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-24T22:06:57Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230825070638' 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. --> # 20230825070638 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3456 - Accuracy: 0.7329 ## 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: 0.005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 80.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 156 \| 0.7894 \| 0.5271 \| \| No log \| 2.0 \| 312 \| 0.6658 \| 0.5379 \| \| No log \| 3.0 \| 468 \| 0.6408 \| 0.5054 \| \| 0.886 \| 4.0 \| 624 \| 0.7134 \| 0.4729 \| \| 0.886 \| 5.0 \| 780 \| 0.6234 \| 0.5560 \| \| 0.886 \| 6.0 \| 936 \| 0.4782 \| 0.6318 \| \| 0.7765 \| 7.0 \| 1092 \| 1.1394 \| 0.5776 \| \| 0.7765 \| 8.0 \| 1248 \| 0.5214 \| 0.6534 \| \| 0.7765 \| 9.0 \| 1404 \| 0.4206 \| 0.6570 \| \| 0.7206 \| 10.0 \| 1560 \| 0.5019 \| 0.6643 \| \| 0.7206 \| 11.0 \| 1716 \| 0.7680 \| 0.5343 \| \| 0.7206 \| 12.0 \| 1872 \| 0.3433 \| 0.7220 \| \| 0.6543 \| 13.0 \| 2028 \| 0.3834 \| 0.7292 \| \| 0.6543 \| 14.0 \| 2184 \| 0.4588 \| 0.6751 \| \| 0.6543 \| 15.0 \| 2340 \| 0.3413 \| 0.7040 \| \| 0.6543 \| 16.0 \| 2496 \| 0.4874 \| 0.6426 \| \| 0.5973 \| 17.0 \| 2652 \| 0.3283 \| 0.7256 \| \| 0.5973 \| 18.0 \| 2808 \| 0.3605 \| 0.7329 \| \| 0.5973 \| 19.0 \| 2964 \| 0.3314 \| 0.7256 \| \| 0.5433 \| 20.0 \| 3120 \| 0.5998 \| 0.6606 \| \| 0.5433 \| 21.0 \| 3276 \| 0.3489 \| 0.6931 \| \| 0.5433 \| 22.0 \| 3432 \| 0.4316 \| 0.6715 \| \| 0.5373 \| 23.0 \| 3588 \| 0.3328 \| 0.7076 \| \| 0.5373 \| 24.0 \| 3744 \| 0.3379 \| 0.7220 \| \| 0.5373 \| 25.0 \| 3900 \| 0.3580 \| 0.7148 \| \| 0.4923 \| 26.0 \| 4056 \| 0.3141 \| 0.7329 \| \| 0.4923 \| 27.0 \| 4212 \| 0.4341 \| 0.7365 \| \| 0.4923 \| 28.0 \| 4368 \| 0.3386 \| 0.7220 \| \| 0.4513 \| 29.0 \| 4524 \| 0.3038 \| 0.7220 \| \| 0.4513 \| 30.0 \| 4680 \| 0.3775 \| 0.7220 \| \| 0.4513 \| 31.0 \| 4836 \| 0.4197 \| 0.7076 \| \| 0.4513 \| 32.0 \| 4992 \| 0.4666 \| 0.7220 \| \| 0.4041 \| 33.0 \| 5148 \| 0.3355 \| 0.7365 \| \| 0.4041 \| 34.0 \| 5304 \| 0.3147 \| 0.7329 \| \| 0.4041 \| 35.0 \| 5460 \| 0.3810 \| 0.7184 \| \| 0.3705 \| 36.0 \| 5616 \| 0.3184 \| 0.7256 \| \| 0.3705 \| 37.0 \| 5772 \| 0.3668 \| 0.7076 \| \| 0.3705 \| 38.0 \| 5928 \| 0.3859 \| 0.7220 \| \| 0.3556 \| 39.0 \| 6084 \| 0.3010 \| 0.7329 \| \| 0.3556 \| 40.0 \| 6240 \| 0.3201 \| 0.7220 \| \| 0.3556 \| 41.0 \| 6396 \| 0.3304 \| 0.7329 \| \| 0.3089 \| 42.0 \| 6552 \| 0.3634 \| 0.7365 \| \| 0.3089 \| 43.0 \| 6708 \| 0.3844 \| 0.7184 \| \| 0.3089 \| 44.0 \| 6864 \| 0.3320 \| 0.7220 \| \| 0.3015 \| 45.0 \| 7020 \| 0.3696 \| 0.7220 \| \| 0.3015 \| 46.0 \| 7176 \| 0.3665 \| 0.7220 \| \| 0.3015 \| 47.0 \| 7332 \| 0.3355 \| 0.7256 \| \| 0.3015 \| 48.0 \| 7488 \| 0.3568 \| 0.7292 \| \| 0.2709 \| 49.0 \| 7644 \| 0.3450 \| 0.7329 \| \| 0.2709 \| 50.0 \| 7800 \| 0.3790 \| 0.7148 \| \| 0.2709 \| 51.0 \| 7956 \| 0.3516 \| 0.7112 \| \| 0.2681 \| 52.0 \| 8112 \| 0.3741 \| 0.7329 \| \| 0.2681 \| 53.0 \| 8268 \| 0.3615 \| 0.7220 \| \| 0.2681 \| 54.0 \| 8424 \| 0.3479 \| 0.7292 \| \| 0.2477 \| 55.0 \| 8580 \| 0.3401 \| 0.7184 \| \| 0.2477 \| 56.0 \| 8736 \| 0.3766 \| 0.7329 \| \| 0.2477 \| 57.0 \| 8892 \| 0.3562 \| 0.7148 \| \| 0.2344 \| 58.0 \| 9048 \| 0.3412 \| 0.7220 \| \| 0.2344 \| 59.0 \| 9204 \| 0.3782 \| 0.7437 \| \| 0.2344 \| 60.0 \| 9360 \| 0.3723 \| 0.7040 \| \| 0.2126 \| 61.0 \| 9516 \| 0.3852 \| 0.7292 \| \| 0.2126 \| 62.0 \| 9672 \| 0.3901 \| 0.7256 \| \| 0.2126 \| 63.0 \| 9828 \| 0.3698 \| 0.7112 \| \| 0.2126 \| 64.0 \| 9984 \| 0.3249 \| 0.7220 \| \| 0.2127 \| 65.0 \| 10140 \| 0.3979 \| 0.7004 \| \| 0.2127 \| 66.0 \| 10296 \| 0.3705 \| 0.7365 \| \| 0.2127 \| 67.0 \| 10452 \| 0.3317 \| 0.7220 \| \| 0.199 \| 68.0 \| 10608 \| 0.3322 \| 0.7329 \| \| 0.199 \| 69.0 \| 10764 \| 0.3706 \| 0.7220 \| \| 0.199 \| 70.0 \| 10920 \| 0.3628 \| 0.7148 \| \| 0.1959 \| 71.0 \| 11076 \| 0.3600 \| 0.7437 \| \| 0.1959 \| 72.0 \| 11232 \| 0.3349 \| 0.7437 \| \| 0.1959 \| 73.0 \| 11388 \| 0.3650 \| 0.7184 \| \| 0.184 \| 74.0 \| 11544 \| 0.3337 \| 0.7365 \| \| 0.184 \| 75.0 \| 11700 \| 0.3309 \| 0.7329 \| \| 0.184 \| 76.0 \| 11856 \| 0.3237 \| 0.7365 \| \| 0.183 \| 77.0 \| 12012 \| 0.3430 \| 0.7256 \| \| 0.183 \| 78.0 \| 12168 \| 0.3567 \| 0.7329 \| \| 0.183 \| 79.0 \| 12324 \| 0.3541 \| 0.7329 \| \| 0.183 \| 80.0 \| 12480 \| 0.3456 \| 0.7329 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
IALABS/Arturosfastfood	IALABS	2023-08-25T00:14:58Z	0	1	null	[ "conversational", "es", "license:other", "region:us" ]	text-generation	2023-08-24T23:32:33Z	--- license: other language: - es pipeline_tag: conversational --- size_categories: n<1K tags: - rlfh - argilla - human-feedback
stevhliu/my_awesome_model	stevhliu	2023-08-25T00:04:52Z	16,177	3	transformers	[ "transformers", "pytorch", "tf", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-28T18:41:57Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: stevhliu/my_awesome_model 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. --> # stevhliu/my_awesome_model 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: 0.0632 - Validation Loss: 0.2355 - Train Accuracy: 0.9295 - Epoch: 2 ## 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': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.2518 \| 0.1859 \| 0.9261 \| 0 \| \| 0.1319 \| 0.1822 \| 0.9318 \| 1 \| \| 0.0632 \| 0.2355 \| 0.9295 \| 2 \| ### Framework versions - Transformers 4.22.2 - TensorFlow 2.8.2 - Datasets 2.5.1 - Tokenizers 0.12.1
PivotOrDie/vit-base-patch16-224-finetuned-flower	PivotOrDie	2023-08-25T00:02:48Z	165	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-08-24T23:46:42Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: vit-base-patch16-224-finetuned-flower 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. --> # vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder 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: 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 ### Framework versions - Transformers 4.24.0 - Pytorch 2.0.1+cu118 - Datasets 2.7.1 - Tokenizers 0.13.3
abdiharyadi/IndoT5-base-amr-to-text-linearized-penman-ilmy-epochs-10	abdiharyadi	2023-08-24T23:50:07Z	291	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:Wikidepia/IndoT5-base", "base_model:finetune:Wikidepia/IndoT5-base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-08-24T23:19:50Z	--- base_model: Wikidepia/IndoT5-base tags: - generated_from_trainer model-index: - name: IndoT5-base-amr-to-text-linearized-penman-ilmy-epochs-10 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. --> # IndoT5-base-amr-to-text-linearized-penman-ilmy-epochs-10 This model is a fine-tuned version of [Wikidepia/IndoT5-base](https://huggingface.co/Wikidepia/IndoT5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1789 ## 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 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 331 \| 0.6415 \| \| 0.4503 \| 2.0 \| 662 \| 0.8102 \| \| 0.4503 \| 3.0 \| 993 \| 0.8944 \| \| 0.0619 \| 4.0 \| 1324 \| 0.9228 \| \| 0.0262 \| 5.0 \| 1655 \| 1.0949 \| \| 0.0262 \| 6.0 \| 1986 \| 1.1223 \| \| 0.0158 \| 7.0 \| 2317 \| 1.1668 \| \| 0.0103 \| 8.0 \| 2648 \| 1.1655 \| \| 0.0103 \| 9.0 \| 2979 \| 1.1861 \| \| 0.008 \| 10.0 \| 3310 \| 1.1789 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
slmnpl/stable-diffusion-webui-master	slmnpl	2023-08-24T23:28:46Z	0	0	null	[ "arxiv:2211.06679", "region:us" ]	null	2023-08-24T23:18:23Z	# Stable Diffusion web UI A browser interface based on Gradio library for Stable Diffusion. ![](screenshot.png) ## Features [Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features): - Original txt2img and img2img modes - One click install and run script (but you still must install python and git) - Outpainting - Inpainting - Color Sketch - Prompt Matrix - Stable Diffusion Upscale - Attention, specify parts of text that the model should pay more attention to - a man in a `((tuxedo))` - will pay more attention to tuxedo - a man in a `(tuxedo:1.21)` - alternative syntax - select text and press `Ctrl+Up` or `Ctrl+Down` (or `Command+Up` or `Command+Down` if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user) - Loopback, run img2img processing multiple times - X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters - Textual Inversion - have as many embeddings as you want and use any names you like for them - use multiple embeddings with different numbers of vectors per token - works with half precision floating point numbers - train embeddings on 8GB (also reports of 6GB working) - Extras tab with: - GFPGAN, neural network that fixes faces - CodeFormer, face restoration tool as an alternative to GFPGAN - RealESRGAN, neural network upscaler - ESRGAN, neural network upscaler with a lot of third party models - SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers - LDSR, Latent diffusion super resolution upscaling - Resizing aspect ratio options - Sampling method selection - Adjust sampler eta values (noise multiplier) - More advanced noise setting options - Interrupt processing at any time - 4GB video card support (also reports of 2GB working) - Correct seeds for batches - Live prompt token length validation - Generation parameters - parameters you used to generate images are saved with that image - in PNG chunks for PNG, in EXIF for JPEG - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI - can be disabled in settings - drag and drop an image/text-parameters to promptbox - Read Generation Parameters Button, loads parameters in promptbox to UI - Settings page - Running arbitrary python code from UI (must run with `--allow-code` to enable) - Mouseover hints for most UI elements - Possible to change defaults/mix/max/step values for UI elements via text config - Tiling support, a checkbox to create images that can be tiled like textures - Progress bar and live image generation preview - Can use a separate neural network to produce previews with almost none VRAM or compute requirement - Negative prompt, an extra text field that allows you to list what you don't want to see in generated image - Styles, a way to save part of prompt and easily apply them via dropdown later - Variations, a way to generate same image but with tiny differences - Seed resizing, a way to generate same image but at slightly different resolution - CLIP interrogator, a button that tries to guess prompt from an image - Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway - Batch Processing, process a group of files using img2img - Img2img Alternative, reverse Euler method of cross attention control - Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions - Reloading checkpoints on the fly - Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one - [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community - [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once - separate prompts using uppercase `AND` - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2` - No token limit for prompts (original stable diffusion lets you use up to 75 tokens) - DeepDanbooru integration, creates danbooru style tags for anime prompts - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args) - via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI - Generate forever option - Training tab - hypernetworks and embeddings options - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime) - Clip skip - Hypernetworks - Loras (same as Hypernetworks but more pretty) - A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt - Can select to load a different VAE from settings screen - Estimated completion time in progress bar - API - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML - via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)) - [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions - [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions - Now without any bad letters! - Load checkpoints in safetensors format - Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64 - Now with a license! - Reorder elements in the UI from settings screen ## Installation and Running Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. Alternatively, use online services (like Google Colab): - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services) ### Installation on Windows 10/11 with NVidia-GPUs using release package 1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract it's contents. 2. Run `update.bat`. 3. Run `run.bat`. > For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) ### Automatic Installation on Windows 1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH". 2. Install [git](https://git-scm.com/download/win). 3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`. 4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user. ### Automatic Installation on Linux 1. Install the dependencies: ```bash # Debian-based: sudo apt install wget git python3 python3-venv # Red Hat-based: sudo dnf install wget git python3 # Arch-based: sudo pacman -S wget git python3 ``` 2. Navigate to the directory you would like the webui to be installed and execute the following command: ```bash bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh) ``` 3. Run `webui.sh`. 4. Check `webui-user.sh` for options. ### Installation on Apple Silicon Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon). ## Contributing Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) ## Documentation The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki). For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) [crawlable wiki](https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/wiki). ## Credits Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file. - Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers - k-diffusion - https://github.com/crowsonkb/k-diffusion.git - GFPGAN - https://github.com/TencentARC/GFPGAN.git - CodeFormer - https://github.com/sczhou/CodeFormer - ESRGAN - https://github.com/xinntao/ESRGAN - SwinIR - https://github.com/JingyunLiang/SwinIR - Swin2SR - https://github.com/mv-lab/swin2sr - LDSR - https://github.com/Hafiidz/latent-diffusion - MiDaS - https://github.com/isl-org/MiDaS - Ideas for optimizations - https://github.com/basujindal/stable-diffusion - Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing. - Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion) - Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention) - Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas). - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator - Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch - xformers - https://github.com/facebookresearch/xformers - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru - Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6) - Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix - Security advice - RyotaK - UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC - TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd - LyCORIS - KohakuBlueleaf - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user. - (You)
dt-and-vanilla-ardt/ardt-vanilla-arrl_train_halfcheetah_high-2408_2205-33	dt-and-vanilla-ardt	2023-08-24T23:16:05Z	31	0	transformers	[ "transformers", "pytorch", "decision_transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2023-08-24T21:06:53Z	--- tags: - generated_from_trainer model-index: - name: ardt-vanilla-arrl_train_halfcheetah_high-2408_2205-33 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. --> # ardt-vanilla-arrl_train_halfcheetah_high-2408_2205-33 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
jlpan/starcoder-cpp2py-newsnippet1	jlpan	2023-08-24T23:02:57Z	0	0	null	[ "generated_from_trainer", "base_model:bigcode/starcoder", "base_model:finetune:bigcode/starcoder", "license:bigcode-openrail-m", "region:us" ]	null	2023-08-23T00:24:41Z	--- license: bigcode-openrail-m base_model: bigcode/starcoder tags: - generated_from_trainer model-index: - name: starcoder-cpp2py-newsnippet1 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. --> # starcoder-cpp2py-newsnippet1 This model is a fine-tuned version of [bigcode/starcoder](https://huggingface.co/bigcode/starcoder) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1964 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 15 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 4.3875 \| 0.17 \| 25 \| 0.4694 \| \| 0.2947 \| 0.33 \| 50 \| 0.2126 \| \| 0.2152 \| 0.5 \| 75 \| 0.2016 \| \| 0.2054 \| 0.67 \| 100 \| 0.1974 \| \| 0.2004 \| 0.83 \| 125 \| 0.1966 \| \| 0.1883 \| 1.05 \| 150 \| 0.1964 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
Akhilsplendid/T5-model	Akhilsplendid	2023-08-24T23:00:48Z	104	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:philschmid/flan-t5-base-samsum", "base_model:finetune:philschmid/flan-t5-base-samsum", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-08-24T22:59:06Z	--- license: apache-2.0 base_model: philschmid/flan-t5-base-samsum tags: - generated_from_trainer model-index: - name: T5-model 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-model This model is a fine-tuned version of [philschmid/flan-t5-base-samsum](https://huggingface.co/philschmid/flan-t5-base-samsum) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7013 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9849 \| 0.8 \| 10 \| 0.8062 \| \| 0.9748 \| 1.61 \| 20 \| 0.8026 \| \| 0.9772 \| 2.41 \| 30 \| 0.7968 \| \| 0.979 \| 3.22 \| 40 \| 0.7889 \| \| 0.9729 \| 4.02 \| 50 \| 0.7793 \| \| 0.9479 \| 4.82 \| 60 \| 0.7687 \| \| 0.9111 \| 5.63 \| 70 \| 0.7577 \| \| 0.8956 \| 6.43 \| 80 \| 0.7460 \| \| 0.8768 \| 7.24 \| 90 \| 0.7338 \| \| 0.8566 \| 8.04 \| 100 \| 0.7224 \| \| 0.8342 \| 8.84 \| 110 \| 0.7120 \| \| 0.8273 \| 9.65 \| 120 \| 0.7013 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
ufal/byt5-small-multilexnorm2021-trde	ufal	2023-08-24T21:40:24Z	115	1	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "lexical normalization", "tr", "de", "multilingual", "dataset:mc4", "dataset:wikipedia", "dataset:multilexnorm", "arxiv:2105.13626", "arxiv:1907.06292", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: - tr - de - multilingual datasets: - mc4 - wikipedia - multilexnorm tags: - lexical normalization license: apache-2.0 --- # Fine-tuned ByT5-small for MultiLexNorm (Turkish-German version) ![model image](https://github.com/ufal/multilexnorm2021/raw/master/img/overall.png) This is the official release of the fine-tuned models for the winning entry to the [W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm) shared task](https://noisy-text.github.io/2021/multi-lexnorm.html), which evaluates lexical-normalization systems on 12 social media datasets in 11 languages. Our system is based on [ByT5](https://arxiv.org/abs/2105.13626), which we first pre-train on synthetic data and then fine-tune on authentic normalization data. It achieves the best performance by a wide margin in intrinsic evaluation, and also the best performance in extrinsic evaluation through dependency parsing. In addition to these fine-tuned models, we also release the source files on [GitHub](https://github.com/ufal/multilexnorm2021) and an interactive demo on [Google Colab](https://colab.research.google.com/drive/1rxpI8IlKk-D2crFqi2hdzbTBIezqgsCg?usp=sharing). ## How to use The model was not fine-tuned in a standard sentence-to-sentence setting – instead, it was tailored to the token-to-token definition of MultiLexNorm data. Please refer to [the interactive demo on Colab notebook](https://colab.research.google.com/drive/1rxpI8IlKk-D2crFqi2hdzbTBIezqgsCg?usp=sharing) to learn how to use these models. ## How to cite ```bibtex @inproceedings{wnut-ufal, title= "{ÚFAL} at {MultiLexNorm} 2021: Improving Multilingual Lexical Normalization by Fine-tuning {ByT5}", author = "Samuel, David and Straka, Milan", booktitle = "Proceedings of the 7th Workshop on Noisy User-generated Text (W-NUT 2021)", year = "2021", publisher = "Association for Computational Linguistics", address = "Punta Cana, Dominican Republic" } ``` ## ByT5 - Small ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-small). ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task. ByT5 works especially well on noisy text data,e.g., `google/byt5-small` significantly outperforms [mt5-small](https://huggingface.co/google/mt5-small) on [TweetQA](https://arxiv.org/abs/1907.06292). Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) Authors: Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel
tifa-benchmark/llama2_tifa_question_generation	tifa-benchmark	2023-08-24T21:28:03Z	418	10	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "llama2", "text-to-image", "en", "dataset:TIFA", "arxiv:2303.11897", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-08-16T00:41:50Z	--- license: apache-2.0 inference: true widget: - text: "<s>[INST] <<SYS>>\nGiven an image description, generate one or two multiple-choice questions that verifies if the image description is correct.\nClassify each concept into a type (object, human, animal, food, activity, attribute, counting, color, material, spatial, location, shape, other), and then generate a question for each type.\n\n<</SYS>>\n\nDescription: a blue rabbit and a red plane [/INST] Entities:" pipeline_tag: text-generation tags: - text-generation-inference - llama2 - text-to-image datasets: - TIFA language: - en --- Project page: <https://tifa-benchmark.github.io/> This is the text parsing and question generation model for the ICCV 2023 paper [TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering](https://arxiv.org/abs/2303.11897) We introduce TIFA (Text-to-Image Faithfulness evaluation with question Answering), an automatic evaluation metric that measures the faithfulness of a generated image to its text input via visual question answering (VQA). Specifically, given a text input, we automatically generate several question-answer pairs using a language model. We calculate image faithfulness by checking whether existing VQA models can answer these questions using the generated image. Specifically, this fine-tuned LLaMA 2 model is the substitute for the GPT-3 model in the paper. It can parse an arbitrary prompt into visual entities, attributes, relations, etc. and generate question-answer tuples for each of them. See examples below. # QuickStart All codes are from <https://github.com/Yushi-Hu/tifa>. Clone this repo to easily use this model together with other modules (e.g. VQA) provided in TIFA. Please follow the prompt format, which will give the best performance. ```python import torch import transformers # prepare the LLaMA 2 model model_name = "tifa-benchmark/llama2_tifa_question_generation" pipeline = transformers.pipeline( "text-generation", model=model_name, torch_dtype=torch.float16, device_map="auto", ) # formating prompt following LLaMA 2 style def create_qg_prompt(caption): INTRO_BLURB = "Given an image description, generate one or two multiple-choice questions that verifies if the image description is correct.\nClassify each concept into a type (object, human, animal, food, activity, attribute, counting, color, material, spatial, location, shape, other), and then generate a question for each type.\n" formated_prompt = f"<s>[INST] <<SYS>>\n{INTRO_BLURB}\n<</SYS>>\n\n" formated_prompt += f"Description: {caption} [/INST] Entities:" return formated_prompt test_caption = "a blue rabbit and a red plane" # create prompt prompt = create_qg_prompt(text_caption) # text completion sequences = pipeline( prompt, do_sample=False, num_beams=5, num_return_sequences=1, max_length=512) output = sequences[0]['generated_text'][len(prompt):] output = output.split('\n\n')[0] # output print(output) #### Expected output ### # rabbit, plane # Activites: # Colors: blue, red # Counting: # Other attributes: # About rabbit (animal): # Q: is this a rabbit? # Choices: yes, no # A: yes # About rabbit (animal): # Q: what animal is in the picture? # Choices: rabbit, dog, cat, fish # A: rabbit # About plane (object): # Q: is this a plane? # Choices: yes, no # A: yes # About plane (object): # Q: what type of vehicle is this? # Choices: plane, car, motorcycle, bus # A: plane # About blue (color): # Q: is the rabbit blue? # Choices: yes, no # A: yes # About blue (color): # Q: what color is the rabbit? # Choices: blue, red, yellow, green # A: blue # About red (color): # Q: is the plane red? # Choices: yes, no # A: yes # About red (color): # Q: what color is the plane? # Choices: red, blue, yellow, green # A: red ``` # Use this LM under tifascore package tifascore provides extra functions to parse this output etc. First install tifascore according to <https://github.com/Yushi-Hu/tifa>. Then the usage is below ```python from tifascore import get_llama2_pipeline, get_llama2_question_and_answers pipeline = get_llama2_pipeline("tifa-benchmark/llama2_tifa_question_generation") print(get_llama2_question_and_answers(pipeline, "a blue rabbit and a red plane")) #### Expected output ### # [{'caption': 'a blue rabbit and a red plane', 'element': 'rabbit', 'question': 'what animal is in the picture?', 'choices': ['rabbit', 'dog', 'cat', 'fish'], 'answer': 'rabbit', 'element_type': 'animal/human'}, {'caption': 'a blue rabbit and a red plane', 'element': 'plane', 'question': 'is this a plane?', 'choices': ['yes', 'no'], 'answer': 'yes', 'element_type': 'object'}, {'caption': 'a blue rabbit and a red plane', 'element': 'plane', 'question': 'what type of vehicle is this?', 'choices': ['plane', 'car', 'motorcycle', 'bus'], 'answer': 'plane', 'element_type': 'object'}, {'caption': 'a blue rabbit and a red plane', 'element': 'blue', 'question': 'is the rabbit blue?', 'choices': ['yes', 'no'], 'answer': 'yes', 'element_type': 'color'}, {'caption': 'a blue rabbit and a red plane', 'element': 'blue', 'question': 'what color is the rabbit?', 'choices': ['blue', 'red', 'yellow', 'green'], 'answer': 'blue', 'element_type': 'color'}, {'caption': 'a blue rabbit and a red plane', 'element': 'red', 'question': 'is the plane red?', 'choices': ['yes', 'no'], 'answer': 'yes', 'element_type': 'color'}, {'caption': 'a blue rabbit and a red plane', 'element': 'red', 'question': 'what color is the plane?', 'choices': ['red', 'blue', 'yellow', 'green'], 'answer': 'red', 'element_type': 'color'}] ``` ## Bibtex ``` @article{hu2023tifa, title={Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering}, author={Hu, Yushi and Liu, Benlin and Kasai, Jungo and Wang, Yizhong and Ostendorf, Mari and Krishna, Ranjay and Smith, Noah A}, journal={arXiv preprint arXiv:2303.11897}, year={2023} } ```
magooie/Reinforce-Cartpole-v1	magooie	2023-08-24T21:09:31Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-08-24T21:09:22Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
anth0nyhak1m/SS_model	anth0nyhak1m	2023-08-24T20:52:54Z	105	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-24T20:51:15Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: SS_model 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. --> # SS_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3980 - Accuracy: 0.9587 ## 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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.153 \| 1.0 \| 4301 \| 0.1472 \| 0.9526 \| \| 0.1165 \| 2.0 \| 8602 \| 0.1376 \| 0.9562 \| \| 0.0951 \| 3.0 \| 12903 \| 0.1462 \| 0.9596 \| \| 0.0851 \| 4.0 \| 17204 \| 0.1550 \| 0.9602 \| \| 0.0709 \| 5.0 \| 21505 \| 0.1848 \| 0.9596 \| \| 0.069 \| 6.0 \| 25806 \| 0.2027 \| 0.9586 \| \| 0.0591 \| 7.0 \| 30107 \| 0.2266 \| 0.9582 \| \| 0.047 \| 8.0 \| 34408 \| 0.2110 \| 0.9573 \| \| 0.0391 \| 9.0 \| 38709 \| 0.2405 \| 0.9577 \| \| 0.0333 \| 10.0 \| 43010 \| 0.2865 \| 0.9566 \| \| 0.0336 \| 11.0 \| 47311 \| 0.2671 \| 0.9588 \| \| 0.0226 \| 12.0 \| 51612 \| 0.2743 \| 0.9567 \| \| 0.0266 \| 13.0 \| 55913 \| 0.3281 \| 0.9577 \| \| 0.0191 \| 14.0 \| 60214 \| 0.3062 \| 0.9572 \| \| 0.0232 \| 15.0 \| 64515 \| 0.3479 \| 0.9585 \| \| 0.0149 \| 16.0 \| 68816 \| 0.3542 \| 0.9587 \| \| 0.0099 \| 17.0 \| 73117 \| 0.3646 \| 0.9587 \| \| 0.0123 \| 18.0 \| 77418 \| 0.3721 \| 0.9584 \| \| 0.0091 \| 19.0 \| 81719 \| 0.3896 \| 0.9590 \| \| 0.0086 \| 20.0 \| 86020 \| 0.3980 \| 0.9587 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
kejolong/racequeen	kejolong	2023-08-24T20:44:59Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-24T20:43:34Z	--- license: creativeml-openrail-m ---
zehralx/distilbert-base-uncased-finetuned-emotion	zehralx	2023-08-24T20:44:54Z	109	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-20T13:38:45Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9235 - name: F1 type: f1 value: 0.9234578926922112 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2208 - Accuracy: 0.9235 - F1: 0.9235 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8077 \| 1.0 \| 250 \| 0.3196 \| 0.9065 \| 0.9049 \| \| 0.2488 \| 2.0 \| 500 \| 0.2208 \| 0.9235 \| 0.9235 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1 - Datasets 2.14.3 - Tokenizers 0.13.3
dimitarrskv/ppo-SnowballTarget	dimitarrskv	2023-08-24T20:40:06Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-08-24T20:40:04Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: dimitarrskv/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
camenduru/StableSR	camenduru	2023-08-24T20:36:48Z	0	3	null	[ "image-to-image", "arxiv:2305.07015", "license:other", "region:us" ]	image-to-image	2023-08-24T20:29:35Z	--- license: other pipeline_tag: image-to-image --- # StableSR Model Card This model card focuses on the models associated with the StableSR, available [here](https://github.com/IceClear/StableSR). ## Model Details - Developed by: Jianyi Wang - Model type: Diffusion-based image super-resolution model - License: [S-Lab License 1.0](https://github.com/IceClear/StableSR/blob/main/LICENSE.txt) - Model Description: This is the model used in [Paper](https://arxiv.org/abs/2305.07015). - Resources for more information: [GitHub Repository](https://github.com/IceClear/StableSR). - Cite as: @InProceedings{wang2023exploiting, author = {Wang, Jianyi and Yue, Zongsheng and Zhou, Shangchen and Chan, Kelvin CK and Loy, Chen Change}, title = {Exploiting Diffusion Prior for Real-World Image Super-Resolution}, booktitle = {arXiv preprint arXiv:2305.07015}, year = {2023}, } # Uses Please refer to [S-Lab License 1.0](https://github.com/IceClear/StableSR/blob/main/LICENSE.txt) ## Limitations and Bias ### Limitations - StableSR still requires multiple steps for generating an image, which is much slower than GAN-based approaches, especially for large images beyond 512 or 768. - StableSR sometimes cannot keep 100% fidelity due to its generative nature. - StableSR sometimes cannot generate perfect details under complex real-world scenarios. ### Bias While our model is based on a pre-trained Stable Diffusion model, currently we do not observe obvious bias in generated results. We conjecture the main reason is that our model does not rely on text prompts but on low-resolution images. Such strong conditions make our model less likely to be affected. ## Training Training Data The model developer used the following dataset for training the model: - Our diffusion model is finetuned on DF2K (DIV2K and Flickr2K) + OST datasets, available [here](https://github.com/xinntao/Real-ESRGAN/blob/master/docs/Training.md). - We further generate 100k synthetic LR-HR pairs on DF2K_OST using the finetuned diffusion model for training the CFW module. Training Procedure StableSR is an image super-resolution model finetuned on [Stable Diffusion](https://github.com/Stability-AI/stablediffusion), further equipped with a time-aware encoder and a controllable feature wrapping (CFW) module. - Following Stable Diffusion, images are encoded through the fixed autoencoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4. - The latent representations are fed to the time-aware encoder as guidance. - The loss is the same as Stable Diffusion. - After finetuning the diffusion model, we further train the CFW module using the data generated by the finetuned diffusion model. - The autoencoder model is fixed and only CFW is trainable. - The loss is similar to training an autoencoder, except that we use a fixed adversarial loss weight of 0.025 rather than a self-adjustable one. We currently provide the following checkpoints: - [stablesr_000117.ckpt](https://huggingface.co/Iceclear/StableSR/resolve/main/stablesr_000117.ckpt): Diffusion model finetuned on [SD2.1-512base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base) with DF2K_OST dataset for 117 epochs. - [vqgan_cfw_00011.ckpt](https://huggingface.co/Iceclear/StableSR/resolve/main/vqgan_cfw_00011.ckpt): CFW module with fixed autoencoder trained on synthetic paired data for 11 epochs. - [stablesr_768v_000139.ckpt](https://huggingface.co/Iceclear/StableSR/blob/main/stablesr_768v_000139.ckpt): Diffusion model finetuned on [SD2.1-768v](https://huggingface.co/stabilityai/stable-diffusion-2-1) with DF2K_OST dataset for 139 epochs. ## Evaluation Results See [Paper](https://arxiv.org/abs/2305.07015) for details.
AmelieSchreiber/esm2_t6_8M_UR50D_cafa5_lora	AmelieSchreiber	2023-08-24T20:35:14Z	5	1	peft	[ "peft", "pytorch", "esm", "esm2", "ESM-2", "protein language model", "LoRA", "Low Rank Adaptation", "biology", "CAFA-5", "protein function prediction", "en", "dataset:AmelieSchreiber/cafa_5", "license:mit", "region:us" ]	null	2023-08-22T05:29:29Z	--- library_name: peft tags: - esm - esm2 - ESM-2 - protein language model - LoRA - Low Rank Adaptation - biology - CAFA-5 - protein function prediction datasets: - AmelieSchreiber/cafa_5 license: mit language: - en --- # ESM-2 LoRA for CAFA-5 Protein Function Prediction This is a Low Rank Adaptation (LoRA) of [cafa_5_protein_function_prediction](https://huggingface.co/AmelieSchreiber/cafa_5_protein_function_prediction), which is a fine-tuned (without LoRA) version of `facebook/esm2_t6_8M_UR50D`, for the same task. For more information on training a sequence classifier langauge model with LoRA [see here](https://github.com/huggingface/peft/blob/main/examples/sequence_classification/LoRA.ipynb). Note, this is for natural language processing and must be adapted to our use case using a protein language model like ESM-2. ## Training procedure Using Hugging Face's Parameter Efficient Fine-Tuning (PEFT) library, a Low Rank Adaptation was trained for 3 epochs on the CAFA-5 protein sequences dataset at an 80/20 train/test split. The dataset can be [found here](https://huggingface.co/datasets/AmelieSchreiber/cafa_5). Somewhat naively, the model was trained on the `train_sequences.fasta` file of protein sequences, with the `train_terms.tsv` file serving as the labels. The gene ontology used is a hierarchy, and so the labels lower in the hierchay should be weighted more, or the graph structure should be taken into account. The model achieved the following metrics: ``` Epoch: 3, Validation Loss: 0.0031, Validation Micro F1: 0.3752, Validation Macro F1: 0.9968, Validation Micro Precision: 0.5287, Validation Macro Precision: 0.9992, Validation Micro Recall: 0.2911, Validation Macro Recall: 0.9968 ``` Future iterations of this model will likely need to take into account class weighting. ### Framework versions - PEFT 0.4.0 ## Using the Model To use the model, try downloading the data [from here](https://huggingface.co/datasets/AmelieSchreiber/cafa_5), adjust the paths to the files in the code below to their local paths on your machine, and try running: ```python import os import numpy as np import torch from transformers import AutoTokenizer, EsmForSequenceClassification, AdamW from torch.nn.functional import binary_cross_entropy_with_logits from sklearn.model_selection import train_test_split from sklearn.metrics import f1_score, precision_score, recall_score from accelerate import Accelerator from Bio import SeqIO # Step 1: Data Preprocessing fasta_file = "data/Train/train_sequences.fasta" tsv_file = "data/Train/train_terms.tsv" fasta_data = {} tsv_data = {} for record in SeqIO.parse(fasta_file, "fasta"): fasta_data[record.id] = str(record.seq) with open(tsv_file, 'r') as f: for line in f: parts = line.strip().split("\t") tsv_data[parts[0]] = parts[1:] unique_terms = list(set(term for terms in tsv_data.values() for term in terms)) def parse_fasta(file_path): """ Parses a FASTA file and returns a list of sequences. """ with open(file_path, 'r') as f: content = f.readlines() sequences = [] current_sequence = "" for line in content: if line.startswith(">"): if current_sequence: sequences.append(current_sequence) current_sequence = "" else: current_sequence += line.strip() if current_sequence: sequences.append(current_sequence) return sequences # Parse the provided FASTA file fasta_file_path = "data/Test/testsuperset.fasta" protein_sequences = parse_fasta(fasta_file_path) # protein_sequences[:3] # Displaying the first 3 sequences for verification import torch from transformers import AutoTokenizer, EsmForSequenceClassification from sklearn.metrics import precision_recall_fscore_support # 1. Parsing the go-basic.obo file (Assuming this is still needed) def parse_obo_file(file_path): with open(file_path, 'r') as f: data = f.read().split("[Term]") terms = [] for entry in data[1:]: lines = entry.strip().split("\n") term = {} for line in lines: if line.startswith("id:"): term["id"] = line.split("id:")[1].strip() elif line.startswith("name:"): term["name"] = line.split("name:")[1].strip() elif line.startswith("namespace:"): term["namespace"] = line.split("namespace:")[1].strip() elif line.startswith("def:"): term["definition"] = line.split("def:")[1].split('"')[1] terms.append(term) return terms # Let's assume the path to go-basic.obo is as follows (please modify if different) obo_file_path = "data/Train/go-basic.obo" parsed_terms = parse_obo_file("data/Train/go-basic.obo") # Replace with your path # 2. Load the saved model and tokenizer # Assuming the model path provided is correct from transformers import AutoTokenizer, AutoModelForSequenceClassification from peft import PeftModel, PeftConfig # Load the tokenizer and model model_id = "AmelieSchreiber/esm2_t6_8M_UR50D_cafa5_lora" # Replace with your Hugging Face hub model name tokenizer = AutoTokenizer.from_pretrained(model_id) # First, we load the underlying base model base_model = AutoModelForSequenceClassification.from_pretrained(model_id) # Then, we load the model with PEFT model = PeftModel.from_pretrained(base_model, model_id) loaded_model = model loaded_tokenizer = AutoTokenizer.from_pretrained(model_id) # 3. The predict_protein_function function def predict_protein_function(sequence, model, tokenizer, go_terms): inputs = tokenizer(sequence, return_tensors="pt", padding=True, truncation=True, max_length=1022) model.eval() with torch.no_grad(): outputs = model(**inputs) predictions = torch.sigmoid(outputs.logits) predicted_indices = torch.where(predictions > 0.05)[1].tolist() functions = [] for idx in predicted_indices: term_id = unique_terms[idx] # Use the unique_terms list from your training script for term in go_terms: if term["id"] == term_id: functions.append(term["name"]) break return functions # 4. Predicting protein function for the sequences in the FASTA file protein_functions = {} for seq in protein_sequences[:20]: # Using only the first 3 sequences for demonstration predicted_functions = predict_protein_function(seq, loaded_model, loaded_tokenizer, parsed_terms) protein_functions[seq[:20] + "..."] = predicted_functions # Using first 20 characters as key protein_functions ```
sidroy/bloom-560m	sidroy	2023-08-24T20:33:24Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-24T20:25:46Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0

dt-and-vanilla-ardt/ardt-vanilla-arrl_sgld_train_halfcheetah_high-2508_0900-99

dt-and-vanilla-ardt

2023-08-25T10:09:44Z

31

0

transformers

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

null

2023-08-25T08:01:50Z

--- tags: - generated_from_trainer model-index: - name: ardt-vanilla-arrl_sgld_train_halfcheetah_high-2508_0900-99 results: [] ---  # ardt-vanilla-arrl_sgld_train_halfcheetah_high-2508_0900-99 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

bigmorning/whisper_syl_cv12_pad_lob100__0055

bigmorning

2023-08-25T10:03:47Z

59

0

transformers

[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-25T10:03:38Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_syl_cv12_pad_lob100__0055 results: [] ---  # whisper_syl_cv12_pad_lob100__0055 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0017 - Train Accuracy: 0.0362 - Train Wermet: 0.3475 - Validation Loss: 0.6087 - Validation Accuracy: 0.0238 - Validation Wermet: 0.2213 - Epoch: 54 ## 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 | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch | |:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:| | 5.0233 | 0.0115 | 1.6383 | 3.8616 | 0.0117 | 0.9516 | 0 | | 4.4412 | 0.0127 | 0.8560 | 3.5410 | 0.0125 | 0.8971 | 1 | | 4.0719 | 0.0138 | 0.8366 | 3.2944 | 0.0132 | 0.8706 | 2 | | 3.8091 | 0.0146 | 0.8133 | 3.1691 | 0.0134 | 0.8487 | 3 | | 3.6239 | 0.0152 | 0.7866 | 3.0647 | 0.0136 | 0.8282 | 4 | | 3.4749 | 0.0156 | 0.7589 | 2.9835 | 0.0139 | 0.8049 | 5 | | 3.3444 | 0.0161 | 0.7359 | 2.9351 | 0.0140 | 0.7979 | 6 | | 3.2215 | 0.0165 | 0.7138 | 2.8468 | 0.0145 | 0.7589 | 7 | | 3.0754 | 0.0172 | 0.6873 | 2.7530 | 0.0148 | 0.7413 | 8 | | 2.8713 | 0.0181 | 0.6484 | 2.5226 | 0.0157 | 0.7017 | 9 | | 2.5469 | 0.0197 | 0.5934 | 2.1931 | 0.0168 | 0.6285 | 10 | | 2.0233 | 0.0225 | 0.4997 | 1.6411 | 0.0189 | 0.5215 | 11 | | 1.3808 | 0.0264 | 0.3852 | 1.2401 | 0.0205 | 0.4238 | 12 | | 0.9722 | 0.0290 | 0.3123 | 1.0195 | 0.0215 | 0.3682 | 13 | | 0.7388 | 0.0305 | 0.2828 | 0.8773 | 0.0221 | 0.3322 | 14 | | 0.5787 | 0.0317 | 0.2751 | 0.7970 | 0.0225 | 0.3083 | 15 | | 0.4642 | 0.0325 | 0.2878 | 0.7315 | 0.0227 | 0.2964 | 16 | | 0.3752 | 0.0332 | 0.4217 | 0.6897 | 0.0229 | 0.3297 | 17 | | 0.3042 | 0.0338 | 0.7294 | 0.6572 | 0.0231 | 0.4453 | 18 | | 0.2444 | 0.0343 | 1.1298 | 0.6369 | 0.0232 | 0.6637 | 19 | | 0.1949 | 0.0348 | 1.6370 | 0.6180 | 0.0233 | 1.6119 | 20 | | 0.1544 | 0.0352 | 1.6151 | 0.6149 | 0.0233 | 1.6843 | 21 | | 0.1212 | 0.0355 | 1.3832 | 0.6066 | 0.0233 | 0.8721 | 22 | | 0.0931 | 0.0357 | 1.2799 | 0.6034 | 0.0234 | 0.5109 | 23 | | 0.0725 | 0.0359 | 1.0940 | 0.6102 | 0.0234 | 1.0111 | 24 | | 0.0551 | 0.0361 | 1.2865 | 0.6000 | 0.0234 | 1.1393 | 25 | | 0.0411 | 0.0361 | 1.8511 | 0.6037 | 0.0235 | 2.0574 | 26 | | 0.0311 | 0.0362 | 1.7179 | 0.6018 | 0.0235 | 1.4847 | 27 | | 0.0253 | 0.0362 | 0.9801 | 0.6010 | 0.0235 | 0.4457 | 28 | | 0.0231 | 0.0362 | 0.9376 | 0.6046 | 0.0235 | 0.9247 | 29 | | 0.0196 | 0.0362 | 0.6466 | 0.6078 | 0.0235 | 0.5271 | 30 | | 0.0177 | 0.0362 | 0.4041 | 0.6155 | 0.0235 | 0.4352 | 31 | | 0.0139 | 0.0362 | 0.4202 | 0.6037 | 0.0236 | 0.5585 | 32 | | 0.0137 | 0.0362 | 0.8151 | 0.6015 | 0.0236 | 1.8476 | 33 | | 0.0122 | 0.0362 | 3.4515 | 0.6043 | 0.0236 | 3.8210 | 34 | | 0.0098 | 0.0362 | 1.1787 | 0.5985 | 0.0236 | 0.8094 | 35 | | 0.0071 | 0.0362 | 0.9920 | 0.5992 | 0.0236 | 0.8755 | 36 | | 0.0055 | 0.0362 | 2.4665 | 0.6047 | 0.0236 | 2.0127 | 37 | | 0.0124 | 0.0362 | 4.2468 | 0.6089 | 0.0236 | 2.8886 | 38 | | 0.0109 | 0.0362 | 2.0177 | 0.6097 | 0.0236 | 0.3417 | 39 | | 0.0073 | 0.0362 | 0.9927 | 0.6057 | 0.0237 | 2.5519 | 40 | | 0.0080 | 0.0362 | 1.7341 | 0.6099 | 0.0236 | 1.3119 | 41 | | 0.0063 | 0.0362 | 2.4288 | 0.6058 | 0.0237 | 1.3465 | 42 | | 0.0038 | 0.0362 | 1.4535 | 0.6022 | 0.0237 | 1.6804 | 43 | | 0.0028 | 0.0362 | 2.2629 | 0.6001 | 0.0238 | 3.4388 | 44 | | 0.0021 | 0.0362 | 3.5877 | 0.6018 | 0.0238 | 2.6165 | 45 | | 0.0017 | 0.0362 | 3.0080 | 0.6043 | 0.0238 | 2.6827 | 46 | | 0.0061 | 0.0362 | 2.5182 | 0.6545 | 0.0235 | 0.2316 | 47 | | 0.0126 | 0.0362 | 0.2097 | 0.6206 | 0.0236 | 0.6194 | 48 | | 0.0071 | 0.0362 | 0.3045 | 0.6047 | 0.0237 | 0.7476 | 49 | | 0.0053 | 0.0362 | 1.2045 | 0.6010 | 0.0238 | 0.6553 | 50 | | 0.0040 | 0.0362 | 0.2626 | 0.5964 | 0.0238 | 0.7027 | 51 | | 0.0021 | 0.0362 | 0.5023 | 0.5950 | 0.0238 | 0.3812 | 52 | | 0.0014 | 0.0362 | 0.7108 | 0.6233 | 0.0237 | 1.4647 | 53 | | 0.0017 | 0.0362 | 0.3475 | 0.6087 | 0.0238 | 0.2213 | 54 | ### Framework versions - Transformers 4.33.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3

jjimdark/distilbert-base-uncased-finetuned-cola

jjimdark

2023-08-25T10:02:06Z

3

0

transformers

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

text-classification

2023-08-24T04:35:29Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5290369945616428 ---  # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5219 - Matthews Correlation: 0.5290 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 1.0 | 268 | 0.5025 | 0.4154 | | 0.4551 | 2.0 | 536 | 0.5071 | 0.4792 | | 0.4551 | 3.0 | 804 | 0.5219 | 0.5290 | | 0.2312 | 4.0 | 1072 | 0.6287 | 0.5089 | | 0.2312 | 5.0 | 1340 | 0.6631 | 0.5182 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3

amritansh/replit_3B

amritansh

2023-08-25T09:56:50Z

5

0

transformers

[ "transformers", "mpt", "text-generation", "custom_code", "license:other", "autotrain_compatible", "region:us" ]

text-generation

2023-08-25T09:51:52Z

--- license: other --- --- This is a [ggml](https://github.com/ggerganov/ggml/) quantized version of [Replit-v2-CodeInstruct-3B](https://huggingface.co/teknium/Replit-v2-CodeInstruct-3B). Quantized to 4bit -> q4_1. To run inference you can use ggml directly or [ctransformers](https://github.com/marella/ctransformers). - Memory usage of model: **2GB~** - Repo to run the model using ctransformers: https://github.com/abacaj/replit-3B-inference

arroyadr/speecht5_finetuned_voxpopuli_it

arroyadr

2023-08-25T09:53:57Z

79

0

transformers

[ "transformers", "pytorch", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "model-index", "endpoints_compatible", "region:us" ]

text-to-speech

2023-08-25T08:40:42Z

--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer - text-to-speech datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_it results: - task: name: text-to-speech type: text-to-speech dataset: name: VOXPOPULI type: facebook/voxpopuli config: it split: train args: all metrics: - name: MSE type: mse value: 0.5028 ---  # speecht5_finetuned_voxpopuli_it This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.5028 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - 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: 50 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5198 | 31.37 | 1000 | 0.5028 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

softaken/Softaken-OLM-to-PST-Converter

softaken

2023-08-25T09:46:47Z

0

null

[ "region:us" ]

null

2023-08-25T09:15:59Z

Softaken OLM to PST Converter Software is the best and most advanced software to convert Windows Mac OLM emails to Outlook PST file format. All users do not need to install other software to convert OLM files to the PST file format. Both professional and non-professional users can also use this tool to convert OLM files to the PST file format. The computer application immediately converts OLM files to Outlook PST file format without taking a long time or losing any single file. Users can load any size file or folder that they want to convert to PST file format without any data limitations. Users can use this tool in any Windows version, such as Windows 11, 10, 8.1, 8, 7, Vista, XP, etc. The app also supports all MS Outlook versions, such as 2002, 2003, 2007, 2010, 2013, 2016, and 2019. The app is fully secure to convert OLM files to PST file format. The computer program has multiple advanced and astonishing features that are helpful for non-technical users who want to convert OLM files to the PST file format. To check out the latest features and capabilities of the software, download the free demo version of this app. Read More: https://www.softaken.com/olm-to-pst-converter/

bigmorning/whisper_syl_cv12_pad_lob100__0045

bigmorning

2023-08-25T09:37:20Z

59

0

transformers

[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-25T09:37:12Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_syl_cv12_pad_lob100__0045 results: [] ---  # whisper_syl_cv12_pad_lob100__0045 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0028 - Train Accuracy: 0.0362 - Train Wermet: 2.2629 - Validation Loss: 0.6001 - Validation Accuracy: 0.0238 - Validation Wermet: 3.4388 - Epoch: 44 ## 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 | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch | |:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:| | 5.0233 | 0.0115 | 1.6383 | 3.8616 | 0.0117 | 0.9516 | 0 | | 4.4412 | 0.0127 | 0.8560 | 3.5410 | 0.0125 | 0.8971 | 1 | | 4.0719 | 0.0138 | 0.8366 | 3.2944 | 0.0132 | 0.8706 | 2 | | 3.8091 | 0.0146 | 0.8133 | 3.1691 | 0.0134 | 0.8487 | 3 | | 3.6239 | 0.0152 | 0.7866 | 3.0647 | 0.0136 | 0.8282 | 4 | | 3.4749 | 0.0156 | 0.7589 | 2.9835 | 0.0139 | 0.8049 | 5 | | 3.3444 | 0.0161 | 0.7359 | 2.9351 | 0.0140 | 0.7979 | 6 | | 3.2215 | 0.0165 | 0.7138 | 2.8468 | 0.0145 | 0.7589 | 7 | | 3.0754 | 0.0172 | 0.6873 | 2.7530 | 0.0148 | 0.7413 | 8 | | 2.8713 | 0.0181 | 0.6484 | 2.5226 | 0.0157 | 0.7017 | 9 | | 2.5469 | 0.0197 | 0.5934 | 2.1931 | 0.0168 | 0.6285 | 10 | | 2.0233 | 0.0225 | 0.4997 | 1.6411 | 0.0189 | 0.5215 | 11 | | 1.3808 | 0.0264 | 0.3852 | 1.2401 | 0.0205 | 0.4238 | 12 | | 0.9722 | 0.0290 | 0.3123 | 1.0195 | 0.0215 | 0.3682 | 13 | | 0.7388 | 0.0305 | 0.2828 | 0.8773 | 0.0221 | 0.3322 | 14 | | 0.5787 | 0.0317 | 0.2751 | 0.7970 | 0.0225 | 0.3083 | 15 | | 0.4642 | 0.0325 | 0.2878 | 0.7315 | 0.0227 | 0.2964 | 16 | | 0.3752 | 0.0332 | 0.4217 | 0.6897 | 0.0229 | 0.3297 | 17 | | 0.3042 | 0.0338 | 0.7294 | 0.6572 | 0.0231 | 0.4453 | 18 | | 0.2444 | 0.0343 | 1.1298 | 0.6369 | 0.0232 | 0.6637 | 19 | | 0.1949 | 0.0348 | 1.6370 | 0.6180 | 0.0233 | 1.6119 | 20 | | 0.1544 | 0.0352 | 1.6151 | 0.6149 | 0.0233 | 1.6843 | 21 | | 0.1212 | 0.0355 | 1.3832 | 0.6066 | 0.0233 | 0.8721 | 22 | | 0.0931 | 0.0357 | 1.2799 | 0.6034 | 0.0234 | 0.5109 | 23 | | 0.0725 | 0.0359 | 1.0940 | 0.6102 | 0.0234 | 1.0111 | 24 | | 0.0551 | 0.0361 | 1.2865 | 0.6000 | 0.0234 | 1.1393 | 25 | | 0.0411 | 0.0361 | 1.8511 | 0.6037 | 0.0235 | 2.0574 | 26 | | 0.0311 | 0.0362 | 1.7179 | 0.6018 | 0.0235 | 1.4847 | 27 | | 0.0253 | 0.0362 | 0.9801 | 0.6010 | 0.0235 | 0.4457 | 28 | | 0.0231 | 0.0362 | 0.9376 | 0.6046 | 0.0235 | 0.9247 | 29 | | 0.0196 | 0.0362 | 0.6466 | 0.6078 | 0.0235 | 0.5271 | 30 | | 0.0177 | 0.0362 | 0.4041 | 0.6155 | 0.0235 | 0.4352 | 31 | | 0.0139 | 0.0362 | 0.4202 | 0.6037 | 0.0236 | 0.5585 | 32 | | 0.0137 | 0.0362 | 0.8151 | 0.6015 | 0.0236 | 1.8476 | 33 | | 0.0122 | 0.0362 | 3.4515 | 0.6043 | 0.0236 | 3.8210 | 34 | | 0.0098 | 0.0362 | 1.1787 | 0.5985 | 0.0236 | 0.8094 | 35 | | 0.0071 | 0.0362 | 0.9920 | 0.5992 | 0.0236 | 0.8755 | 36 | | 0.0055 | 0.0362 | 2.4665 | 0.6047 | 0.0236 | 2.0127 | 37 | | 0.0124 | 0.0362 | 4.2468 | 0.6089 | 0.0236 | 2.8886 | 38 | | 0.0109 | 0.0362 | 2.0177 | 0.6097 | 0.0236 | 0.3417 | 39 | | 0.0073 | 0.0362 | 0.9927 | 0.6057 | 0.0237 | 2.5519 | 40 | | 0.0080 | 0.0362 | 1.7341 | 0.6099 | 0.0236 | 1.3119 | 41 | | 0.0063 | 0.0362 | 2.4288 | 0.6058 | 0.0237 | 1.3465 | 42 | | 0.0038 | 0.0362 | 1.4535 | 0.6022 | 0.0237 | 1.6804 | 43 | | 0.0028 | 0.0362 | 2.2629 | 0.6001 | 0.0238 | 3.4388 | 44 | ### Framework versions - Transformers 4.33.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3

gvolovskiy/ppo-LunarLander-v2

gvolovskiy

2023-08-25T09:35:34Z

0

stable-baselines3

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

reinforcement-learning

2023-08-25T09:35:11Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: ppo results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 253.05 +/- 21.23 name: mean_reward verified: false --- # **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 ... ```

larabe/testt

larabe

2023-08-25T09:21:37Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]

image-text-to-text

2023-08-23T22:01:20Z

--- license: mit tags: - generated_from_trainer model-index: - name: testt results: [] ---  # testt This model is a fine-tuned version of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2) 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: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.0 - Tokenizers 0.13.3

64FC/whisper-tiny-en

64FC

2023-08-25T09:20:10Z

76

0

transformers

[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-25T08:36:01Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-en results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.36186540731995276 ---  # whisper-tiny-en This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.7102 - Wer Ortho: 0.3646 - Wer: 0.3619 ## 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: 23 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | 0.0005 | 35.71 | 500 | 0.7102 | 0.3646 | 0.3619 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

AbeShinzo0708/Voicevox_SugaYoshihide

AbeShinzo0708

2023-08-25T09:18:05Z

0

1

null

[ "菅義偉", "Former Japanese Prime Minister", "Suga", "SugaYoshihide", "Yoshihide", "ja", "license:openrail", "region:us" ]

null

2023-03-18T09:38:23Z

--- license: openrail language: - ja tags: - 菅義偉 - Former Japanese Prime Minister - Suga - SugaYoshihide - Yoshihide ---

caffeinatedwoof/Llama-2-7b-chat-hf-mental_health_counseling_conversations_peft

caffeinatedwoof

2023-08-25T09:10:37Z

0

peft

[ "peft", "region:us" ]

null

2023-08-24T07:27:26Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0

Isotonic/flan-t5-base-trading_candles

Isotonic

2023-08-25T09:10:33Z

126

11

transformers

[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "dataset:0xMaka/trading-candles-subset-qa-format", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-08-22T16:32:50Z

--- base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-base-trading_candles results: [] datasets: - 0xMaka/trading-candles-subset-qa-format widget: - text: "Context: -30811302.00,464.00,-156202.00,309984.00,276.00,7664.00,4174.00,824467.00,19741.12,19798.04,19860.18,19567.9 Question: identify candle" - text: "Context: 867553.00,-4282049.00,6306.00,4440418.00,13.00,50962.00,101.00,59152496.00,39512.71,39477.49,39512.71,39380.74 Question: identify candle" - text: "Context: -206.00,626162.00,-35917428.00,-49739.00,6669939.00,64.00,19988.00,7094559.00,17752.71,17752.71,17752.71,17752.71 Question: find candle: Four Price Doji" pipeline_tag: text2text-generation ---  # flan-t5-base-trading_candles This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on [0xMaka/trading-candles-subset-qa-format](https://huggingface.co/datasets/0xMaka/trading-candles-subset-qa-format) dataset. It achieves the following results on the evaluation set: - Loss: 0.0061 - Rouge1: 88.3665 - Rouge2: 86.86 - Rougel: 88.3651 - Rougelsum: 88.3665 - Gen Len: 18.9025 ## 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: 4 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.019 | 1.0 | 70009 | 0.0089 | 88.0774 | 86.4734 | 88.0734 | 88.0748 | 18.9022 | | 0.0095 | 2.0 | 140018 | 0.0069 | 88.3636 | 86.8542 | 88.3612 | 88.3625 | 18.9016 | | 0.0071 | 3.0 | 210027 | 0.0061 | 88.3665 | 86.86 | 88.3651 | 88.3665 | 18.9025 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

nishant-glance/model-sd-1-4-priorp-unet-1200

nishant-glance

2023-08-25T08:55:23Z

1

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-25T08:22:30Z

--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - nishant-glance/model-sd-1-4-priorp-unet-1200 This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks person using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: True.

diana9m/falcon-7b-sharded-bf16-finetuned-mental-health-NUNA_reevaluated

diana9m

2023-08-25T08:47:26Z

0

null

[ "generated_from_trainer", "base_model:ybelkada/falcon-7b-sharded-bf16", "base_model:finetune:ybelkada/falcon-7b-sharded-bf16", "region:us" ]

null

2023-08-24T13:06:28Z

--- base_model: ybelkada/falcon-7b-sharded-bf16 tags: - generated_from_trainer model-index: - name: falcon-7b-sharded-bf16-finetuned-mental-health-NUNA_reevaluated results: [] ---  # falcon-7b-sharded-bf16-finetuned-mental-health-NUNA_reevaluated This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 320 ### Training results ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

bigmorning/whisper_syl_cv12_pad_lob100__0025

bigmorning

2023-08-25T08:44:26Z

59

0

transformers

[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-25T08:44:18Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_syl_cv12_pad_lob100__0025 results: [] ---  # whisper_syl_cv12_pad_lob100__0025 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0725 - Train Accuracy: 0.0359 - Train Wermet: 1.0940 - Validation Loss: 0.6102 - Validation Accuracy: 0.0234 - Validation Wermet: 1.0111 - Epoch: 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: - 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 | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch | |:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:| | 5.0233 | 0.0115 | 1.6383 | 3.8616 | 0.0117 | 0.9516 | 0 | | 4.4412 | 0.0127 | 0.8560 | 3.5410 | 0.0125 | 0.8971 | 1 | | 4.0719 | 0.0138 | 0.8366 | 3.2944 | 0.0132 | 0.8706 | 2 | | 3.8091 | 0.0146 | 0.8133 | 3.1691 | 0.0134 | 0.8487 | 3 | | 3.6239 | 0.0152 | 0.7866 | 3.0647 | 0.0136 | 0.8282 | 4 | | 3.4749 | 0.0156 | 0.7589 | 2.9835 | 0.0139 | 0.8049 | 5 | | 3.3444 | 0.0161 | 0.7359 | 2.9351 | 0.0140 | 0.7979 | 6 | | 3.2215 | 0.0165 | 0.7138 | 2.8468 | 0.0145 | 0.7589 | 7 | | 3.0754 | 0.0172 | 0.6873 | 2.7530 | 0.0148 | 0.7413 | 8 | | 2.8713 | 0.0181 | 0.6484 | 2.5226 | 0.0157 | 0.7017 | 9 | | 2.5469 | 0.0197 | 0.5934 | 2.1931 | 0.0168 | 0.6285 | 10 | | 2.0233 | 0.0225 | 0.4997 | 1.6411 | 0.0189 | 0.5215 | 11 | | 1.3808 | 0.0264 | 0.3852 | 1.2401 | 0.0205 | 0.4238 | 12 | | 0.9722 | 0.0290 | 0.3123 | 1.0195 | 0.0215 | 0.3682 | 13 | | 0.7388 | 0.0305 | 0.2828 | 0.8773 | 0.0221 | 0.3322 | 14 | | 0.5787 | 0.0317 | 0.2751 | 0.7970 | 0.0225 | 0.3083 | 15 | | 0.4642 | 0.0325 | 0.2878 | 0.7315 | 0.0227 | 0.2964 | 16 | | 0.3752 | 0.0332 | 0.4217 | 0.6897 | 0.0229 | 0.3297 | 17 | | 0.3042 | 0.0338 | 0.7294 | 0.6572 | 0.0231 | 0.4453 | 18 | | 0.2444 | 0.0343 | 1.1298 | 0.6369 | 0.0232 | 0.6637 | 19 | | 0.1949 | 0.0348 | 1.6370 | 0.6180 | 0.0233 | 1.6119 | 20 | | 0.1544 | 0.0352 | 1.6151 | 0.6149 | 0.0233 | 1.6843 | 21 | | 0.1212 | 0.0355 | 1.3832 | 0.6066 | 0.0233 | 0.8721 | 22 | | 0.0931 | 0.0357 | 1.2799 | 0.6034 | 0.0234 | 0.5109 | 23 | | 0.0725 | 0.0359 | 1.0940 | 0.6102 | 0.0234 | 1.0111 | 24 | ### Framework versions - Transformers 4.33.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3

libin46/llama2-qlora-finetunined-french

libin46

2023-08-25T08:40:12Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-25T08:40:04Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0

Donnaphat/bloomz-560m_PROMPT_TUNING_CAUSAL_LM

Donnaphat

2023-08-25T08:36:44Z

0

peft

[ "peft", "region:us" ]

null

2023-08-25T08:36:41Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0

feliciamj/ppo-Huggy

feliciamj

2023-08-25T08:32:29Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2023-08-25T08:32:23Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: feliciamj/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Vertti/TuumaPEFTDialogue01

Vertti

2023-08-25T08:29:37Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-25T08:29:18Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0

arroyadr/speecht5_finetuned_voxpopuli_nl

arroyadr

2023-08-25T08:26:45Z

75

0

transformers

[ "transformers", "pytorch", "speecht5", "text-to-audio", "generated_from_trainer", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-audio

2023-08-24T21:59:06Z

--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl results: [] ---  # speecht5_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.5937 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - 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: 50 - training_steps: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.6482 | 3.14 | 100 | 0.5937 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

bigmorning/whisper_syl_cv12_pad_lob100__0015

bigmorning

2023-08-25T08:18:04Z

59

0

transformers

[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-25T08:17:55Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_syl_cv12_pad_lob100__0015 results: [] ---  # whisper_syl_cv12_pad_lob100__0015 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7388 - Train Accuracy: 0.0305 - Train Wermet: 0.2828 - Validation Loss: 0.8773 - Validation Accuracy: 0.0221 - Validation Wermet: 0.3322 - Epoch: 14 ## 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 | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch | |:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:| | 5.0233 | 0.0115 | 1.6383 | 3.8616 | 0.0117 | 0.9516 | 0 | | 4.4412 | 0.0127 | 0.8560 | 3.5410 | 0.0125 | 0.8971 | 1 | | 4.0719 | 0.0138 | 0.8366 | 3.2944 | 0.0132 | 0.8706 | 2 | | 3.8091 | 0.0146 | 0.8133 | 3.1691 | 0.0134 | 0.8487 | 3 | | 3.6239 | 0.0152 | 0.7866 | 3.0647 | 0.0136 | 0.8282 | 4 | | 3.4749 | 0.0156 | 0.7589 | 2.9835 | 0.0139 | 0.8049 | 5 | | 3.3444 | 0.0161 | 0.7359 | 2.9351 | 0.0140 | 0.7979 | 6 | | 3.2215 | 0.0165 | 0.7138 | 2.8468 | 0.0145 | 0.7589 | 7 | | 3.0754 | 0.0172 | 0.6873 | 2.7530 | 0.0148 | 0.7413 | 8 | | 2.8713 | 0.0181 | 0.6484 | 2.5226 | 0.0157 | 0.7017 | 9 | | 2.5469 | 0.0197 | 0.5934 | 2.1931 | 0.0168 | 0.6285 | 10 | | 2.0233 | 0.0225 | 0.4997 | 1.6411 | 0.0189 | 0.5215 | 11 | | 1.3808 | 0.0264 | 0.3852 | 1.2401 | 0.0205 | 0.4238 | 12 | | 0.9722 | 0.0290 | 0.3123 | 1.0195 | 0.0215 | 0.3682 | 13 | | 0.7388 | 0.0305 | 0.2828 | 0.8773 | 0.0221 | 0.3322 | 14 | ### Framework versions - Transformers 4.33.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3

zimhe/controlnet-wall-constrained-floorplan

zimhe

2023-08-25T08:06:23Z

3

0

diffusers

[ "diffusers", "safetensors", "dataset:zimhe/wall-constrained-floorplans-10k", "region:us" ]

null

2023-08-23T09:05:20Z

--- datasets: - zimhe/wall-constrained-floorplans-10k ---

dt-and-vanilla-ardt/ardt-vanilla-arrl_sgld_train_halfcheetah_high-2508_0648-66

dt-and-vanilla-ardt

2023-08-25T08:00:01Z

31

0

transformers

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

null

2023-08-25T05:49:57Z

--- tags: - generated_from_trainer model-index: - name: ardt-vanilla-arrl_sgld_train_halfcheetah_high-2508_0648-66 results: [] ---  # ardt-vanilla-arrl_sgld_train_halfcheetah_high-2508_0648-66 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

LibrAI/longformer-action-ro

LibrAI

2023-08-25T07:58:30Z

2,940

0

transformers

[ "transformers", "pytorch", "longformer", "text-classification", "generated_from_trainer", "base_model:allenai/longformer-base-4096", "base_model:finetune:allenai/longformer-base-4096", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-24T12:33:56Z

--- license: apache-2.0 base_model: allenai/longformer-base-4096 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: longformer-action-ro results: [] ---  # longformer-action-ro This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1084 - Accuracy: 0.964 - Precision: 0.961 - Recall: 0.936 - F1: 0.946 ## 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: 64 - eval_batch_size: 128 - 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 | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:-----:| | No log | 1.0 | 89 | 0.2301 | 0.926 | 0.933 | 0.861 | 0.883 | | No log | 2.0 | 178 | 0.1487 | 0.964 | 0.968 | 0.915 | 0.937 | | No log | 3.0 | 267 | 0.1084 | 0.964 | 0.961 | 0.936 | 0.946 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

Koantek/dolly_llama-v4

Koantek

2023-08-25T07:56:08Z

3

0

peft

[ "peft", "region:us" ]

null

2023-08-25T07:56:05Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0

Intel/whisper-base-int8-dynamic-inc

Intel

2023-08-25T07:55:37Z

4

1

transformers

[ "transformers", "onnx", "whisper", "automatic-speech-recognition", "int8", "ONNX", "PostTrainingDynamic", "Intel® Neural Compressor", "neural-compressor", "dataset:librispeech_asr", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-25T07:50:51Z

--- license: apache-2.0 datasets: - librispeech_asr metrics: - wer pipeline_tag: automatic-speech-recognition tags: - automatic-speech-recognition - int8 - ONNX - PostTrainingDynamic - Intel® Neural Compressor - neural-compressor library_name: transformers --- ## Model Details: INT8 Whisper base Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. This int8 ONNX model is generated by [neural-compressor](https://github.com/intel/neural-compressor) and the fp32 model can be exported with below command: ```shell optimum-cli export onnx --model openai/whisper-base whisper-base-with-past/ --task automatic-speech-recognition-with-past --opset 13 ``` | Model Detail | Description | | ----------- | ----------- | | Model Authors - Company | Intel | | Date | August 25, 2023 | | Version | 1 | | Type | Speech Recognition | | Paper or Other Resources | - | | License | Apache 2.0 | | Questions or Comments | [Community Tab](https://huggingface.co/Intel/whisper-base-int8-dynamic/discussions)| | Intended Use | Description | | ----------- | ----------- | | Primary intended uses | You can use the raw model for automatic speech recognition inference | | Primary intended users | Anyone doing automatic speech recognition inference | | Out-of-scope uses | This model in most cases will need to be fine-tuned for your particular task. The model should not be used to intentionally create hostile or alienating environments for people.| ### How to use Download the model by cloning the repository: ```shell git clone https://huggingface.co/Intel/whisper-base-int8-dynamic ``` Evaluate the model with below code: ```python import os from evaluate import load from datasets import load_dataset from transformers import WhisperForConditionalGeneration, WhisperProcessor, AutoConfig model_name = 'openai/whisper-base' model_path = 'whisper-base-int8-dynamic' processor = WhisperProcessor.from_pretrained(model_name) model = WhisperForConditionalGeneration.from_pretrained(model_name) config = AutoConfig.from_pretrained(model_name) wer = load("wer") librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test") from optimum.onnxruntime import ORTModelForSpeechSeq2Seq from transformers import PretrainedConfig model_config = PretrainedConfig.from_pretrained(model_name) predictions = [] references = [] sessions = ORTModelForSpeechSeq2Seq.load_model( os.path.join(model_path, 'encoder_model.onnx'), os.path.join(model_path, 'decoder_model.onnx'), os.path.join(model_path, 'decoder_with_past_model.onnx')) model = ORTModelForSpeechSeq2Seq(sessions[0], sessions[1], model_config, model_path, sessions[2]) for idx, batch in enumerate(librispeech_test_clean): audio = batch["audio"] input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features reference = processor.tokenizer._normalize(batch['text']) references.append(reference) predicted_ids = model.generate(input_features)[0] transcription = processor.decode(predicted_ids) prediction = processor.tokenizer._normalize(transcription) predictions.append(prediction) wer_result = wer.compute(references=references, predictions=predictions) print(f"Result wer: {wer_result * 100}") accuracy = 1 - wer_result print("Accuracy: %.5f" % accuracy) ``` ## Metrics (Model Performance): | Model | Model Size (GB) | wer | |---|:---:|:---:| | FP32 |0.95|5.04| | INT8 |0.17|5.31|

chrisrtt/gbert-multi-class-german-hate

chrisrtt

2023-08-25T07:53:41Z

645

2

transformers

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

text-classification

2023-08-25T07:39:21Z

# Model Card for German Hate Speech Classifier ## Model Details ### Introduction This model was developed to explore the potential of German language models in multi-class classification of hate speech in German online journals. It is a fine-tuned version of the GBERT model from (Chan, Schweter, and Möller, 2020). ### Dataset The dataset used for training is a consolidation of three pre-existing German hate speech datasets: - **RP (Assenmacher et al., 2021)** - **DeTox (Demus et al., 2022)** - **Twitter dataset (Glasenbach, 2022)** The combined dataset underwent cleaning to minimize biases and remove redundant data. ## Performance Our experiments delivered promising results, with the model reliably classifying comments into: - **No Hate Speech** - **Other Hate Speech (Threat, Insult, Profanity)** - **Political Hate Speech** - **Racist Hate Speech** - **Sexist Hate Speech** The model achieved a macro F1-score of 0.775. However, to further reduce misclassifications, improvements are essential. Short comments are overproportionally classified as Sexist Hate Speech.

bigmorning/whisper_syl_cv12_pad_lob100__0005

bigmorning

2023-08-25T07:51:33Z

59

0

transformers

[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-25T07:51:23Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_syl_cv12_pad_lob100__0005 results: [] ---  # whisper_syl_cv12_pad_lob100__0005 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.6239 - Train Accuracy: 0.0152 - Train Wermet: 0.7866 - Validation Loss: 3.0647 - Validation Accuracy: 0.0136 - Validation Wermet: 0.8282 - Epoch: 4 ## 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 | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch | |:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:| | 5.0233 | 0.0115 | 1.6383 | 3.8616 | 0.0117 | 0.9516 | 0 | | 4.4412 | 0.0127 | 0.8560 | 3.5410 | 0.0125 | 0.8971 | 1 | | 4.0719 | 0.0138 | 0.8366 | 3.2944 | 0.0132 | 0.8706 | 2 | | 3.8091 | 0.0146 | 0.8133 | 3.1691 | 0.0134 | 0.8487 | 3 | | 3.6239 | 0.0152 | 0.7866 | 3.0647 | 0.0136 | 0.8282 | 4 | ### Framework versions - Transformers 4.33.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3

JennnDexter/Translation

JennnDexter

2023-08-25T07:40:49Z

103

0

transformers

[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "en", "fr", "dataset:opus_books", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-08-25T07:12:41Z

--- language: - en - fr license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - opus_books model-index: - name: Translation results: [] ---  # Translation This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books en-fr 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: 16 - eval_batch_size: 16 - 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 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

LibrAI/bert-action-ro

LibrAI

2023-08-25T07:39:45Z

113

1

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-24T12:10:09Z

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: bert-action-ro results: [] ---  # bert-action-ro This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1567 - Accuracy: 0.958 - Precision: 0.949 - Recall: 0.941 - F1: 0.944 ## 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: 64 - eval_batch_size: 128 - 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 | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:-----:| | No log | 1.0 | 89 | 0.3700 | 0.876 | 0.836 | 0.809 | 0.815 | | No log | 2.0 | 178 | 0.2057 | 0.936 | 0.927 | 0.924 | 0.924 | | No log | 3.0 | 267 | 0.1567 | 0.958 | 0.949 | 0.941 | 0.944 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

natsusakiyomi/AsagaoMix

natsusakiyomi

2023-08-25T07:32:49Z

13

7

diffusers

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

text-to-image

2023-07-29T03:42:53Z

--- license: creativeml-openrail-m language: - ja pipeline_tag: text-to-image tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image library_name: diffusers --- --- <h4>📄 ライセンス / License</h4> <div class="px-2"> <table class="table-fixed border mt-0 text-xs"> <tbody> <tr> <td class="px-4 text-base" colspan="2"> <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license"> 修正 CreativeML OpenRAIL-M ライセンス / Modified CreativeML OpenRAIL-M license </a> </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span class="text-green-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" /> </svg> </span> </td> <td> このモデルのクレジットを入れずに使用する<br> Use the model without crediting the creator </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span class="text-green-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" /> </svg> </span> </td> <td> このモデルで生成した画像を商用利用する<br> Sell images they generate </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span class="text-red-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" /> </svg> </span> </td> <td> このモデルを商用の画像生成サービスで利用する</br> Run on services that generate images for money </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span class="text-green-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" /> </svg> </span> </td> <td> このモデルを使用したマージモデルを共有する<br> Share merges using this model </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span class="text-red-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" /> </svg> </span> </td> <td> このモデル、またはこのモデルをマージしたモデルを販売する</br> Sell this model or merges using this model </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span class="text-red-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" /> </svg> </span> </td> <td> このモデルをマージしたモデルに異なる権限を設定する</br> Have different permissions when sharing merges </td> </tr> </tbody> </table> </div>

MateiCv/spa-eng-pos-tagging-v6

MateiCv

2023-08-25T07:27:55Z

180

0

transformers

[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-multilingual-cased", "base_model:finetune:distilbert/distilbert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-08-25T07:27:22Z

--- license: apache-2.0 base_model: distilbert-base-multilingual-cased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: spa-eng-pos-tagging-v6 results: [] ---  # spa-eng-pos-tagging-v6 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3128 - Accuracy: 0.9056 - Precision: 0.9032 - Recall: 0.8293 - F1: 0.8345 - Hamming Loss: 0.0944 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 14 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Hamming Loss | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:------------:| | 1.0141 | 1.0 | 1744 | 0.7804 | 0.7158 | 0.7328 | 0.6183 | 0.6345 | 0.2842 | | 0.6292 | 2.0 | 3488 | 0.5384 | 0.7973 | 0.8111 | 0.7029 | 0.7213 | 0.2027 | | 0.4438 | 3.0 | 5232 | 0.4236 | 0.8462 | 0.8346 | 0.7762 | 0.7732 | 0.1538 | | 0.3626 | 4.0 | 6976 | 0.3856 | 0.8651 | 0.8524 | 0.7933 | 0.7903 | 0.1349 | | 0.3141 | 5.0 | 8720 | 0.3697 | 0.8712 | 0.8688 | 0.7998 | 0.8028 | 0.1288 | | 0.2575 | 6.0 | 10464 | 0.3689 | 0.8751 | 0.8758 | 0.8003 | 0.8058 | 0.1249 | | 0.2117 | 7.0 | 12208 | 0.3329 | 0.8890 | 0.8832 | 0.8169 | 0.8184 | 0.1110 | | 0.1864 | 8.0 | 13952 | 0.3235 | 0.9010 | 0.8946 | 0.8278 | 0.8293 | 0.0990 | | 0.1555 | 9.0 | 15696 | 0.3128 | 0.9056 | 0.9032 | 0.8293 | 0.8345 | 0.0944 | | 0.1322 | 10.0 | 17440 | 0.3311 | 0.9088 | 0.9010 | 0.8376 | 0.8377 | 0.0912 | | 0.1111 | 11.0 | 19184 | 0.3394 | 0.9101 | 0.9081 | 0.8319 | 0.8383 | 0.0899 | | 0.0874 | 12.0 | 20928 | 0.3472 | 0.9148 | 0.9100 | 0.8407 | 0.8440 | 0.0852 | | 0.0659 | 13.0 | 22672 | 0.3635 | 0.9131 | 0.9072 | 0.8400 | 0.8422 | 0.0869 | | 0.0608 | 14.0 | 24416 | 0.3560 | 0.9187 | 0.9140 | 0.8452 | 0.8482 | 0.0813 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Tokenizers 0.13.3

Datactive/BERT_sud_queries_classification

Datactive

2023-08-25T07:14:42Z

61

0

transformers

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

text-classification

2023-07-24T17:02:17Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Datactive/BERT_sud_queries_classification results: [] ---  # Datactive/BERT_sud_queries_classification This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0277 - Validation Loss: 0.0188 - Train F1: 0.9958 - 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1419, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train F1 | Epoch | |:----------:|:---------------:|:--------:|:-----:| | 0.0277 | 0.0188 | 0.9958 | 0 | ### Framework versions - Transformers 4.29.0.dev0 - TensorFlow 2.12.0 - Datasets 2.11.0 - Tokenizers 0.13.3

raygx/BERT-NepSA-domainAdapt

raygx

2023-08-25T06:55:43Z

62

0

transformers

[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:raygx/BertClassifier4NepaliNews", "base_model:finetune:raygx/BertClassifier4NepaliNews", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-24T14:47:00Z

--- license: mit base_model: raygx/BertClassifier4NepaliNews tags: - generated_from_keras_callback model-index: - name: BERT-NepSA-domainAdapt results: [] ---  # BERT-NepSA-domainAdapt This model is a fine-tuned version of [raygx/BertClassifier4NepaliNews](https://huggingface.co/raygx/BertClassifier4NepaliNews) 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: {'name': 'AdamWeightDecay', 'learning_rate': 9.99e-06, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.001} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.32.0 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3

bogeumkim/polyglot-1.3b-qlora-emotion-classification

bogeumkim

2023-08-25T06:35:41Z

2

0

peft

[ "peft", "region:us" ]

null

2023-08-25T06:23:51Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0

rachit221195/rachit-trained-xl-colab

rachit221195

2023-08-25T06:27:16Z

6

1

diffusers

[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2023-08-25T06:04:55Z

--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks human tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - rachit221195/rachit-trained-xl-colab These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks human using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

nishant-glance/model-sd-1-4-priorp-lowlr-unet

nishant-glance

2023-08-25T06:19:48Z

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-25T05:41:42Z

--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - nishant-glance/model-sd-1-4-priorp-lowlr-unet This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks person using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: True.

eunbi-jeong/gpt2

eunbi-jeong

2023-08-25T06:19:07Z

0

null

[ "translation", "en", "dataset:hellaswag", "region:us" ]

translation

2023-08-25T06:17:58Z

--- datasets: - hellaswag language: - en pipeline_tag: translation ---

VkStyle/roma

VkStyle

2023-08-25T06:18:05Z

0

null

[ "arxiv:1910.09700", "region:us" ]

null

2023-08-22T20:25:11Z

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

54data/xlm-roberta-base-finetuned-panx-de-fr

54data

2023-08-25T06:16:07Z

101

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-08-25T06:03:26Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1658 - F1: 0.8588 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2908 | 1.0 | 715 | 0.1909 | 0.8125 | | 0.1466 | 2.0 | 1430 | 0.1613 | 0.8492 | | 0.0945 | 3.0 | 2145 | 0.1658 | 0.8588 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

nithiroj/wav2vec2-base-finetuned-gtzan

nithiroj

2023-08-25T06:07:33Z

159

0

transformers

[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

audio-classification

2023-08-25T03:44:15Z

--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.81 ---  # wav2vec2-base-finetuned-gtzan This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.6608 - Accuracy: 0.81 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9578 | 1.0 | 113 | 1.8537 | 0.28 | | 1.4644 | 2.0 | 226 | 1.5867 | 0.5 | | 0.9624 | 3.0 | 339 | 1.1706 | 0.66 | | 0.8329 | 4.0 | 452 | 0.8807 | 0.76 | | 0.5047 | 5.0 | 565 | 0.9421 | 0.73 | | 0.4525 | 6.0 | 678 | 0.7879 | 0.73 | | 0.5111 | 7.0 | 791 | 0.6493 | 0.79 | | 0.1836 | 8.0 | 904 | 0.5938 | 0.85 | | 0.1806 | 9.0 | 1017 | 0.5787 | 0.84 | | 0.1338 | 10.0 | 1130 | 0.6608 | 0.81 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

hihisu1231/mbti_230825_newdata

hihisu1231

2023-08-25T06:03:01Z

20

0

transformers

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

text-generation

2023-08-25T04:08:40Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: polyglot-1.3b-koalpaca-v1.1a-rtx3090_0825_newdata results: [] ---  # polyglot-1.3b-koalpaca-v1.1a-rtx3090_0825_newdata This model is a fine-tuned version of [EleutherAI/polyglot-ko-1.3b](https://huggingface.co/EleutherAI/polyglot-ko-1.3b) 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1000.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3

cx-olquinjica/angXLMR

cx-olquinjica

2023-08-25T05:47:00Z

106

0

transformers

[ "transformers", "pytorch", "safetensors", "xlm-roberta", "fill-mask", "umb", "lua", "cjk", "kmb", "kg", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-08-25T03:21:00Z

--- language: - umb - lua - cjk - kmb - kg --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

darkbloodevil/bloomz-560m_PROMPT_TUNING_CAUSAL_LM

darkbloodevil

2023-08-25T05:43:48Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-25T05:43:45Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0

64FC/whisper-small-dv

64FC

2023-08-25T05:30:50Z

84

1

transformers

[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dv", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-25T03:35:28Z

--- language: - dv license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Small Dv - Francois Chaumet results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 config: dv split: test args: dv metrics: - name: Wer type: wer value: 11.703237472615363 ---  # Whisper Small Dv - Francois Chaumet This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. It achieves the following results on the evaluation set: - Loss: 0.1658 - Wer Ortho: 59.2103 - Wer: 11.7032 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:| | 0.1231 | 1.63 | 500 | 0.1689 | 61.9054 | 13.0142 | | 0.046 | 3.26 | 1000 | 0.1658 | 59.2103 | 11.7032 | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

dkimds/ppo-LunarLander-v2

dkimds

2023-08-25T05:17:39Z

2

0

transformers

[ "transformers", "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "endpoints_compatible", "region:us" ]

reinforcement-learning

2023-08-01T04:24:12Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -132.01 +/- 71.74 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'dkimds/ppo-LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```

ccore/smart-gpt2-test

ccore

2023-08-25T04:46:08Z

0

3

null

[ "license:gpl-3.0", "region:us" ]

null

2023-08-21T20:05:01Z

--- license: gpl-3.0 --- ./gpt-2 -m ggml-model.bin -p "[INSTRUCTION] your prompt [RESPONSE]" -n 1000 --top_p 1 GGML model - more information about framework - https://github.com/ggerganov/ggml/tree/master/examples/gpt-2

vodkaslime/codellama-7b-hf

vodkaslime

2023-08-25T04:09:00Z

7

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "llama-2", "code", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-08-25T04:03:43Z

--- language: - code pipeline_tag: text-generation tags: - llama-2 license: llama2 --- # **Code Llama** Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the base 7B version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom. | | Base Model | Python | Instruct | | --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) | | 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) | | 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) | Make sure to be using this temporary branch of transformers unit support is fully merged and released. ```bash pip install git+https://github.com/huggingface/transformers.git@refs/pull/25740/head accelerate ``` ```python from transformers import AutoTokenizer import transformers import torch model = "codellama/CodeLlama-7b-hf" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( 'import socket\n\ndef ping_exponential_backoff(host: str):', do_sample=True, top_k=10, temperature=0.1, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Model Details *Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs). **Model Developers** Meta **Variations** Code Llama comes in three model sizes, and three variants: * Code Llama: base models designed for general code synthesis and understanding * Code Llama - Python: designed specifically for Python * Code Llama - Instruct: for instruction following and safer deployment =All variants are available in sizes of 7B, 13B and 34B parameters. **This repository contains the base model of 7B parameters.** **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. **Model Dates** Code Llama and its variants have been trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)". ## Intended Use **Intended Use Cases** Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications. **Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants. ## Hardware and Software **Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster. **Carbon Footprint** In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program. ## Training Data All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details). ## Evaluation Results See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper. ## Ethical Considerations and Limitations Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-user-guide](https://ai.meta.com/llama/responsible-user-guide).

zhang-yice/spt-absa-bert-10k

zhang-yice

2023-08-25T04:06:13Z

33

3

transformers

[ "transformers", "pytorch", "bert", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

null

2023-05-23T10:36:20Z

--- license: cc-by-4.0 --- ## SPT-ABSA We continue to pre-train BERT-base via Sentiment-enhance pre-training (SPT). - Title: An Empirical Study of Sentiment-Enhanced Pre-Training for Aspect-Based Sentiment Analysis - Author: Yice Zhang, Yifan Yang, Bin Liang, Shiwei Chen, Bing Qin, and Ruifeng Xu - Conference: ACL-2023 Finding (Long) GitHub Repository: https://github.com/HITSZ-HLT/SPT-ABSA ### What Did We Do? Aspect-Based Sentiment Analysis (ABSA) is an important problem in sentiment analysis. Its goal is to recognize opinions and sentiments towards specific aspects from user-generated content. Many research efforts leverage pre-training techniques to learn sentiment-aware representations and achieve significant gains in various ABSA tasks. We conduct an empirical study of SPT-ABSA to systematically investigate and analyze the effectiveness of the existing approaches. We mainly concentrate on the following questions: - (a) what impact do different types of sentiment knowledge have on downstream ABSA tasks?; - (b) which knowledge integration method is most effective?; and - (c) does injecting non-sentiment-specific linguistic knowledge (e.g., part-of-speech tags and syntactic relations) into pre-training have positive impacts? Based on the experimental investigation of these questions, we eventually obtain a powerful sentiment-enhanced pre-trained model. The powerful sentiment-enhanced pre-trained model has two versions, namely [zhang-yice/spt-absa-bert-400k](https://huggingface.co/zhang-yice/spt-absa-bert-400k) and [zhang-yice/spt-absa-bert-10k](https://huggingface.co/zhang-yice/spt-absa-bert-10k), which integrates three types of knowledge: - aspect words: masking aspects' context and predicting them. - review's rating score: rating prediction. - syntax knowledge: - part-of-speech, - dependency direction, - dependency distance. ### Experimental Results <img width="75%" alt="image" src="https://github.com/HITSZ-HLT/SPT-ABSA/assets/9134454/38fc2db0-6ccf-47a7-a93c-cf54667e1a23"> <img width="75%" alt="image" src="https://github.com/HITSZ-HLT/SPT-ABSA/assets/9134454/20c5a976-014e-433f-a2ec-4bb259e5a382">

LarryAIDraw/rosaria1

LarryAIDraw

2023-08-25T03:58:54Z

0

null

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

null

2023-08-25T03:54:54Z

--- license: creativeml-openrail-m --- https://civitai.com/models/106421/rosaria-genshin-impact

LarryAIDraw/rosaria

LarryAIDraw

2023-08-25T03:58:15Z

0

null

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

null

2023-08-25T03:52:53Z

--- license: creativeml-openrail-m --- https://civitai.com/models/101711/rosaria-genshin-impact-or-goofy-ai

peteryushunli/codeparrot-ds

peteryushunli

2023-08-25T03:45:36Z

143

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-08-25T02:41:41Z

--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: codeparrot-ds results: [] ---  # codeparrot-ds This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) 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: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

AdanLee/Reinforce-CartPole-v1

AdanLee

2023-08-25T03:45:01Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-08-25T03:44:49Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

rohn132/ppo-Pyramids

rohn132

2023-08-25T03:40:18Z

3

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]

reinforcement-learning

2023-08-25T03:37:43Z

--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: rohn132/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

dt-and-vanilla-ardt/ardt-vanilla-arrl_train_halfcheetah_high-2508_0228-99

dt-and-vanilla-ardt

2023-08-25T03:36:57Z

32

0

transformers

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

null

2023-08-25T01:29:48Z

--- tags: - generated_from_trainer model-index: - name: ardt-vanilla-arrl_train_halfcheetah_high-2508_0228-99 results: [] ---  # ardt-vanilla-arrl_train_halfcheetah_high-2508_0228-99 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

spear1/q-Taxi-v3

spear1

2023-08-25T03:31:57Z

0

null

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

reinforcement-learning

2023-08-25T03:31:56Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.72 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="spear1/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"]) ```

spear1/q-FrozenLake-v1-4x4-noSlippery

spear1

2023-08-25T03:30:17Z

0

null

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

reinforcement-learning

2023-08-25T03:30:15Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="spear1/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"]) ```

DataMonke/bert-base-uncased-finetuned-review-sentiment-analysis

DataMonke

2023-08-25T03:27:20Z

4

2

transformers

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

text-classification

2023-08-21T14:55:06Z

--- language: - en metrics: - accuracy pipeline_tag: text-classification datasets: - amazon_us_reviews --- # E-Commerce Product Sentiment Analysis This model classifies texts into stars categories ranging from 1 to 5. This model has a BERT base and further finetuned on Amazon and e-commerce clothing product reviews.

AdanLee/dqn-SpaceInvadersNoFrameskip-v4

AdanLee

2023-08-25T03:15:50Z

0

stable-baselines3

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

reinforcement-learning

2023-08-25T03:15:07Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 690.50 +/- 213.28 name: mean_reward verified: false --- # **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 Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AdanLee -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AdanLee -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga AdanLee ``` ## 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', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

DunnBC22/trocr-base-handwritten-OCR-handwriting_recognition_v2

DunnBC22

2023-08-25T03:15:17Z

487

14

transformers

[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "image-to-text", "en", "endpoints_compatible", "region:us" ]

image-to-text

2023-04-17T00:13:38Z

--- tags: - generated_from_trainer model-index: - name: trocr-base-handwritten-OCR-handwriting_recognition_v2 results: [] language: - en metrics: - cer pipeline_tag: image-to-text --- # trocr-base-handwritten-OCR-handwriting_recognition_v2 This model is a fine-tuned version of [microsoft/trocr-base-handwritten](https://huggingface.co/microsoft/trocr-base-handwritten). It achieves the following results on the evaluation set: - Loss: 0.2470 - CER: 0.0360 ## Model description For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Optical%20Character%20Recognition%20(OCR)/Handwriting%20Recognition/Handwriting%20Recognition_v2/Mini%20Handwriting%20OCR%20Project.ipynb ## Intended uses & limitations This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to test and experiment with this model, but it is at your own risk/peril. ## Training and evaluation data Dataset Source: https://www.kaggle.com/datasets/ssarkar445/handwriting-recognitionocr _Character Length for Training Dataset:_ ![Input Character Length for Training Dataset](https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Optical%20Character%20Recognition%20(OCR)/Handwriting%20Recognition/Images/Input%20Character%20Length%20Distribution%20for%20Training%20Dataset.png) _Character Length for Evaluation Dataset:_ ![Input Character Length for Evaluation Dataset](https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Optical%20Character%20Recognition%20(OCR)/Handwriting%20Recognition/Images/Input%20Characgter%20Length%20Distribution%20for%20Evaluation%20Dataset.png) ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4292 | 1.0 | 2500 | 0.4332 | 0.0679 | | 0.2521 | 2.0 | 5000 | 0.2767 | 0.0483 | | 0.1049 | 3.0 | 7500 | 0.2470 | 0.0360 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.12.1 - Datasets 2.8.0 - Tokenizers 0.12.1

DunnBC22/trocr-large-printed-cmc7_tesseract_MICR_ocr

DunnBC22

2023-08-25T03:15:01Z

77

4

transformers

[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "image-to-text", "en", "base_model:microsoft/trocr-large-printed", "base_model:finetune:microsoft/trocr-large-printed", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]

image-to-text

2023-07-23T18:53:50Z

--- base_model: microsoft/trocr-large-printed tags: - generated_from_trainer model-index: - name: trocr-large-printed-cmc7_tesseract_MICR_ocr results: [] license: bsd-3-clause language: - en metrics: - cer pipeline_tag: image-to-text --- # trocr-large-printed-cmc7_tesseract_MICR_ocr This model is a fine-tuned version of [microsoft/trocr-large-printed](https://huggingface.co/microsoft/trocr-large-printed). ## Model description For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Optical%20Character%20Recognition%20(OCR)/Tesseract%20MICR%20(CMC7%20Dataset)/TrOCR_cmc7_tesseractMICR.ipynb ## Intended uses & limitations This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to test and experiment with this model, but it is at your own risk/peril. ## Training and evaluation data Dataset Source: https://github.com/DoubangoTelecom/tesseractMICR/tree/master/datasets/cmc7 **Histogram of Label Character Lengths** ![Histogram of Label Character Lengths](https://raw.githubusercontent.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/main/Optical%20Character%20Recognition%20(OCR)/Tesseract%20MICR%20(CMC7%20Dataset)/Images/Histogram%20of%20Label%20Character%20Length.png) ## 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: 5 ### Training results The Character Error Rate (CER) for this model is 0.004970720413999727. ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Chirayu/nl2kql

Chirayu

2023-08-25T03:06:43Z

159

0

transformers

[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "code", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-11T23:00:42Z

--- license: mit tags: - code --- # What does this model do? This model converts the natural language input to Kusto (KQL) query. It is a fine-tuned CodeT5+ 220M. This model is a part of nl2query repository which is present at https://github.com/Chirayu-Tripathi/nl2query You can use this model via the github repository or via following code. More information can be found on the repository. ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import torch model = AutoModelForSeq2SeqLM.from_pretrained("Chirayu/nl2kql") tokenizer = AutoTokenizer.from_pretrained("Chirayu/nl2kql") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = model.to(device) textual_query = '''kusto: find the session ids which have duration greater than 10 and having Manoj Raheja as the owner | conferencesessions : conference, sessionid, session_title, session_type, owner, participants, URL, level, session_location, starttime, duration, time_and_duration, kusto_affinity''' def generate_query( textual_query: str, num_beams: int = 10, max_length: int = 128, repetition_penalty: int = 2.5, length_penalty: int = 1, early_stopping: bool = True, top_p: int = 0.95, top_k: int = 50, num_return_sequences: int = 1, ) -> str: input_ids = tokenizer.encode( textual_query, return_tensors="pt", add_special_tokens=True ) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") input_ids = input_ids.to(device) generated_ids = model.generate( input_ids=input_ids, num_beams=num_beams, max_length=max_length, repetition_penalty=repetition_penalty, length_penalty=length_penalty, early_stopping=early_stopping, top_p=top_p, top_k=top_k, num_return_sequences=num_return_sequences, ) query = [ tokenizer.decode( generated_id, skip_special_tokens=True, clean_up_tokenization_spaces=True, ) for generated_id in generated_ids ][0] return query ```

tyayoi/xlm-roberta-base-finetuned-panx-all

tyayoi

2023-08-25T03:05:19Z

101

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-08-21T11:01:34Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all results: [] ---  # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1761 - F1: 0.8555 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.303 | 1.0 | 835 | 0.1887 | 0.8212 | | 0.1582 | 2.0 | 1670 | 0.1708 | 0.8409 | | 0.1034 | 3.0 | 2505 | 0.1761 | 0.8555 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.13.3

Nilkanth014/ppo-LunarLander-v2

Nilkanth014

2023-08-25T03:00:07Z

4

0

stable-baselines3

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

reinforcement-learning

2023-08-24T21:16:04Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 282.56 +/- 15.68 name: mean_reward verified: false --- # **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 ... ```

tyayoi/xlm-roberta-base-finetuned-panx-de-fr

tyayoi

2023-08-25T02:53:58Z

104

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-08-21T09:18:03Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1642 - F1: 0.8561 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2932 | 1.0 | 715 | 0.1829 | 0.8220 | | 0.1486 | 2.0 | 1430 | 0.1612 | 0.8463 | | 0.0925 | 3.0 | 2145 | 0.1642 | 0.8561 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.13.3

rohn132/ppo-SnowballTarget

rohn132

2023-08-25T02:49:11Z

1

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]

reinforcement-learning

2023-08-25T02:49:03Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: rohn132/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

lukelarue/dqn-SpaceInvadersNoFrameskip-v4

lukelarue

2023-08-25T02:48:59Z

0

stable-baselines3

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

reinforcement-learning

2023-08-25T02:48:25Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 626.50 +/- 166.78 name: mean_reward verified: false --- # **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 Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga lukelarue -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga lukelarue -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga lukelarue ``` ## 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', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

debadas/dog

debadas

2023-08-25T02:35:28Z

1

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-08-25T02:28:07Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - debadas/dog These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.

ad019el/m2m100_418M-finetuned-tq-to-ar-1

ad019el

2023-08-25T02:24:05Z

4

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "generated_from_trainer", "base_model:ad019el/m2m100_418M-finetuned-tq-to-ar", "base_model:finetune:ad019el/m2m100_418M-finetuned-tq-to-ar", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-08-23T02:51:54Z

--- base_model: ad019el/m2m100_418M-finetuned-tq-to-ar tags: - generated_from_trainer metrics: - bleu model-index: - name: m2m100_418M-finetuned-tq-to-ar-1 results: [] ---  # m2m100_418M-finetuned-tq-to-ar-1 This model is a fine-tuned version of [ad019el/m2m100_418M-finetuned-tq-to-ar](https://huggingface.co/ad019el/m2m100_418M-finetuned-tq-to-ar) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2002 - Bleu: 3.6349 - Gen Len: 35.5271 ## 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-06 - 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: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | 2.7537 | 0.71 | 500 | 2.2710 | 4.2969 | 35.4312 | | 2.6442 | 1.42 | 1000 | 2.2373 | 4.0784 | 35.1062 | | 2.6329 | 2.13 | 1500 | 2.2257 | 3.8894 | 36.225 | | 2.564 | 2.84 | 2000 | 2.2210 | 3.5513 | 36.076 | | 2.5352 | 3.56 | 2500 | 2.2151 | 3.7339 | 35.0885 | | 2.4991 | 4.27 | 3000 | 2.2078 | 3.4662 | 36.3333 | | 2.4782 | 4.98 | 3500 | 2.2100 | 3.3332 | 36.4062 | | 2.4363 | 5.69 | 4000 | 2.2085 | 3.3587 | 36.3135 | | 2.4411 | 6.4 | 4500 | 2.2034 | 3.8744 | 34.5073 | | 2.4002 | 7.11 | 5000 | 2.2036 | 3.6693 | 36.3448 | | 2.3841 | 7.82 | 5500 | 2.2030 | 3.7486 | 35.076 | | 2.3619 | 8.53 | 6000 | 2.1970 | 3.5687 | 35.8271 | | 2.3627 | 9.25 | 6500 | 2.2016 | 3.5394 | 35.3583 | | 2.3451 | 9.96 | 7000 | 2.1996 | 3.5863 | 34.9271 | | 2.3323 | 10.67 | 7500 | 2.2002 | 3.6349 | 35.5271 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

tyayoi/xlm-roberta-base-finetuned-panx-de

tyayoi

2023-08-25T02:13:35Z

135

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-08-21T09:01:45Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8606487530534567 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1409 - F1: 0.8606 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2572 | 1.0 | 525 | 0.1538 | 0.8187 | | 0.1233 | 2.0 | 1050 | 0.1475 | 0.8492 | | 0.0796 | 3.0 | 1575 | 0.1409 | 0.8606 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.13.3

TaeLeeKyung/KoSimCSE-roberta-multitask-marketing-lms

TaeLeeKyung

2023-08-25T02:09:51Z

9

0

sentence-transformers

[ "sentence-transformers", "pytorch", "roberta", "sentence-similarity", "marketing", "sts", "nli", "ko", "dataset:TaeLeeKyung/ko_marketing_lms_dataset", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-08-25T01:58:20Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - marketing - sts - nli datasets: - TaeLeeKyung/ko_marketing_lms_dataset language: - ko --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 702 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 4, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 141, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': True}) with Transformer model: RobertaModel (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

Unmand/procare_referrer_org_build2

Unmand

2023-08-25T02:04:44Z

0

spacy

[ "spacy", "text-classification", "en", "region:us" ]

text-classification

2023-08-25T01:36:02Z

--- tags: - spacy - text-classification language: - en model-index: - name: en_procare_referrer_organisation results: [] --- | Feature | Description | | --- | --- | | **Name** | `en_procare_referrer_organisation` | | **Version** | `0.0.0` | | **spaCy** | `>=3.5.4,<3.6.0` | | **Default Pipeline** | `textcat_multilabel` | | **Components** | `textcat_multilabel` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | n/a | | **License** | n/a | | **Author** | [n/a]() | ### Label Scheme <details> <summary>View label scheme (726 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`textcat_multilabel`** | `H D PROJECTS PTY LTD`, `McCabe Curwood`, `Dept of Education, Skills & Employment`, `StateCover Mutual Limited`, `Perth Orthopaedic & Sports Medicine`, `Queensland Child Care Service Pty Ltd Ttee`, `Allianz Australia Insurance Limited c/- Jensen McConaghy Lawyers`, `Catholic Care Diocese of Broken Bay`, `Helping Hand New Aged Care`, `Suncorp Life`, `Qantas Airways Limited`, `Department of Defence`, `Master Builders Association of SA`, `HWL Ebsworth Lawyers`, `Alexander Watson`, `Zoetis`, `RSL Care`, `P&N Bank`, `University of NSW`, `Uber Technologies, Inc.`, `Finlay Plumbing Services Pty Ltd`, `Hays Specialist Recruitment`, `KENNARDS HIRE PTY LIMITED`, `Carer Solutions Australia`, `Unitingcare`, `No. 1 Riverside Quay Proprietary Limited`, `Gallagher Basset`, `Department of the Chief MInister and Cabinet`, `CHEP Australia`, `Minda Incorporated`, `The Star`, `Tas Water`, `Feros Care`, `Roshana Group`, `Atradius Crédito y Caución S.A de Seguros y Reaseguros`, `Services Australia`, `RT Consulting`, `The Australian Electoral Commission`, `Federal Court of Australia`, `NRMA INSURANCE`, `Catholic Education Office`, `Svitzer Australia Pty Ltd`, `QBE acting as the agent of NSW Self Insurance Corporation`, `LAWRENCE & HANSON`, `UnitingCare Queensland`, `LibertyGFG`, `Australian Tax Office`, `Alvaro Transport Pty Ltd`, `GIO Workers Compensation ACT`, `Cso Diocese Of Broken Bay`, `Glencore`, `EASTERN HOSPITAL`, `BOC Limited, a member of the Linde Group`, `INVOCARE AUSTRALIA PTY LIMITED`, `UNITRANS ASIA PACIFIC PTY LTD`, `Services Australia (Dept of Human Services)`, `VEOLIA ENVIRONMENTAL SERVICES (AUSTRALIA) PTY LTD `, `Vickilynn Pty Ltd`, `Coles Team Cover`, `MLC Life Insurance`, `Sparke Helmore Lawyers`, `RSL Lifecare Limited`, `QBE Workers Compensation TAS`, `Kimberley Clark Australia`, `The Personnel Group Ltd`, `Insurance Australia Group`, `Canberra Sand & Gravel`, `Viva Energy Australia Pty Ltd`, `Moran Aged Care Engadine`, `Australian Taxation Office`, `Youis Group Pty Ltd`, `Cleanaway`, `Mosaic Brands (Rockmans)`, `Children Hospital Foundation`, `Civil Aviation Safety Authority`, `QBE Workers Compensation WA`, `United Protestant Association`, `PSC Capital Insurance Brokers`, `Woolworths Group Limited`, `Kilcoy Global Foods`, `American Express Australia Limited`, `Palios Meegan Nicholson`, `Uniting`, `Coles Group Supply Chain Pty Ltd`, `QBE`, `OBE Organic`, `Cyprium Metals Limited`, `Kincare Health Services Pty Ltd`, `StateCover Mutual Ltd`, `FIRE RESCUE VICTORIA`, `N2N Claims Solutions`, `WesFarmers – Group TeamCover`, `NDIS Quality and Safeguards Commission`, `HD Projects Pty Ltd`, `St Finn Barr's Catholic Primary School - Lanceston`, `Power and Water Corporation`, `EML VIC Pty Ltd`, `Wanton Kearney`, `Kmart Australia Ltd`, `Territory Families – Housing & Communities`, `Calvary Community Care`, `Sedgwick`, `Leonora Contracting P/L`, `NSW Health Pathology`, `Kilcoy Pastoral Company Ltd`, `GIO CTP ACT`, `DXC Claims Management Services - VIC`, `Schindler Lifts Australia Pty Ltd`, `Meridian Lawyers`, `GIO Workers Compensation WA`, `AUB Group Limited`, `Coateshire`, `Aurizon`, `JWLand`, `Trusted Support Coordination`, `Gosford Quarries Pty Ltd`, `GIO NSW Workers Compensation`, `DESE`, `Busways Group`, `Gallagher Bassett Workers Compensation NSW`, `Allianz Australia Insurance Limited C/- McInnes Wilson Lawyers`, `oOh!Media`, `West Gate Tunnel Project`, `KOMATSU MARKETING SUPPORT AUST`, `Mills Oakley Lawyers`, `Hall & Wilcox`, `Skybridge Group Pty Limited`, `Retirement Living Business & Financial Services`, `Allianz Workers Compensation NT`, `Environmental Industries Pty Ltd`, `EML Workers Insurance NSW`, `Department of Agriculture, Water and the Environment`, `MS Australia`, `CSIRO`, `Orange Health Service`, `AHI Insurance`, `Bupa`, `Allianz Australia Workers Compensation (Victoria) Ltd`, `Cappello Civil Contracting Services Pty Ltd`, `LAF Group`, `RTozerconsulting`, `St Michaels College`, `Gallagher Bassett for Opal Healthcare`, `Department of Families, Fairness and Housing`, `WESTHAVEN LIMITED`, `Integrity Care`, `GPC Asia Pacific`, `Department of Primary Industries`, `Mosaic Brands Limited`, `QBE Workers Compensation NT`, `Coredev`, `South Western Sydney Local Health District`, `CGU Workers Compensation ACT`, `Tas Prison Service`, `Sonic Healthcare`, `Workcover C/BT Lawyers`, `PSC WCS`, `CPB Contractors Pty Ltd`, `Cookie Steelfixing and Construction`, `Warner Bros`, `CGU Workers Compensation NT`, `CMET`, `AnglicareSA`, `St Vincent’s Care Services Carseldine`, `Tasmanian Catholic Education Office`, `Allianz Australia Insurance Ltd`, `Roussos Legal Advisory`, `BGIS Technical Services`, `AAMI NSW CTP`, `Wotton Kearney`, `Galllgher Bassett Workers Compensation VIC`, `Brisbane Fire Pty Ltd`, `QBE Workers Compensation NSW`, `Sunshine Coast Hospital and Health Service`, `Oaks Hotels & Resorts Limited - 9004`, `Ausgrid`, `Boral Limited`, `Aerison Pty Ltd`, `Cooper Grace Ward Lawyers`, `Hsswa Pty Ltd`, `Weir Minerals Australia Ltd`, `Labour Force Pty Ltd`, `Barry Nilsson Lawyers`, `Liberty Oil Australia Pty Ltd`, `ABPhillips`, `Austral Risk`, `AAI Limited trading as GIO - Agent for the Workers Compensation Nominal Insurer`, `OCEAN GARDENS INC`, `Roshana Group Pty Ltd`, `GIO CTP NSW`, `Lachlan Shire Council`, `Allianz Workers Compensation WA`, `United Equipment Pty Ltd`, `PFD FOOD SERVICES PTY LTD`, `Phoenix Insurance Brokers`, `Blumers`, `Department of Home Affairs`, `Anglo Coal (Grosvenor Management) Pty Ltd c/- Ashurst Australia`, `Anglicare Southern QLD`, `Lifetime Support`, `The Trustee for The Roshana Family Trust`, `Zurich Australian Insurance Ltd`, `Dept of Education & Training - School Cleaners`, `DXC Claims Management Services`, `The Medical Clinic Millicent`, `Melbourne Water`, `COMPASS GROUP AUSTRALIA PTY LTD`, `Andreasens Green NSW Andreasens Green QLD`, `Astridge and Murray`, `EML Plus`, `Philips Electronics P/L`, `ISS Facility Services Australia Ltd`, `Busy Bees Early Learning Australia Pty Ltd`, `Coates Hire`, `Sydney Trains`, `Catholic Schools Parramatta Diocese Limited`, `CGU Workers Compensation TAS`, `Mercer`, `COFFS HARBOUR SUPPORT SERVICES LTD`, `I-MED GROUP`, `One Path`, `Transport Accident Commission`, `Department of Corporate and Digital Development Northern Territory Government`, `Boral Insurance Pty Limited`, `Department of Justice`, `AB Phillips Pty Ltd`, `Irwin & Hartshorn`, `Pacific Labour Facility`, `Suncorp Staff Pty Ltd`, `Vilis Bakery`, `NRMA`, `The Hospitals Contribution Fund Of Australia Ltd`, `SCE Group`, `Our Lady of Mercy College Parramatta`, `DOSER Freight Forwarding`, `Employers Mutual NSW Limited`, `Cappello Hydraulics & Civil Pty Ltd`, `Buderim Kindergarten`, `ACT Recycling Pty Ltd`, `Bupa Medical Visa Services`, `Allianz CTP SA`, `Auspost`, `Liverpool Plains Shire Council`, `Corporate Services Network Pty Ltd`, `DP World Australia Pty Ltd`, `Complete Personnel Recruitment`, `DXC Integrated Services`, `QBE Workers Compensation - ACT`, `BINGO PTY LTD`, `The Arnott’s Group`, `EML Agent for icare Workers Insurance`, `IHG Irwin Hartshorn Group`, `Civilmart`, `ORAMS Agencies`, `Liberty GFG`, `QBE NSW Treasury Managed Fund`, `EML (NSW Treasury Managed Fund)`, `Hays Recruitment`, `Mosaic Group Ltd Pty`, `BlueCare`, `Gallagher Bassett Services`, `Ernst & Young (EY)`, `Cootharinga North Queensland`, `BUPA AGED CARE AUSTRALIA P/L`, `Toll Self Insurance`, `Corporate Services Network`, `ACT GOV`, `SA Health Northern Adelaide Local Health Network`, `Inghams Enterprises Pty Ltd`, `Centrewest Insurance Brokers`, `Department of Foreign Affairs and Trade (DFAT)`, `RSL Life Care`, `Star of the Sea School`, `Chubb`, `Suncorp CTP QLD`, `JACANA ENERGY`, `Toll Group`, `Corporeal Health`, `Mosaic Brands (Noni B Limited)`, `QBE CTP Insurance`, `Q Super`, `Bartier Perry Lawyers`, `Queensland Government`, `Department of Health and Human Services Tasmania`, `Hall and Wilcox Lawyers`, `Griffin Coal`, `Cappello Commercial Hydraulics and Civil Pty Ltd`, `Bolton Clarke`, `Australian Unity`, `Gallagher Bassett Services Pty Ltd`, `St John Ambulance Western Australia Ltd`, `Geocon Group Pty Ltd`, `Allianz Australia Insurance Limited c/ Jensen McConaghy Lawyers`, `UAA Pty Ltd`, `Tamex Transport Services Pty Ltd`, `WFI Insurance Limited`, `Programmed Skilled Workforce Limited`, `Bartier Perry`, `Australian Competition & Consumer Commission`, `Queensland Health`, `Holcim (Australia) Pty Ltd`, `Southern NSW Local Health District`, `Blue Care`, `Gallagher Bassett Workers Compensation VIC`, `Point Insurance`, `Workers Compensation & Risk Specialists (WCRS) services render for Philips electronics P/L`, `Country Wide Insurance Brokers (CWIB)`, `Allianz Australia Insurance Ltd C/ - Moray and Agnew Lawyers`, `CHUBB AUSTRALASIA`, `Sirius Support & Industrious People`, `BORG MANUFACTURING P/L`, `Department of Climate Change, Energy, the Environment and Water`, `Hireup Pty. Ltd.`, `Workcover QLD`, `Greenham Tasmania `, `Fantastic Furniture Ltd`, `CGU Workers Compensation VIC`, `Lawson Risk Management Services Pty Ltd`, `SGP Civil`, `Moray & Agnew`, `Edwards Michael Lawyers`, `Jensen McConarchy`, `Cyprium Metals`, `Hunter New England Local Health District`, `EML TMF, Insurance for NSW`, `RACQ Insurance`, `Blue Care ATF The Uniting Church in Aust. Property Trust (Q)`, `ENERGYAUSTRALIA SERVICES P/L`, `AAMI CTP`, `Bupa Asia Pacific`, `The Good Shepherd Home`, `Department of Corporate and Digital Development`, `Allianz CTP Claims NSW`, `Sedgwick Australia`, `Racing NSW`, `GCI Group`, `Australia Post`, `Coles Group Limited`, `Minter Ellison`, `MCCOLL'S OPERATIONS P/L`, `Apprenticeship Support Australia`, `AIA Australia Limited`, `Ernst & Young Services Pty Limited`, `North Metropolitan Health Service`, `St Vincent de Paul Society Canberra/Goulburn (Inc)`, `DP WORLD AUSTRALIA FREMANTLE TERMINAL`, `Moray and Agnew`, `Mosaic Group`, `Ovato`, `ACT Formwork Pty Ltd`, `DORMAKABA AUSTRALIA PTY LTD`, `Jones Harley Toole`, `QBE Accident and Health`, `Crawford Legal`, `REA Group Ltd`, `Amadeus IT Pacific Pty Ltd`, `DXC Integrated Services Victoria Pty Ltd`, `Vellex Pty Ltd`, `3M Australia`, `RTC Consulting`, `Somerset College Ltd`, `Bupa Care Services`, `IKEA North Lakes`, `Australian Criminal Intelligence Commission`, `McInnes Wilson Lawyers`, `UnitingCare Queensland `, `Anglican Community Care Incorporated (trading as ac.care)`, `Electrolux Home Products Pty Ltd`, `Gen Leads`, `FUSE RECRUITMENT MELBOURNE P/L`, `Zurich Financial Services Australia Limited`, `Wesfarmers Group TeamCover`, `Connect Infrastructure`, `Oji Fibre Solutions (Aus) Pty Ltd`, `Quality Bakers Australia Pty Limited`, `Workers Compensation & Risk Specialists`, `Civil Aviation Safety Authority (CASA)`, `Endeavour Foundation`, `The Territory Boundless Possible`, `Territory Families – Housing & Communities`, `Ampol Australia Petroleum Pty Ltd`, `Seven Network (Operations) Ltd`, `HopgoodGanim Lawyers`, `Coal Mines Insurance`, `QBE Insurance Australia`, `UGL Limited`, `QBE Accident and Health `, `C.INC`, `Ikea Logan`, `VERO`, `Geodis Australia`, `McCabes Lawyers`, `Programmed`, `UNSW Canberra`, `EML, Agent for ReturnToWorkSA`, `TEST ORG 2. EML Workers Insurance NSW`, `Kings Group`, `Maney Transport`, `South Western Sydney Lhd`, `Force Fire and Safety Pty Ltd`, `Astridge & Murray Solicitors `, `Rankin Ellison Lawyers`, `EML Insurance`, `ACCC/AER`, `Facilities First`, `Turks Legal`, `Jenson McConaghy Lawyers`, `CGU Insurance`, `AAI Limited trading as GIO`, `BP Australia Limited C/ Collin Biggers & Paisley Lawyers`, `O’Neill & Brown Electrical Services Pty Ltd`, `St Kilda PCYC`, `Justice Services Pty Ltd`, `American Express International Inc`, `Gillis Delaney Lawyers`, `Cabra Dominican College Ltd.`, `Trident Services Cleaning Pty Ltd`, `Hicksons Lawyers`, `Healthscope Operations Pty Ltd`, `GSK CX Healthcare Pty Ltd`, `ACT Government`, `AJ Bush & Sons Pty Ltd`, `OMB Solicitors`, `EML Self Insurance`, `Cooper Grace Ward`, `GC Legal`, `Centacare Catholic Family Services`, `Etex Australia Pty Ltd`, `Allianz Australia Ltd`, `Envirolab Service`, `Ikea `, `Allianz Australia Insurance Limited`, `WorkCover Queensland`, `Allianz Workers Compensation ACT`, `GIO Workers Compensation NSW`, `GenesisCare`, `Rocklea Pressed Metal Pty Ltd `, `Australian Digital Health Agency`, `HWL Ebsworth`, `Museum and Art Gallery Northern Territory (MAGNT)`, `CSR`, `Connell`, `4cRisk`, `HBA Legal`, `Coles Supermarkets Australia Pty Ltd`, `The University of Queensland`, `VENTIA SERVICES GROUP P/L,VENT`, `Point Underwriting Agency Pty Ltd`, `Youi CTP SA`, `Allianz Workers Compensation NSW`, `Detmold Packaging Pty Ltd`, `KENNARDS HIRE PTY LTD`, `QBE CTP QLD`, `Insurance House Group`, `Kilcoy Pastoral Company Limited`, `SRG Global Mining (Australia) Pty Ltd`, `Hunter Imaging Group`, `Park Hyatt Melbourne`, `Enviro Lab`, `QBE Australia Insurance Limited`, `EML c/o Moray`, `Catholic Church Insurance Limited`, `NV EMPLOYMENT PTY LTD`, `IP Australia`, `Qantas`, `Wesfarmer Limited`, `Melton City Council`, `Workcover Employer For Special Policies`, `Allianz Australia Workers Compensation (NSW) Ltd.`, `Uniting Care Health`, `Staff Australia Payroll Services Pty Ltd`, `WN Group`, `Infrabuild`, `Western NSW Local Health District`, `APS Group`, `DXC Claims Management Services - VIC`, `GIO`, `Northern Adelaide Local Health Network `, `Austbrokers Canberra`, `Department of Treasury and Finance Northern Territory Government`, `PSC Workers Compensation & Consulting`, `Alinta Energy`, `Sunline ACT Pty Ltd`, `Allianz Australia Workers' Compensation (Victoria)`, `Suncorp`, `JW Land Construction`, `Comcare - VIC`, `IKEA Pty Limited`, `KENNARDS HIRE`, `IRI Worldwide`, `RFI Technology Solutions`, `Engage TSS Internal Resources`, `St Vincent’s Care Services Mitchelton`, `Cappello Concreting Services Pty Ltd`, `Correct Care Australasia P/L`, `Coal Services`, `VELLA TRANSPORT ADMINISTRATION PTY LTD`, `CGU Workers Compensation WA`, `CORPORATE SERVICE NETWORK`, `BGIS`, `SCENTRE LIMITED`, `Employers Mutual Limited`, `RAPE & DOMESTIC VIOLENCE SERVICES AUSTRALIA`, `PSC Insurance`, `Allianz Australia Insurance Ltd ACT`, `Big W`, `Coverforce Pty Ltd`, `AAMI SA CTP Claims`, `EML Workers Insurance`, `Emjay Insurance Brokers`, `EML Victoria`, `WorkSafe Claims and Recovery Support team`, `Adcor`, `Territory Families, Housing and Communities (TFHC)`, `Nazareth Catholic Community`, `Gallagher Bassett Workers Compensation SA`, `INVOCARE AUSTRALIA P/L`, `Hardman Risk Management`, `The Sydney Childrens Hospital Network`, `The Junction Works Limited`, `PEM DEMO`, `Queensland Ambulance Service`, `Fel Child Care Centres 1 Pty Ltd`, `Allianz CTP QLD`, `Moray & Agnew Lawyers`, `Programmed Maintenance Services Ltd (Self Insured)`, `iag`, `Barnardos`, `eReports `, `Youi Pty Ltd`, `HM Focus Pty Ltd`, `Allianz Workers Compensation VIC`, `iCare Workers Insurance`, `Procare Group`, `Kemp & Co Lawyers`, `AAMI Insurance`, `Combined Insurance`, `STAWELL GOLD MINES P/L`, `QBE CTP NSW`, `SA Health`, `Gilshenan & Luton Legal Practice`, `Genesis Care`, `SOUTH AUSTRALIA POLICE`, `Wollongong City Council`, `TUTT BRYANT GROUP LTD`, `Endeavour Energy`, `Tasmanian Health Service`, `IC Formwork Services Pty Ltd`, `Humdrum`, `Comcare`, `The Gowrie (Qld) Inc`, `Australian Government Department of Education, Skills and Employment`, `Gair Legal`, `Dept of Territory Families, Housing and Communities`, `McArthur River Mining PTY Ltd`, `Kincare Management Pty Ltd`, `CFA`, `Department of Territory Families, Housing and Communities Division Library & Archives NT`, `Department for Education and Child Development`, `Core Building Group Pty Ltd`, `ACH Group`, `Busy Bees Australia Operations Pty Ltd.`, `Wesfarmers Ltd`, `JBC Corporate`, `NULL`, `No Employer - ADL`, `BT Lawyers`, `InfraBuild Steel Centre`, `Kimberly-Clark`, `Tas TAFE`, `EML National Self Insurance`, `National Disability Insurance Agency`, `Colin Biggers & Paisley Pty`, `DP World Brisbane Pty Ltd`, `Australian Trade and Investment Commission (Austrade)`, `Allianz Australia Limited c/- McInnes Wilson Lawyers`, `Community Solutions`, `RFI`, `RACQ Insurance Limited ABN 50 009 704 152`, `AAI Limited trading as GIO`, `Gallagher Bassett Services Workers Compensation Vic Pty Ltd`, `Department of Infrastructure, Transport and Regional Development`, `PSC Insurance Group`, `Allianz CTP NSW`, `CSR Limited`, `Kimberly-Clark Australia P/L`, `Hall and Willcox Lawyers`, `Page Seager Lawyers`, `Iconic Hotels Management`, `St John Medical Centre`, `Department of Veterans Affairs`, `Allianz QLD CTP`, `Morgan & Agnew Lawyers`, `Bureau of Meteorology`, `Forest Coach Lines Pty / Ltd`, `Shaw's Darwin Transport Pty Ltd`, `Dynamic Diesel Mechanical Services Pty Ltd`, `Hall & Wilcox Lawyers`, `Moran Aged Care`, `DJarvis@shepelectrical.com.au`, `Gallagher Bassett Self Insurance NSW`, `EML as agent for icare Workers Insurance NSW`, `Minter Ellison Lawyers`, `Lee Legal Group`, `Child and Adolescent Health Service (CAHS)`, `Holman Webb Lawyers`, `Dept of Home Affairs`, `QSuper`, `TIO Motor Accidents Compensation `, `Allianz Australia Workers' Compensation (Victoria) Limited`, `Perpetual Limited`, `Barwang Pty Ltd`, `CTP QLD Claims Division`, `InvoCare`, `Australian Border Force`, `I MED Radiology Network`, `Ensure Pty Ltd`, `CITY OF PALMERSTON`, `AKUBRA HATS PTY LTD`, `Secom Australia`, `GIO Workers Compensation NT`, `Pialligo Estate`, `Berry Buddle Wilkins`, `Department of Infrastructure, Transport, Regional Development and Communications`, `Aussie Skip Bins Services P/L`, `BGIS Pty Ltd`, `NSW Police Force`, `GIO Workers Compensation TAS`, `Eighteen33 Pty Ltd`, `Crown Law`, `Paramatta Council`, `Northern Territory Government`, `Australian Electoral Commission`, `Department of Health`, `Hunt & Hunt Lawyers`, `Batemans Bay Soldiers Club`, `Allianz Workers Compensation Tasmania`, `SMK Lawyers`, `Envirolab Group`, `WorkSafe Victoria`, `Allianz Australia Insurance Limited, c/- Moray & Agnew`, `Allianz Australia Insurance Limited ABN 15 000 122 850, c/- Moray & Agnew`, `City of Parramatta`, `UES International Pty Ltd`, `Westpac Group`, `Logistics & Stores (Mailroom, Stores & Transport) Services CHW`, `Device Technologies Australia Pty Ltd`, `Willis Towers Watson`, `Hsswa Pty Ltd & HSS Resources Pty Ltd & Other`, `Kingspan Water & Energy Pty Limited`, `SAPOL`, `Guild Insurance`, `Westpac Banking Group`, `St Hilarion Aged Care`, `AAI Limited trading as GIO - Agent for the Workers Compensation Nominal Insurer ABN 83 564 379 108`, `Roshana Pty Ltd`, `QBE Insurance (Australia) Limited (ABN 78003191035)`, `Service Australia`, `BOC Limited `, `HWLE Lawyers`, `NRMA CTP NSW`, `RACQ Insurance Limited ABN 50009704152/ C- Cooper Grace Ward`, `CALVARY ADMINISTRATION PTY LTD`, `Cappello Group`, `Wesfarmers Limited`, `GIO NSW CTP `, `FK Gardner Services (Qld) Pty Ltd`, `Challenge Implements Holdings`, `Bartier Perry Pty Limited`, `Chubb Insurance Australia Limited`, `EMP Michael Lawyers`, `I-MED RADIOLOGY NETWORK LIMITED`, `Gilchrist Connell Legal`, `Premier Office Relocations`, `Nominal Defendant c/- Jensen McConaghy Lawyers`, `Detmold Mental Health Training`, `EML`, `Premise`, `Balance Rehab`, `Xchanging Workers Compensation - NSW`, `Coogee Chemicals Pty Ltd`, `Safe Work Australia`, `Jensen McConaghy Lawyers`, `Hawkesbury City Council`, `Toll Global Express`, `The Corporation of the Synod of the Diocese of Brisbane`, `NRMA CTP SA`, `Ambulance Victoria`, `APSystems`, `Austbrokers (Finsura)`, `SCENTRE GROUP`, `Ikea Australia`, `Department of Treasury and Finance`, `Gallagher Bassett Services Workers Compensation NSW`, `NONI B HOLDINGS PTY LIMITED`, `QBE Workers Compensation SA`, `The Star Entertainment Group Self Insurance Unit`, `Catholic Care Diocese of Bathurst`, `GAIR LEGAL PTY LIMITED`, `QBE CTP SA`, `Wesfarmers Group`, `Rod Pilon Transport`, `TG Legal`, `Department of the Prime Minister and Cabinet`, `UNSW`, `RACQ Group`, `REMONDIS Australia Pty Ltd`, `Australian Federal Police`, `Marshall & Brougham Constructions `, `Chandler Macleod Group`, `University of Tasmania`, `Goodman Fielder Pty Limited`, `SONIC HEALTHCARE GROUP`, `Hastings Medical Centre`, `Hospitality Employers Mutual`, `HCF`, `Colin Biggers Paisley Lawyers`, `Department Veterans Affairs`, `Maddocks Lawyers`, `SRG Group`, `Australian Personnel Solutions (APS Group)`, `EY Business Solutions Pty Ltd`, `National Indigenous Australians Agency`, `St Catherine's School, Berwick`, `Transport for NSW`, `South Australian Native Titles Services` | </details> ### Accuracy | Type | Score | | --- | --- | | `CATS_SCORE` | 32.28 | | `CATS_MICRO_P` | 71.89 | | `CATS_MICRO_R` | 23.49 | | `CATS_MICRO_F` | 35.41 | | `CATS_MACRO_P` | 7.06 | | `CATS_MACRO_R` | 3.40 | | `CATS_MACRO_F` | 4.32 | | `CATS_MACRO_AUC` | 32.28 | | `TEXTCAT_MULTILABEL_LOSS` | 7.88 |

hemlataC/llama-2-7b-hindie2-v4

hemlataC

2023-08-25T02:04:02Z

0

peft

[ "peft", "region:us" ]

null

2023-08-25T02:02:53Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0

Bugsys0302/CharactersLoRA

Bugsys0302

2023-08-25T01:54:36Z

0

null

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

null

2023-08-25T01:54:36Z

--- license: creativeml-openrail-m ---

Vasanth/idefics-mscoco-captioner

Vasanth

2023-08-25T01:49:11Z

0

peft

[ "peft", "region:us" ]

null

2023-08-25T01:49:08Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: ['lm_head', 'embed_tokens'] - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0

jimmyofdoom/rl_course_vizdoom_health_gathering_supreme

jimmyofdoom

2023-08-25T01:47:33Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-08-25T01:47:25Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.17 +/- 5.17 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r jimmyofdoom/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

AdanLee/q-Taxi-v3

AdanLee

2023-08-25T01:41:15Z

0

null

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

reinforcement-learning

2023-08-25T01:30:27Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.70 name: mean_reward verified: false --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ``` from urllib.error import HTTPError from huggingface_hub import hf_hub_download def load_from_hub(repo_id: str, filename: str) -> str: """ Download a model from Hugging Face Hub. :param repo_id: id of the model repository from the Hugging Face Hub :param filename: name of the model zip file from the repository """ # Get the model from the Hub, download and cache the model on your local disk pickle_model = hf_hub_download(repo_id=repo_id, filename=filename) with open(pickle_model, "rb") as f: downloaded_model_file = pickle.load(f) return downloaded_model_file model = load_from_hub(repo_id="AdanLee/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"]) ```

dt-and-vanilla-ardt/ardt-vanilla-arrl_train_halfcheetah_high-2508_0016-66

dt-and-vanilla-ardt

2023-08-25T01:27:59Z

32

0

transformers

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

null

2023-08-24T23:17:55Z

--- tags: - generated_from_trainer model-index: - name: ardt-vanilla-arrl_train_halfcheetah_high-2508_0016-66 results: [] ---  # ardt-vanilla-arrl_train_halfcheetah_high-2508_0016-66 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

Unmand/procare_referrer_organisation

Unmand

2023-08-25T00:49:54Z

2

1

spacy

[ "spacy", "text-classification", "en", "region:us" ]

text-classification

2023-07-11T00:57:04Z

--- tags: - spacy - text-classification language: - en model-index: - name: en_procare_referrer_organisation results: [] --- | Feature | Description | | --- | --- | | **Name** | `en_procare_referrer_organisation` | | **Version** | `0.0.0` | | **spaCy** | `>=3.5.4,<3.6.0` | | **Default Pipeline** | `textcat_multilabel` | | **Components** | `textcat_multilabel` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | n/a | | **License** | n/a | | **Author** | [n/a]() | ### Label Scheme <details> <summary>View label scheme (726 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`textcat_multilabel`** | `H D PROJECTS PTY LTD`, `McCabe Curwood`, `Dept of Education, Skills & Employment`, `StateCover Mutual Limited`, `Perth Orthopaedic & Sports Medicine`, `Queensland Child Care Service Pty Ltd Ttee`, `Allianz Australia Insurance Limited c/- Jensen McConaghy Lawyers`, `Catholic Care Diocese of Broken Bay`, `Helping Hand New Aged Care`, `Suncorp Life`, `Qantas Airways Limited`, `Department of Defence`, `Master Builders Association of SA`, `HWL Ebsworth Lawyers`, `Alexander Watson`, `Zoetis`, `RSL Care`, `P&N Bank`, `University of NSW`, `Uber Technologies, Inc.`, `Finlay Plumbing Services Pty Ltd`, `Hays Specialist Recruitment`, `KENNARDS HIRE PTY LIMITED`, `Carer Solutions Australia`, `Unitingcare`, `No. 1 Riverside Quay Proprietary Limited`, `Gallagher Basset`, `Department of the Chief MInister and Cabinet`, `CHEP Australia`, `Minda Incorporated`, `The Star`, `Tas Water`, `Feros Care`, `Roshana Group`, `Atradius Crédito y Caución S.A de Seguros y Reaseguros`, `Services Australia`, `RT Consulting`, `The Australian Electoral Commission`, `Federal Court of Australia`, `NRMA INSURANCE`, `Catholic Education Office`, `Svitzer Australia Pty Ltd`, `QBE acting as the agent of NSW Self Insurance Corporation`, `LAWRENCE & HANSON`, `UnitingCare Queensland`, `LibertyGFG`, `Australian Tax Office`, `Alvaro Transport Pty Ltd`, `GIO Workers Compensation ACT`, `Cso Diocese Of Broken Bay`, `Glencore`, `EASTERN HOSPITAL`, `BOC Limited, a member of the Linde Group`, `INVOCARE AUSTRALIA PTY LIMITED`, `UNITRANS ASIA PACIFIC PTY LTD`, `Services Australia (Dept of Human Services)`, `VEOLIA ENVIRONMENTAL SERVICES (AUSTRALIA) PTY LTD `, `Vickilynn Pty Ltd`, `Coles Team Cover`, `MLC Life Insurance`, `Sparke Helmore Lawyers`, `RSL Lifecare Limited`, `QBE Workers Compensation TAS`, `Kimberley Clark Australia`, `The Personnel Group Ltd`, `Insurance Australia Group`, `Canberra Sand & Gravel`, `Viva Energy Australia Pty Ltd`, `Moran Aged Care Engadine`, `Australian Taxation Office`, `Youis Group Pty Ltd`, `Cleanaway`, `Mosaic Brands (Rockmans)`, `Children Hospital Foundation`, `Civil Aviation Safety Authority`, `QBE Workers Compensation WA`, `United Protestant Association`, `PSC Capital Insurance Brokers`, `Woolworths Group Limited`, `Kilcoy Global Foods`, `American Express Australia Limited`, `Palios Meegan Nicholson`, `Uniting`, `Coles Group Supply Chain Pty Ltd`, `QBE`, `OBE Organic`, `Cyprium Metals Limited`, `Kincare Health Services Pty Ltd`, `StateCover Mutual Ltd`, `FIRE RESCUE VICTORIA`, `N2N Claims Solutions`, `WesFarmers – Group TeamCover`, `NDIS Quality and Safeguards Commission`, `HD Projects Pty Ltd`, `St Finn Barr's Catholic Primary School - Lanceston`, `Power and Water Corporation`, `EML VIC Pty Ltd`, `Wanton Kearney`, `Kmart Australia Ltd`, `Territory Families – Housing & Communities`, `Calvary Community Care`, `Sedgwick`, `Leonora Contracting P/L`, `NSW Health Pathology`, `Kilcoy Pastoral Company Ltd`, `GIO CTP ACT`, `DXC Claims Management Services - VIC`, `Schindler Lifts Australia Pty Ltd`, `Meridian Lawyers`, `GIO Workers Compensation WA`, `AUB Group Limited`, `Coateshire`, `Aurizon`, `JWLand`, `Trusted Support Coordination`, `Gosford Quarries Pty Ltd`, `GIO NSW Workers Compensation`, `DESE`, `Busways Group`, `Gallagher Bassett Workers Compensation NSW`, `Allianz Australia Insurance Limited C/- McInnes Wilson Lawyers`, `oOh!Media`, `West Gate Tunnel Project`, `KOMATSU MARKETING SUPPORT AUST`, `Mills Oakley Lawyers`, `Hall & Wilcox`, `Skybridge Group Pty Limited`, `Retirement Living Business & Financial Services`, `Allianz Workers Compensation NT`, `Environmental Industries Pty Ltd`, `EML Workers Insurance NSW`, `Department of Agriculture, Water and the Environment`, `MS Australia`, `CSIRO`, `Orange Health Service`, `AHI Insurance`, `Bupa`, `Allianz Australia Workers Compensation (Victoria) Ltd`, `Cappello Civil Contracting Services Pty Ltd`, `LAF Group`, `RTozerconsulting`, `St Michaels College`, `Gallagher Bassett for Opal Healthcare`, `Department of Families, Fairness and Housing`, `WESTHAVEN LIMITED`, `Integrity Care`, `GPC Asia Pacific`, `Department of Primary Industries`, `Mosaic Brands Limited`, `QBE Workers Compensation NT`, `Coredev`, `South Western Sydney Local Health District`, `CGU Workers Compensation ACT`, `Tas Prison Service`, `Sonic Healthcare`, `Workcover C/BT Lawyers`, `PSC WCS`, `CPB Contractors Pty Ltd`, `Cookie Steelfixing and Construction`, `Warner Bros`, `CGU Workers Compensation NT`, `CMET`, `AnglicareSA`, `St Vincent’s Care Services Carseldine`, `Tasmanian Catholic Education Office`, `Allianz Australia Insurance Ltd`, `Roussos Legal Advisory`, `BGIS Technical Services`, `AAMI NSW CTP`, `Wotton Kearney`, `Galllgher Bassett Workers Compensation VIC`, `Brisbane Fire Pty Ltd`, `QBE Workers Compensation NSW`, `Sunshine Coast Hospital and Health Service`, `Oaks Hotels & Resorts Limited - 9004`, `Ausgrid`, `Boral Limited`, `Aerison Pty Ltd`, `Cooper Grace Ward Lawyers`, `Hsswa Pty Ltd`, `Weir Minerals Australia Ltd`, `Labour Force Pty Ltd`, `Barry Nilsson Lawyers`, `Liberty Oil Australia Pty Ltd`, `ABPhillips`, `Austral Risk`, `AAI Limited trading as GIO - Agent for the Workers Compensation Nominal Insurer`, `OCEAN GARDENS INC`, `Roshana Group Pty Ltd`, `GIO CTP NSW`, `Lachlan Shire Council`, `Allianz Workers Compensation WA`, `United Equipment Pty Ltd`, `PFD FOOD SERVICES PTY LTD`, `Phoenix Insurance Brokers`, `Blumers`, `Department of Home Affairs`, `Anglo Coal (Grosvenor Management) Pty Ltd c/- Ashurst Australia`, `Anglicare Southern QLD`, `Lifetime Support`, `The Trustee for The Roshana Family Trust`, `Zurich Australian Insurance Ltd`, `Dept of Education & Training - School Cleaners`, `DXC Claims Management Services`, `The Medical Clinic Millicent`, `Melbourne Water`, `COMPASS GROUP AUSTRALIA PTY LTD`, `Andreasens Green NSW Andreasens Green QLD`, `Astridge and Murray`, `EML Plus`, `Philips Electronics P/L`, `ISS Facility Services Australia Ltd`, `Busy Bees Early Learning Australia Pty Ltd`, `Coates Hire`, `Sydney Trains`, `Catholic Schools Parramatta Diocese Limited`, `CGU Workers Compensation TAS`, `Mercer`, `COFFS HARBOUR SUPPORT SERVICES LTD`, `I-MED GROUP`, `One Path`, `Transport Accident Commission`, `Department of Corporate and Digital Development Northern Territory Government`, `Boral Insurance Pty Limited`, `Department of Justice`, `AB Phillips Pty Ltd`, `Irwin & Hartshorn`, `Pacific Labour Facility`, `Suncorp Staff Pty Ltd`, `Vilis Bakery`, `NRMA`, `The Hospitals Contribution Fund Of Australia Ltd`, `SCE Group`, `Our Lady of Mercy College Parramatta`, `DOSER Freight Forwarding`, `Employers Mutual NSW Limited`, `Cappello Hydraulics & Civil Pty Ltd`, `Buderim Kindergarten`, `ACT Recycling Pty Ltd`, `Bupa Medical Visa Services`, `Allianz CTP SA`, `Auspost`, `Liverpool Plains Shire Council`, `Corporate Services Network Pty Ltd`, `DP World Australia Pty Ltd`, `Complete Personnel Recruitment`, `DXC Integrated Services`, `QBE Workers Compensation - ACT`, `BINGO PTY LTD`, `The Arnott’s Group`, `EML Agent for icare Workers Insurance`, `IHG Irwin Hartshorn Group`, `Civilmart`, `ORAMS Agencies`, `Liberty GFG`, `QBE NSW Treasury Managed Fund`, `EML (NSW Treasury Managed Fund)`, `Hays Recruitment`, `Mosaic Group Ltd Pty`, `BlueCare`, `Gallagher Bassett Services`, `Ernst & Young (EY)`, `Cootharinga North Queensland`, `BUPA AGED CARE AUSTRALIA P/L`, `Toll Self Insurance`, `Corporate Services Network`, `ACT GOV`, `SA Health Northern Adelaide Local Health Network`, `Inghams Enterprises Pty Ltd`, `Centrewest Insurance Brokers`, `Department of Foreign Affairs and Trade (DFAT)`, `RSL Life Care`, `Star of the Sea School`, `Chubb`, `Suncorp CTP QLD`, `JACANA ENERGY`, `Toll Group`, `Corporeal Health`, `Mosaic Brands (Noni B Limited)`, `QBE CTP Insurance`, `Q Super`, `Bartier Perry Lawyers`, `Queensland Government`, `Department of Health and Human Services Tasmania`, `Hall and Wilcox Lawyers`, `Griffin Coal`, `Cappello Commercial Hydraulics and Civil Pty Ltd`, `Bolton Clarke`, `Australian Unity`, `Gallagher Bassett Services Pty Ltd`, `St John Ambulance Western Australia Ltd`, `Geocon Group Pty Ltd`, `Allianz Australia Insurance Limited c/ Jensen McConaghy Lawyers`, `UAA Pty Ltd`, `Tamex Transport Services Pty Ltd`, `WFI Insurance Limited`, `Programmed Skilled Workforce Limited`, `Bartier Perry`, `Australian Competition & Consumer Commission`, `Queensland Health`, `Holcim (Australia) Pty Ltd`, `Southern NSW Local Health District`, `Blue Care`, `Gallagher Bassett Workers Compensation VIC`, `Point Insurance`, `Workers Compensation & Risk Specialists (WCRS) services render for Philips electronics P/L`, `Country Wide Insurance Brokers (CWIB)`, `Allianz Australia Insurance Ltd C/ - Moray and Agnew Lawyers`, `CHUBB AUSTRALASIA`, `Sirius Support & Industrious People`, `BORG MANUFACTURING P/L`, `Department of Climate Change, Energy, the Environment and Water`, `Hireup Pty. Ltd.`, `Workcover QLD`, `Greenham Tasmania `, `Fantastic Furniture Ltd`, `CGU Workers Compensation VIC`, `Lawson Risk Management Services Pty Ltd`, `SGP Civil`, `Moray & Agnew`, `Edwards Michael Lawyers`, `Jensen McConarchy`, `Cyprium Metals`, `Hunter New England Local Health District`, `EML TMF, Insurance for NSW`, `RACQ Insurance`, `Blue Care ATF The Uniting Church in Aust. Property Trust (Q)`, `ENERGYAUSTRALIA SERVICES P/L`, `AAMI CTP`, `Bupa Asia Pacific`, `The Good Shepherd Home`, `Department of Corporate and Digital Development`, `Allianz CTP Claims NSW`, `Sedgwick Australia`, `Racing NSW`, `GCI Group`, `Australia Post`, `Coles Group Limited`, `Minter Ellison`, `MCCOLL'S OPERATIONS P/L`, `Apprenticeship Support Australia`, `AIA Australia Limited`, `Ernst & Young Services Pty Limited`, `North Metropolitan Health Service`, `St Vincent de Paul Society Canberra/Goulburn (Inc)`, `DP WORLD AUSTRALIA FREMANTLE TERMINAL`, `Moray and Agnew`, `Mosaic Group`, `Ovato`, `ACT Formwork Pty Ltd`, `DORMAKABA AUSTRALIA PTY LTD`, `Jones Harley Toole`, `QBE Accident and Health`, `Crawford Legal`, `REA Group Ltd`, `Amadeus IT Pacific Pty Ltd`, `DXC Integrated Services Victoria Pty Ltd`, `Vellex Pty Ltd`, `3M Australia`, `RTC Consulting`, `Somerset College Ltd`, `Bupa Care Services`, `IKEA North Lakes`, `Australian Criminal Intelligence Commission`, `McInnes Wilson Lawyers`, `UnitingCare Queensland `, `Anglican Community Care Incorporated (trading as ac.care)`, `Electrolux Home Products Pty Ltd`, `Gen Leads`, `FUSE RECRUITMENT MELBOURNE P/L`, `Zurich Financial Services Australia Limited`, `Wesfarmers Group TeamCover`, `Connect Infrastructure`, `Oji Fibre Solutions (Aus) Pty Ltd`, `Quality Bakers Australia Pty Limited`, `Workers Compensation & Risk Specialists`, `Civil Aviation Safety Authority (CASA)`, `Endeavour Foundation`, `The Territory Boundless Possible`, `Territory Families – Housing & Communities`, `Ampol Australia Petroleum Pty Ltd`, `Seven Network (Operations) Ltd`, `HopgoodGanim Lawyers`, `Coal Mines Insurance`, `QBE Insurance Australia`, `UGL Limited`, `QBE Accident and Health `, `C.INC`, `Ikea Logan`, `VERO`, `Geodis Australia`, `McCabes Lawyers`, `Programmed`, `UNSW Canberra`, `EML, Agent for ReturnToWorkSA`, `TEST ORG 2. EML Workers Insurance NSW`, `Kings Group`, `Maney Transport`, `South Western Sydney Lhd`, `Force Fire and Safety Pty Ltd`, `Astridge & Murray Solicitors `, `Rankin Ellison Lawyers`, `EML Insurance`, `ACCC/AER`, `Facilities First`, `Turks Legal`, `Jenson McConaghy Lawyers`, `CGU Insurance`, `AAI Limited trading as GIO`, `BP Australia Limited C/ Collin Biggers & Paisley Lawyers`, `O’Neill & Brown Electrical Services Pty Ltd`, `St Kilda PCYC`, `Justice Services Pty Ltd`, `American Express International Inc`, `Gillis Delaney Lawyers`, `Cabra Dominican College Ltd.`, `Trident Services Cleaning Pty Ltd`, `Hicksons Lawyers`, `Healthscope Operations Pty Ltd`, `GSK CX Healthcare Pty Ltd`, `ACT Government`, `AJ Bush & Sons Pty Ltd`, `OMB Solicitors`, `EML Self Insurance`, `Cooper Grace Ward`, `GC Legal`, `Centacare Catholic Family Services`, `Etex Australia Pty Ltd`, `Allianz Australia Ltd`, `Envirolab Service`, `Ikea `, `Allianz Australia Insurance Limited`, `WorkCover Queensland`, `Allianz Workers Compensation ACT`, `GIO Workers Compensation NSW`, `GenesisCare`, `Rocklea Pressed Metal Pty Ltd `, `Australian Digital Health Agency`, `HWL Ebsworth`, `Museum and Art Gallery Northern Territory (MAGNT)`, `CSR`, `Connell`, `4cRisk`, `HBA Legal`, `Coles Supermarkets Australia Pty Ltd`, `The University of Queensland`, `VENTIA SERVICES GROUP P/L,VENT`, `Point Underwriting Agency Pty Ltd`, `Youi CTP SA`, `Allianz Workers Compensation NSW`, `Detmold Packaging Pty Ltd`, `KENNARDS HIRE PTY LTD`, `QBE CTP QLD`, `Insurance House Group`, `Kilcoy Pastoral Company Limited`, `SRG Global Mining (Australia) Pty Ltd`, `Hunter Imaging Group`, `Park Hyatt Melbourne`, `Enviro Lab`, `QBE Australia Insurance Limited`, `EML c/o Moray`, `Catholic Church Insurance Limited`, `NV EMPLOYMENT PTY LTD`, `IP Australia`, `Qantas`, `Wesfarmer Limited`, `Melton City Council`, `Workcover Employer For Special Policies`, `Allianz Australia Workers Compensation (NSW) Ltd.`, `Uniting Care Health`, `Staff Australia Payroll Services Pty Ltd`, `WN Group`, `Infrabuild`, `Western NSW Local Health District`, `APS Group`, `DXC Claims Management Services - VIC`, `GIO`, `Northern Adelaide Local Health Network `, `Austbrokers Canberra`, `Department of Treasury and Finance Northern Territory Government`, `PSC Workers Compensation & Consulting`, `Alinta Energy`, `Sunline ACT Pty Ltd`, `Allianz Australia Workers' Compensation (Victoria)`, `Suncorp`, `JW Land Construction`, `Comcare - VIC`, `IKEA Pty Limited`, `KENNARDS HIRE`, `IRI Worldwide`, `RFI Technology Solutions`, `Engage TSS Internal Resources`, `St Vincent’s Care Services Mitchelton`, `Cappello Concreting Services Pty Ltd`, `Correct Care Australasia P/L`, `Coal Services`, `VELLA TRANSPORT ADMINISTRATION PTY LTD`, `CGU Workers Compensation WA`, `CORPORATE SERVICE NETWORK`, `BGIS`, `SCENTRE LIMITED`, `Employers Mutual Limited`, `RAPE & DOMESTIC VIOLENCE SERVICES AUSTRALIA`, `PSC Insurance`, `Allianz Australia Insurance Ltd ACT`, `Big W`, `Coverforce Pty Ltd`, `AAMI SA CTP Claims`, `EML Workers Insurance`, `Emjay Insurance Brokers`, `EML Victoria`, `WorkSafe Claims and Recovery Support team`, `Adcor`, `Territory Families, Housing and Communities (TFHC)`, `Nazareth Catholic Community`, `Gallagher Bassett Workers Compensation SA`, `INVOCARE AUSTRALIA P/L`, `Hardman Risk Management`, `The Sydney Childrens Hospital Network`, `The Junction Works Limited`, `PEM DEMO`, `Queensland Ambulance Service`, `Fel Child Care Centres 1 Pty Ltd`, `Allianz CTP QLD`, `Moray & Agnew Lawyers`, `Programmed Maintenance Services Ltd (Self Insured)`, `iag`, `Barnardos`, `eReports `, `Youi Pty Ltd`, `HM Focus Pty Ltd`, `Allianz Workers Compensation VIC`, `iCare Workers Insurance`, `Procare Group`, `Kemp & Co Lawyers`, `AAMI Insurance`, `Combined Insurance`, `STAWELL GOLD MINES P/L`, `QBE CTP NSW`, `SA Health`, `Gilshenan & Luton Legal Practice`, `Genesis Care`, `SOUTH AUSTRALIA POLICE`, `Wollongong City Council`, `TUTT BRYANT GROUP LTD`, `Endeavour Energy`, `Tasmanian Health Service`, `IC Formwork Services Pty Ltd`, `Humdrum`, `Comcare`, `The Gowrie (Qld) Inc`, `Australian Government Department of Education, Skills and Employment`, `Gair Legal`, `Dept of Territory Families, Housing and Communities`, `McArthur River Mining PTY Ltd`, `Kincare Management Pty Ltd`, `CFA`, `Department of Territory Families, Housing and Communities Division Library & Archives NT`, `Department for Education and Child Development`, `Core Building Group Pty Ltd`, `ACH Group`, `Busy Bees Australia Operations Pty Ltd.`, `Wesfarmers Ltd`, `JBC Corporate`, `NULL`, `No Employer - ADL`, `BT Lawyers`, `InfraBuild Steel Centre`, `Kimberly-Clark`, `Tas TAFE`, `EML National Self Insurance`, `National Disability Insurance Agency`, `Colin Biggers & Paisley Pty`, `DP World Brisbane Pty Ltd`, `Australian Trade and Investment Commission (Austrade)`, `Allianz Australia Limited c/- McInnes Wilson Lawyers`, `Community Solutions`, `RFI`, `RACQ Insurance Limited ABN 50 009 704 152`, `AAI Limited trading as GIO`, `Gallagher Bassett Services Workers Compensation Vic Pty Ltd`, `Department of Infrastructure, Transport and Regional Development`, `PSC Insurance Group`, `Allianz CTP NSW`, `CSR Limited`, `Kimberly-Clark Australia P/L`, `Hall and Willcox Lawyers`, `Page Seager Lawyers`, `Iconic Hotels Management`, `St John Medical Centre`, `Department of Veterans Affairs`, `Allianz QLD CTP`, `Morgan & Agnew Lawyers`, `Bureau of Meteorology`, `Forest Coach Lines Pty / Ltd`, `Shaw's Darwin Transport Pty Ltd`, `Dynamic Diesel Mechanical Services Pty Ltd`, `Hall & Wilcox Lawyers`, `Moran Aged Care`, `DJarvis@shepelectrical.com.au`, `Gallagher Bassett Self Insurance NSW`, `EML as agent for icare Workers Insurance NSW`, `Minter Ellison Lawyers`, `Lee Legal Group`, `Child and Adolescent Health Service (CAHS)`, `Holman Webb Lawyers`, `Dept of Home Affairs`, `QSuper`, `TIO Motor Accidents Compensation `, `Allianz Australia Workers' Compensation (Victoria) Limited`, `Perpetual Limited`, `Barwang Pty Ltd`, `CTP QLD Claims Division`, `InvoCare`, `Australian Border Force`, `I MED Radiology Network`, `Ensure Pty Ltd`, `CITY OF PALMERSTON`, `AKUBRA HATS PTY LTD`, `Secom Australia`, `GIO Workers Compensation NT`, `Pialligo Estate`, `Berry Buddle Wilkins`, `Department of Infrastructure, Transport, Regional Development and Communications`, `Aussie Skip Bins Services P/L`, `BGIS Pty Ltd`, `NSW Police Force`, `GIO Workers Compensation TAS`, `Eighteen33 Pty Ltd`, `Crown Law`, `Paramatta Council`, `Northern Territory Government`, `Australian Electoral Commission`, `Department of Health`, `Hunt & Hunt Lawyers`, `Batemans Bay Soldiers Club`, `Allianz Workers Compensation Tasmania`, `SMK Lawyers`, `Envirolab Group`, `WorkSafe Victoria`, `Allianz Australia Insurance Limited, c/- Moray & Agnew`, `Allianz Australia Insurance Limited ABN 15 000 122 850, c/- Moray & Agnew`, `City of Parramatta`, `UES International Pty Ltd`, `Westpac Group`, `Logistics & Stores (Mailroom, Stores & Transport) Services CHW`, `Device Technologies Australia Pty Ltd`, `Willis Towers Watson`, `Hsswa Pty Ltd & HSS Resources Pty Ltd & Other`, `Kingspan Water & Energy Pty Limited`, `SAPOL`, `Guild Insurance`, `Westpac Banking Group`, `St Hilarion Aged Care`, `AAI Limited trading as GIO - Agent for the Workers Compensation Nominal Insurer ABN 83 564 379 108`, `Roshana Pty Ltd`, `QBE Insurance (Australia) Limited (ABN 78003191035)`, `Service Australia`, `BOC Limited `, `HWLE Lawyers`, `NRMA CTP NSW`, `RACQ Insurance Limited ABN 50009704152/ C- Cooper Grace Ward`, `CALVARY ADMINISTRATION PTY LTD`, `Cappello Group`, `Wesfarmers Limited`, `GIO NSW CTP `, `FK Gardner Services (Qld) Pty Ltd`, `Challenge Implements Holdings`, `Bartier Perry Pty Limited`, `Chubb Insurance Australia Limited`, `EMP Michael Lawyers`, `I-MED RADIOLOGY NETWORK LIMITED`, `Gilchrist Connell Legal`, `Premier Office Relocations`, `Nominal Defendant c/- Jensen McConaghy Lawyers`, `Detmold Mental Health Training`, `EML`, `Premise`, `Balance Rehab`, `Xchanging Workers Compensation - NSW`, `Coogee Chemicals Pty Ltd`, `Safe Work Australia`, `Jensen McConaghy Lawyers`, `Hawkesbury City Council`, `Toll Global Express`, `The Corporation of the Synod of the Diocese of Brisbane`, `NRMA CTP SA`, `Ambulance Victoria`, `APSystems`, `Austbrokers (Finsura)`, `SCENTRE GROUP`, `Ikea Australia`, `Department of Treasury and Finance`, `Gallagher Bassett Services Workers Compensation NSW`, `NONI B HOLDINGS PTY LIMITED`, `QBE Workers Compensation SA`, `The Star Entertainment Group Self Insurance Unit`, `Catholic Care Diocese of Bathurst`, `GAIR LEGAL PTY LIMITED`, `QBE CTP SA`, `Wesfarmers Group`, `Rod Pilon Transport`, `TG Legal`, `Department of the Prime Minister and Cabinet`, `UNSW`, `RACQ Group`, `REMONDIS Australia Pty Ltd`, `Australian Federal Police`, `Marshall & Brougham Constructions `, `Chandler Macleod Group`, `University of Tasmania`, `Goodman Fielder Pty Limited`, `SONIC HEALTHCARE GROUP`, `Hastings Medical Centre`, `Hospitality Employers Mutual`, `HCF`, `Colin Biggers Paisley Lawyers`, `Department Veterans Affairs`, `Maddocks Lawyers`, `SRG Group`, `Australian Personnel Solutions (APS Group)`, `EY Business Solutions Pty Ltd`, `National Indigenous Australians Agency`, `St Catherine's School, Berwick`, `Transport for NSW`, `South Australian Native Titles Services` | </details> ### Accuracy | Type | Score | | --- | --- | | `CATS_SCORE` | 32.28 | | `CATS_MICRO_P` | 71.89 | | `CATS_MICRO_R` | 23.49 | | `CATS_MICRO_F` | 35.41 | | `CATS_MACRO_P` | 7.06 | | `CATS_MACRO_R` | 3.40 | | `CATS_MACRO_F` | 4.32 | | `CATS_MACRO_AUC` | 32.28 | | `TEXTCAT_MULTILABEL_LOSS` | 7.88 |

dkqjrm/20230825071702

dkqjrm

2023-08-25T00:32:53Z

19

0

transformers

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

text-classification

2023-08-24T22:17:21Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230825071702' results: [] ---  # 20230825071702 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2804 - Accuracy: 0.7617 ## 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: 0.005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 80.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | No log | 1.0 | 156 | 0.6793 | 0.5307 | | No log | 2.0 | 312 | 0.9039 | 0.4765 | | No log | 3.0 | 468 | 0.7107 | 0.4729 | | 0.8982 | 4.0 | 624 | 0.6969 | 0.5199 | | 0.8982 | 5.0 | 780 | 0.5729 | 0.5560 | | 0.8982 | 6.0 | 936 | 0.6447 | 0.5596 | | 0.8495 | 7.0 | 1092 | 0.6093 | 0.5921 | | 0.8495 | 8.0 | 1248 | 0.4289 | 0.6679 | | 0.8495 | 9.0 | 1404 | 0.4954 | 0.6282 | | 0.751 | 10.0 | 1560 | 0.3952 | 0.6715 | | 0.751 | 11.0 | 1716 | 0.6147 | 0.6462 | | 0.751 | 12.0 | 1872 | 0.4183 | 0.7004 | | 0.6407 | 13.0 | 2028 | 0.3743 | 0.6968 | | 0.6407 | 14.0 | 2184 | 0.3907 | 0.7292 | | 0.6407 | 15.0 | 2340 | 0.3409 | 0.7148 | | 0.6407 | 16.0 | 2496 | 0.5288 | 0.6426 | | 0.6476 | 17.0 | 2652 | 0.4492 | 0.7220 | | 0.6476 | 18.0 | 2808 | 0.3312 | 0.7220 | | 0.6476 | 19.0 | 2964 | 0.4062 | 0.6606 | | 0.6425 | 20.0 | 3120 | 0.3715 | 0.6859 | | 0.6425 | 21.0 | 3276 | 0.3305 | 0.7256 | | 0.6425 | 22.0 | 3432 | 0.6557 | 0.6245 | | 0.5658 | 23.0 | 3588 | 0.3943 | 0.6859 | | 0.5658 | 24.0 | 3744 | 0.3394 | 0.7040 | | 0.5658 | 25.0 | 3900 | 0.4640 | 0.6823 | | 0.5333 | 26.0 | 4056 | 0.3419 | 0.7220 | | 0.5333 | 27.0 | 4212 | 0.3646 | 0.7112 | | 0.5333 | 28.0 | 4368 | 0.3626 | 0.7184 | | 0.5164 | 29.0 | 4524 | 0.3215 | 0.7473 | | 0.5164 | 30.0 | 4680 | 0.2941 | 0.7581 | | 0.5164 | 31.0 | 4836 | 0.4957 | 0.6173 | | 0.5164 | 32.0 | 4992 | 0.3362 | 0.7329 | | 0.4676 | 33.0 | 5148 | 0.3116 | 0.7437 | | 0.4676 | 34.0 | 5304 | 0.3344 | 0.7401 | | 0.4676 | 35.0 | 5460 | 0.4769 | 0.7220 | | 0.4443 | 36.0 | 5616 | 0.2822 | 0.7509 | | 0.4443 | 37.0 | 5772 | 0.3748 | 0.6859 | | 0.4443 | 38.0 | 5928 | 0.2989 | 0.7509 | | 0.4179 | 39.0 | 6084 | 0.3193 | 0.7292 | | 0.4179 | 40.0 | 6240 | 0.3725 | 0.6715 | | 0.4179 | 41.0 | 6396 | 0.3336 | 0.7509 | | 0.3974 | 42.0 | 6552 | 0.2967 | 0.7365 | | 0.3974 | 43.0 | 6708 | 0.2908 | 0.7545 | | 0.3974 | 44.0 | 6864 | 0.2887 | 0.7473 | | 0.3774 | 45.0 | 7020 | 0.3012 | 0.7401 | | 0.3774 | 46.0 | 7176 | 0.3437 | 0.7509 | | 0.3774 | 47.0 | 7332 | 0.3390 | 0.7292 | | 0.3774 | 48.0 | 7488 | 0.2952 | 0.7473 | | 0.3419 | 49.0 | 7644 | 0.3116 | 0.7401 | | 0.3419 | 50.0 | 7800 | 0.2856 | 0.7473 | | 0.3419 | 51.0 | 7956 | 0.3227 | 0.7256 | | 0.3275 | 52.0 | 8112 | 0.2861 | 0.7509 | | 0.3275 | 53.0 | 8268 | 0.3534 | 0.7401 | | 0.3275 | 54.0 | 8424 | 0.3395 | 0.7256 | | 0.3225 | 55.0 | 8580 | 0.3113 | 0.7401 | | 0.3225 | 56.0 | 8736 | 0.2932 | 0.7473 | | 0.3225 | 57.0 | 8892 | 0.4312 | 0.7112 | | 0.3104 | 58.0 | 9048 | 0.3085 | 0.7509 | | 0.3104 | 59.0 | 9204 | 0.3164 | 0.7545 | | 0.3104 | 60.0 | 9360 | 0.2758 | 0.7473 | | 0.3164 | 61.0 | 9516 | 0.3183 | 0.7220 | | 0.3164 | 62.0 | 9672 | 0.3571 | 0.7220 | | 0.3164 | 63.0 | 9828 | 0.3156 | 0.7365 | | 0.3164 | 64.0 | 9984 | 0.2756 | 0.7653 | | 0.2939 | 65.0 | 10140 | 0.2859 | 0.7437 | | 0.2939 | 66.0 | 10296 | 0.2934 | 0.7545 | | 0.2939 | 67.0 | 10452 | 0.2977 | 0.7690 | | 0.2826 | 68.0 | 10608 | 0.2871 | 0.7653 | | 0.2826 | 69.0 | 10764 | 0.2903 | 0.7653 | | 0.2826 | 70.0 | 10920 | 0.2974 | 0.7581 | | 0.2663 | 71.0 | 11076 | 0.2778 | 0.7509 | | 0.2663 | 72.0 | 11232 | 0.2849 | 0.7365 | | 0.2663 | 73.0 | 11388 | 0.2970 | 0.7653 | | 0.2637 | 74.0 | 11544 | 0.3025 | 0.7545 | | 0.2637 | 75.0 | 11700 | 0.2793 | 0.7617 | | 0.2637 | 76.0 | 11856 | 0.2778 | 0.7545 | | 0.2699 | 77.0 | 12012 | 0.2861 | 0.7617 | | 0.2699 | 78.0 | 12168 | 0.2857 | 0.7690 | | 0.2699 | 79.0 | 12324 | 0.2774 | 0.7617 | | 0.2699 | 80.0 | 12480 | 0.2804 | 0.7617 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

dkqjrm/20230825070638

dkqjrm

2023-08-25T00:19:17Z

15

0

transformers

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

text-classification

2023-08-24T22:06:57Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - super_glue metrics: - accuracy model-index: - name: '20230825070638' results: [] ---  # 20230825070638 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3456 - Accuracy: 0.7329 ## 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: 0.005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 11 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 80.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | No log | 1.0 | 156 | 0.7894 | 0.5271 | | No log | 2.0 | 312 | 0.6658 | 0.5379 | | No log | 3.0 | 468 | 0.6408 | 0.5054 | | 0.886 | 4.0 | 624 | 0.7134 | 0.4729 | | 0.886 | 5.0 | 780 | 0.6234 | 0.5560 | | 0.886 | 6.0 | 936 | 0.4782 | 0.6318 | | 0.7765 | 7.0 | 1092 | 1.1394 | 0.5776 | | 0.7765 | 8.0 | 1248 | 0.5214 | 0.6534 | | 0.7765 | 9.0 | 1404 | 0.4206 | 0.6570 | | 0.7206 | 10.0 | 1560 | 0.5019 | 0.6643 | | 0.7206 | 11.0 | 1716 | 0.7680 | 0.5343 | | 0.7206 | 12.0 | 1872 | 0.3433 | 0.7220 | | 0.6543 | 13.0 | 2028 | 0.3834 | 0.7292 | | 0.6543 | 14.0 | 2184 | 0.4588 | 0.6751 | | 0.6543 | 15.0 | 2340 | 0.3413 | 0.7040 | | 0.6543 | 16.0 | 2496 | 0.4874 | 0.6426 | | 0.5973 | 17.0 | 2652 | 0.3283 | 0.7256 | | 0.5973 | 18.0 | 2808 | 0.3605 | 0.7329 | | 0.5973 | 19.0 | 2964 | 0.3314 | 0.7256 | | 0.5433 | 20.0 | 3120 | 0.5998 | 0.6606 | | 0.5433 | 21.0 | 3276 | 0.3489 | 0.6931 | | 0.5433 | 22.0 | 3432 | 0.4316 | 0.6715 | | 0.5373 | 23.0 | 3588 | 0.3328 | 0.7076 | | 0.5373 | 24.0 | 3744 | 0.3379 | 0.7220 | | 0.5373 | 25.0 | 3900 | 0.3580 | 0.7148 | | 0.4923 | 26.0 | 4056 | 0.3141 | 0.7329 | | 0.4923 | 27.0 | 4212 | 0.4341 | 0.7365 | | 0.4923 | 28.0 | 4368 | 0.3386 | 0.7220 | | 0.4513 | 29.0 | 4524 | 0.3038 | 0.7220 | | 0.4513 | 30.0 | 4680 | 0.3775 | 0.7220 | | 0.4513 | 31.0 | 4836 | 0.4197 | 0.7076 | | 0.4513 | 32.0 | 4992 | 0.4666 | 0.7220 | | 0.4041 | 33.0 | 5148 | 0.3355 | 0.7365 | | 0.4041 | 34.0 | 5304 | 0.3147 | 0.7329 | | 0.4041 | 35.0 | 5460 | 0.3810 | 0.7184 | | 0.3705 | 36.0 | 5616 | 0.3184 | 0.7256 | | 0.3705 | 37.0 | 5772 | 0.3668 | 0.7076 | | 0.3705 | 38.0 | 5928 | 0.3859 | 0.7220 | | 0.3556 | 39.0 | 6084 | 0.3010 | 0.7329 | | 0.3556 | 40.0 | 6240 | 0.3201 | 0.7220 | | 0.3556 | 41.0 | 6396 | 0.3304 | 0.7329 | | 0.3089 | 42.0 | 6552 | 0.3634 | 0.7365 | | 0.3089 | 43.0 | 6708 | 0.3844 | 0.7184 | | 0.3089 | 44.0 | 6864 | 0.3320 | 0.7220 | | 0.3015 | 45.0 | 7020 | 0.3696 | 0.7220 | | 0.3015 | 46.0 | 7176 | 0.3665 | 0.7220 | | 0.3015 | 47.0 | 7332 | 0.3355 | 0.7256 | | 0.3015 | 48.0 | 7488 | 0.3568 | 0.7292 | | 0.2709 | 49.0 | 7644 | 0.3450 | 0.7329 | | 0.2709 | 50.0 | 7800 | 0.3790 | 0.7148 | | 0.2709 | 51.0 | 7956 | 0.3516 | 0.7112 | | 0.2681 | 52.0 | 8112 | 0.3741 | 0.7329 | | 0.2681 | 53.0 | 8268 | 0.3615 | 0.7220 | | 0.2681 | 54.0 | 8424 | 0.3479 | 0.7292 | | 0.2477 | 55.0 | 8580 | 0.3401 | 0.7184 | | 0.2477 | 56.0 | 8736 | 0.3766 | 0.7329 | | 0.2477 | 57.0 | 8892 | 0.3562 | 0.7148 | | 0.2344 | 58.0 | 9048 | 0.3412 | 0.7220 | | 0.2344 | 59.0 | 9204 | 0.3782 | 0.7437 | | 0.2344 | 60.0 | 9360 | 0.3723 | 0.7040 | | 0.2126 | 61.0 | 9516 | 0.3852 | 0.7292 | | 0.2126 | 62.0 | 9672 | 0.3901 | 0.7256 | | 0.2126 | 63.0 | 9828 | 0.3698 | 0.7112 | | 0.2126 | 64.0 | 9984 | 0.3249 | 0.7220 | | 0.2127 | 65.0 | 10140 | 0.3979 | 0.7004 | | 0.2127 | 66.0 | 10296 | 0.3705 | 0.7365 | | 0.2127 | 67.0 | 10452 | 0.3317 | 0.7220 | | 0.199 | 68.0 | 10608 | 0.3322 | 0.7329 | | 0.199 | 69.0 | 10764 | 0.3706 | 0.7220 | | 0.199 | 70.0 | 10920 | 0.3628 | 0.7148 | | 0.1959 | 71.0 | 11076 | 0.3600 | 0.7437 | | 0.1959 | 72.0 | 11232 | 0.3349 | 0.7437 | | 0.1959 | 73.0 | 11388 | 0.3650 | 0.7184 | | 0.184 | 74.0 | 11544 | 0.3337 | 0.7365 | | 0.184 | 75.0 | 11700 | 0.3309 | 0.7329 | | 0.184 | 76.0 | 11856 | 0.3237 | 0.7365 | | 0.183 | 77.0 | 12012 | 0.3430 | 0.7256 | | 0.183 | 78.0 | 12168 | 0.3567 | 0.7329 | | 0.183 | 79.0 | 12324 | 0.3541 | 0.7329 | | 0.183 | 80.0 | 12480 | 0.3456 | 0.7329 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

IALABS/Arturosfastfood

IALABS

2023-08-25T00:14:58Z

0

1

null

[ "conversational", "es", "license:other", "region:us" ]

text-generation

2023-08-24T23:32:33Z

--- license: other language: - es pipeline_tag: conversational --- size_categories: n<1K tags: - rlfh - argilla - human-feedback

stevhliu/my_awesome_model

stevhliu

2023-08-25T00:04:52Z

16,177

3

transformers

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

text-classification

2022-09-28T18:41:57Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: stevhliu/my_awesome_model results: [] ---  # stevhliu/my_awesome_model 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: 0.0632 - Validation Loss: 0.2355 - Train Accuracy: 0.9295 - Epoch: 2 ## 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': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.2518 | 0.1859 | 0.9261 | 0 | | 0.1319 | 0.1822 | 0.9318 | 1 | | 0.0632 | 0.2355 | 0.9295 | 2 | ### Framework versions - Transformers 4.22.2 - TensorFlow 2.8.2 - Datasets 2.5.1 - Tokenizers 0.12.1

PivotOrDie/vit-base-patch16-224-finetuned-flower

PivotOrDie

2023-08-25T00:02:48Z

165

0

transformers

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

image-classification

2023-08-24T23:46:42Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: vit-base-patch16-224-finetuned-flower results: [] ---  # vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder 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: 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 ### Framework versions - Transformers 4.24.0 - Pytorch 2.0.1+cu118 - Datasets 2.7.1 - Tokenizers 0.13.3

abdiharyadi/IndoT5-base-amr-to-text-linearized-penman-ilmy-epochs-10

abdiharyadi

2023-08-24T23:50:07Z

291

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:Wikidepia/IndoT5-base", "base_model:finetune:Wikidepia/IndoT5-base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-08-24T23:19:50Z

--- base_model: Wikidepia/IndoT5-base tags: - generated_from_trainer model-index: - name: IndoT5-base-amr-to-text-linearized-penman-ilmy-epochs-10 results: [] ---  # IndoT5-base-amr-to-text-linearized-penman-ilmy-epochs-10 This model is a fine-tuned version of [Wikidepia/IndoT5-base](https://huggingface.co/Wikidepia/IndoT5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1789 ## 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 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 331 | 0.6415 | | 0.4503 | 2.0 | 662 | 0.8102 | | 0.4503 | 3.0 | 993 | 0.8944 | | 0.0619 | 4.0 | 1324 | 0.9228 | | 0.0262 | 5.0 | 1655 | 1.0949 | | 0.0262 | 6.0 | 1986 | 1.1223 | | 0.0158 | 7.0 | 2317 | 1.1668 | | 0.0103 | 8.0 | 2648 | 1.1655 | | 0.0103 | 9.0 | 2979 | 1.1861 | | 0.008 | 10.0 | 3310 | 1.1789 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

slmnpl/stable-diffusion-webui-master

slmnpl

2023-08-24T23:28:46Z

0

null

[ "arxiv:2211.06679", "region:us" ]

null

2023-08-24T23:18:23Z

# Stable Diffusion web UI A browser interface based on Gradio library for Stable Diffusion. ![](screenshot.png) ## Features [Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features): - Original txt2img and img2img modes - One click install and run script (but you still must install python and git) - Outpainting - Inpainting - Color Sketch - Prompt Matrix - Stable Diffusion Upscale - Attention, specify parts of text that the model should pay more attention to - a man in a `((tuxedo))` - will pay more attention to tuxedo - a man in a `(tuxedo:1.21)` - alternative syntax - select text and press `Ctrl+Up` or `Ctrl+Down` (or `Command+Up` or `Command+Down` if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user) - Loopback, run img2img processing multiple times - X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters - Textual Inversion - have as many embeddings as you want and use any names you like for them - use multiple embeddings with different numbers of vectors per token - works with half precision floating point numbers - train embeddings on 8GB (also reports of 6GB working) - Extras tab with: - GFPGAN, neural network that fixes faces - CodeFormer, face restoration tool as an alternative to GFPGAN - RealESRGAN, neural network upscaler - ESRGAN, neural network upscaler with a lot of third party models - SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers - LDSR, Latent diffusion super resolution upscaling - Resizing aspect ratio options - Sampling method selection - Adjust sampler eta values (noise multiplier) - More advanced noise setting options - Interrupt processing at any time - 4GB video card support (also reports of 2GB working) - Correct seeds for batches - Live prompt token length validation - Generation parameters - parameters you used to generate images are saved with that image - in PNG chunks for PNG, in EXIF for JPEG - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI - can be disabled in settings - drag and drop an image/text-parameters to promptbox - Read Generation Parameters Button, loads parameters in promptbox to UI - Settings page - Running arbitrary python code from UI (must run with `--allow-code` to enable) - Mouseover hints for most UI elements - Possible to change defaults/mix/max/step values for UI elements via text config - Tiling support, a checkbox to create images that can be tiled like textures - Progress bar and live image generation preview - Can use a separate neural network to produce previews with almost none VRAM or compute requirement - Negative prompt, an extra text field that allows you to list what you don't want to see in generated image - Styles, a way to save part of prompt and easily apply them via dropdown later - Variations, a way to generate same image but with tiny differences - Seed resizing, a way to generate same image but at slightly different resolution - CLIP interrogator, a button that tries to guess prompt from an image - Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway - Batch Processing, process a group of files using img2img - Img2img Alternative, reverse Euler method of cross attention control - Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions - Reloading checkpoints on the fly - Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one - [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community - [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once - separate prompts using uppercase `AND` - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2` - No token limit for prompts (original stable diffusion lets you use up to 75 tokens) - DeepDanbooru integration, creates danbooru style tags for anime prompts - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args) - via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI - Generate forever option - Training tab - hypernetworks and embeddings options - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime) - Clip skip - Hypernetworks - Loras (same as Hypernetworks but more pretty) - A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt - Can select to load a different VAE from settings screen - Estimated completion time in progress bar - API - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML - via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)) - [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions - [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions - Now without any bad letters! - Load checkpoints in safetensors format - Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64 - Now with a license! - Reorder elements in the UI from settings screen ## Installation and Running Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. Alternatively, use online services (like Google Colab): - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services) ### Installation on Windows 10/11 with NVidia-GPUs using release package 1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract it's contents. 2. Run `update.bat`. 3. Run `run.bat`. > For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) ### Automatic Installation on Windows 1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH". 2. Install [git](https://git-scm.com/download/win). 3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`. 4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user. ### Automatic Installation on Linux 1. Install the dependencies: ```bash # Debian-based: sudo apt install wget git python3 python3-venv # Red Hat-based: sudo dnf install wget git python3 # Arch-based: sudo pacman -S wget git python3 ``` 2. Navigate to the directory you would like the webui to be installed and execute the following command: ```bash bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh) ``` 3. Run `webui.sh`. 4. Check `webui-user.sh` for options. ### Installation on Apple Silicon Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon). ## Contributing Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) ## Documentation The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki). For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) [crawlable wiki](https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/wiki). ## Credits Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file. - Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers - k-diffusion - https://github.com/crowsonkb/k-diffusion.git - GFPGAN - https://github.com/TencentARC/GFPGAN.git - CodeFormer - https://github.com/sczhou/CodeFormer - ESRGAN - https://github.com/xinntao/ESRGAN - SwinIR - https://github.com/JingyunLiang/SwinIR - Swin2SR - https://github.com/mv-lab/swin2sr - LDSR - https://github.com/Hafiidz/latent-diffusion - MiDaS - https://github.com/isl-org/MiDaS - Ideas for optimizations - https://github.com/basujindal/stable-diffusion - Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing. - Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion) - Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention) - Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas). - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator - Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch - xformers - https://github.com/facebookresearch/xformers - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru - Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6) - Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix - Security advice - RyotaK - UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC - TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd - LyCORIS - KohakuBlueleaf - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user. - (You)

dt-and-vanilla-ardt/ardt-vanilla-arrl_train_halfcheetah_high-2408_2205-33

dt-and-vanilla-ardt

2023-08-24T23:16:05Z

31

0

transformers

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

null

2023-08-24T21:06:53Z

--- tags: - generated_from_trainer model-index: - name: ardt-vanilla-arrl_train_halfcheetah_high-2408_2205-33 results: [] ---  # ardt-vanilla-arrl_train_halfcheetah_high-2408_2205-33 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - 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 - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.1.0.dev20230727+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

jlpan/starcoder-cpp2py-newsnippet1

jlpan

2023-08-24T23:02:57Z

0

null

[ "generated_from_trainer", "base_model:bigcode/starcoder", "base_model:finetune:bigcode/starcoder", "license:bigcode-openrail-m", "region:us" ]

null

2023-08-23T00:24:41Z

--- license: bigcode-openrail-m base_model: bigcode/starcoder tags: - generated_from_trainer model-index: - name: starcoder-cpp2py-newsnippet1 results: [] ---  # starcoder-cpp2py-newsnippet1 This model is a fine-tuned version of [bigcode/starcoder](https://huggingface.co/bigcode/starcoder) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1964 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 15 - training_steps: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.3875 | 0.17 | 25 | 0.4694 | | 0.2947 | 0.33 | 50 | 0.2126 | | 0.2152 | 0.5 | 75 | 0.2016 | | 0.2054 | 0.67 | 100 | 0.1974 | | 0.2004 | 0.83 | 125 | 0.1966 | | 0.1883 | 1.05 | 150 | 0.1964 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

Akhilsplendid/T5-model

Akhilsplendid

2023-08-24T23:00:48Z

104

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:philschmid/flan-t5-base-samsum", "base_model:finetune:philschmid/flan-t5-base-samsum", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-08-24T22:59:06Z

--- license: apache-2.0 base_model: philschmid/flan-t5-base-samsum tags: - generated_from_trainer model-index: - name: T5-model results: [] ---  # T5-model This model is a fine-tuned version of [philschmid/flan-t5-base-samsum](https://huggingface.co/philschmid/flan-t5-base-samsum) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7013 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9849 | 0.8 | 10 | 0.8062 | | 0.9748 | 1.61 | 20 | 0.8026 | | 0.9772 | 2.41 | 30 | 0.7968 | | 0.979 | 3.22 | 40 | 0.7889 | | 0.9729 | 4.02 | 50 | 0.7793 | | 0.9479 | 4.82 | 60 | 0.7687 | | 0.9111 | 5.63 | 70 | 0.7577 | | 0.8956 | 6.43 | 80 | 0.7460 | | 0.8768 | 7.24 | 90 | 0.7338 | | 0.8566 | 8.04 | 100 | 0.7224 | | 0.8342 | 8.84 | 110 | 0.7120 | | 0.8273 | 9.65 | 120 | 0.7013 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

ufal/byt5-small-multilexnorm2021-trde

ufal

2023-08-24T21:40:24Z

115

1

transformers

[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "lexical normalization", "tr", "de", "multilingual", "dataset:mc4", "dataset:wikipedia", "dataset:multilexnorm", "arxiv:2105.13626", "arxiv:1907.06292", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-02T23:29:05Z

--- language: - tr - de - multilingual datasets: - mc4 - wikipedia - multilexnorm tags: - lexical normalization license: apache-2.0 --- # Fine-tuned ByT5-small for MultiLexNorm (Turkish-German version) ![model image](https://github.com/ufal/multilexnorm2021/raw/master/img/overall.png) This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://noisy-text.github.io/2021/multi-lexnorm.html), which evaluates lexical-normalization systems on 12 social media datasets in 11 languages. Our system is based on [ByT5](https://arxiv.org/abs/2105.13626), which we first pre-train on synthetic data and then fine-tune on authentic normalization data. It achieves the best performance by a wide margin in intrinsic evaluation, and also the best performance in extrinsic evaluation through dependency parsing. In addition to these fine-tuned models, we also release the source files on [GitHub](https://github.com/ufal/multilexnorm2021) and an interactive demo on [Google Colab](https://colab.research.google.com/drive/1rxpI8IlKk-D2crFqi2hdzbTBIezqgsCg?usp=sharing). ## How to use The model was *not* fine-tuned in a standard sentence-to-sentence setting – instead, it was tailored to the token-to-token definition of MultiLexNorm data. Please refer to [**the interactive demo on Colab notebook**](https://colab.research.google.com/drive/1rxpI8IlKk-D2crFqi2hdzbTBIezqgsCg?usp=sharing) to learn how to use these models. ## How to cite ```bibtex @inproceedings{wnut-ufal, title= "{ÚFAL} at {MultiLexNorm} 2021: Improving Multilingual Lexical Normalization by Fine-tuning {ByT5}", author = "Samuel, David and Straka, Milan", booktitle = "Proceedings of the 7th Workshop on Noisy User-generated Text (W-NUT 2021)", year = "2021", publisher = "Association for Computational Linguistics", address = "Punta Cana, Dominican Republic" } ``` ## ByT5 - Small ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-small). ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task. ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-small` significantly outperforms [mt5-small](https://huggingface.co/google/mt5-small) on [TweetQA](https://arxiv.org/abs/1907.06292). Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) Authors: *Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel*

tifa-benchmark/llama2_tifa_question_generation

tifa-benchmark

2023-08-24T21:28:03Z

418

10

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "llama2", "text-to-image", "en", "dataset:TIFA", "arxiv:2303.11897", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-08-16T00:41:50Z

--- license: apache-2.0 inference: true widget: - text: "<s>[INST] <<SYS>>\nGiven an image description, generate one or two multiple-choice questions that verifies if the image description is correct.\nClassify each concept into a type (object, human, animal, food, activity, attribute, counting, color, material, spatial, location, shape, other), and then generate a question for each type.\n\n<</SYS>>\n\nDescription: a blue rabbit and a red plane [/INST] Entities:" pipeline_tag: text-generation tags: - text-generation-inference - llama2 - text-to-image datasets: - TIFA language: - en --- Project page: <https://tifa-benchmark.github.io/> This is the text parsing and question generation model for the ICCV 2023 paper [TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering](https://arxiv.org/abs/2303.11897) We introduce TIFA (Text-to-Image Faithfulness evaluation with question Answering), an automatic evaluation metric that measures the faithfulness of a generated image to its text input via visual question answering (VQA). Specifically, given a text input, we automatically generate several question-answer pairs using a language model. We calculate image faithfulness by checking whether existing VQA models can answer these questions using the generated image. Specifically, this fine-tuned LLaMA 2 model is the substitute for the GPT-3 model in the paper. It can parse an arbitrary prompt into visual entities, attributes, relations, etc. and generate question-answer tuples for each of them. See examples below. # QuickStart All codes are from <https://github.com/Yushi-Hu/tifa>. Clone this repo to easily use this model together with other modules (e.g. VQA) provided in TIFA. Please follow the prompt format, which will give the best performance. ```python import torch import transformers # prepare the LLaMA 2 model model_name = "tifa-benchmark/llama2_tifa_question_generation" pipeline = transformers.pipeline( "text-generation", model=model_name, torch_dtype=torch.float16, device_map="auto", ) # formating prompt following LLaMA 2 style def create_qg_prompt(caption): INTRO_BLURB = "Given an image description, generate one or two multiple-choice questions that verifies if the image description is correct.\nClassify each concept into a type (object, human, animal, food, activity, attribute, counting, color, material, spatial, location, shape, other), and then generate a question for each type.\n" formated_prompt = f"<s>[INST] <<SYS>>\n{INTRO_BLURB}\n<</SYS>>\n\n" formated_prompt += f"Description: {caption} [/INST] Entities:" return formated_prompt test_caption = "a blue rabbit and a red plane" # create prompt prompt = create_qg_prompt(text_caption) # text completion sequences = pipeline( prompt, do_sample=False, num_beams=5, num_return_sequences=1, max_length=512) output = sequences[0]['generated_text'][len(prompt):] output = output.split('\n\n')[0] # output print(output) #### Expected output ### # rabbit, plane # Activites: # Colors: blue, red # Counting: # Other attributes: # About rabbit (animal): # Q: is this a rabbit? # Choices: yes, no # A: yes # About rabbit (animal): # Q: what animal is in the picture? # Choices: rabbit, dog, cat, fish # A: rabbit # About plane (object): # Q: is this a plane? # Choices: yes, no # A: yes # About plane (object): # Q: what type of vehicle is this? # Choices: plane, car, motorcycle, bus # A: plane # About blue (color): # Q: is the rabbit blue? # Choices: yes, no # A: yes # About blue (color): # Q: what color is the rabbit? # Choices: blue, red, yellow, green # A: blue # About red (color): # Q: is the plane red? # Choices: yes, no # A: yes # About red (color): # Q: what color is the plane? # Choices: red, blue, yellow, green # A: red ``` # Use this LM under tifascore package tifascore provides extra functions to parse this output etc. First install tifascore according to <https://github.com/Yushi-Hu/tifa>. Then the usage is below ```python from tifascore import get_llama2_pipeline, get_llama2_question_and_answers pipeline = get_llama2_pipeline("tifa-benchmark/llama2_tifa_question_generation") print(get_llama2_question_and_answers(pipeline, "a blue rabbit and a red plane")) #### Expected output ### # [{'caption': 'a blue rabbit and a red plane', 'element': 'rabbit', 'question': 'what animal is in the picture?', 'choices': ['rabbit', 'dog', 'cat', 'fish'], 'answer': 'rabbit', 'element_type': 'animal/human'}, {'caption': 'a blue rabbit and a red plane', 'element': 'plane', 'question': 'is this a plane?', 'choices': ['yes', 'no'], 'answer': 'yes', 'element_type': 'object'}, {'caption': 'a blue rabbit and a red plane', 'element': 'plane', 'question': 'what type of vehicle is this?', 'choices': ['plane', 'car', 'motorcycle', 'bus'], 'answer': 'plane', 'element_type': 'object'}, {'caption': 'a blue rabbit and a red plane', 'element': 'blue', 'question': 'is the rabbit blue?', 'choices': ['yes', 'no'], 'answer': 'yes', 'element_type': 'color'}, {'caption': 'a blue rabbit and a red plane', 'element': 'blue', 'question': 'what color is the rabbit?', 'choices': ['blue', 'red', 'yellow', 'green'], 'answer': 'blue', 'element_type': 'color'}, {'caption': 'a blue rabbit and a red plane', 'element': 'red', 'question': 'is the plane red?', 'choices': ['yes', 'no'], 'answer': 'yes', 'element_type': 'color'}, {'caption': 'a blue rabbit and a red plane', 'element': 'red', 'question': 'what color is the plane?', 'choices': ['red', 'blue', 'yellow', 'green'], 'answer': 'red', 'element_type': 'color'}] ``` ## Bibtex ``` @article{hu2023tifa, title={Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering}, author={Hu, Yushi and Liu, Benlin and Kasai, Jungo and Wang, Yizhong and Ostendorf, Mari and Krishna, Ranjay and Smith, Noah A}, journal={arXiv preprint arXiv:2303.11897}, year={2023} } ```

magooie/Reinforce-Cartpole-v1

magooie

2023-08-24T21:09:31Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-08-24T21:09:22Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

anth0nyhak1m/SS_model

anth0nyhak1m

2023-08-24T20:52:54Z

105

0

transformers

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

text-classification

2023-08-24T20:51:15Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: SS_model results: [] ---  # SS_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3980 - Accuracy: 0.9587 ## 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.153 | 1.0 | 4301 | 0.1472 | 0.9526 | | 0.1165 | 2.0 | 8602 | 0.1376 | 0.9562 | | 0.0951 | 3.0 | 12903 | 0.1462 | 0.9596 | | 0.0851 | 4.0 | 17204 | 0.1550 | 0.9602 | | 0.0709 | 5.0 | 21505 | 0.1848 | 0.9596 | | 0.069 | 6.0 | 25806 | 0.2027 | 0.9586 | | 0.0591 | 7.0 | 30107 | 0.2266 | 0.9582 | | 0.047 | 8.0 | 34408 | 0.2110 | 0.9573 | | 0.0391 | 9.0 | 38709 | 0.2405 | 0.9577 | | 0.0333 | 10.0 | 43010 | 0.2865 | 0.9566 | | 0.0336 | 11.0 | 47311 | 0.2671 | 0.9588 | | 0.0226 | 12.0 | 51612 | 0.2743 | 0.9567 | | 0.0266 | 13.0 | 55913 | 0.3281 | 0.9577 | | 0.0191 | 14.0 | 60214 | 0.3062 | 0.9572 | | 0.0232 | 15.0 | 64515 | 0.3479 | 0.9585 | | 0.0149 | 16.0 | 68816 | 0.3542 | 0.9587 | | 0.0099 | 17.0 | 73117 | 0.3646 | 0.9587 | | 0.0123 | 18.0 | 77418 | 0.3721 | 0.9584 | | 0.0091 | 19.0 | 81719 | 0.3896 | 0.9590 | | 0.0086 | 20.0 | 86020 | 0.3980 | 0.9587 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

kejolong/racequeen

kejolong

2023-08-24T20:44:59Z

0

null

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

null

2023-08-24T20:43:34Z

--- license: creativeml-openrail-m ---

zehralx/distilbert-base-uncased-finetuned-emotion

zehralx

2023-08-24T20:44:54Z

109

0

transformers

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

text-classification

2023-08-20T13:38:45Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9235 - name: F1 type: f1 value: 0.9234578926922112 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2208 - Accuracy: 0.9235 - F1: 0.9235 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8077 | 1.0 | 250 | 0.3196 | 0.9065 | 0.9049 | | 0.2488 | 2.0 | 500 | 0.2208 | 0.9235 | 0.9235 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1 - Datasets 2.14.3 - Tokenizers 0.13.3

dimitarrskv/ppo-SnowballTarget

dimitarrskv

2023-08-24T20:40:06Z

1

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]

reinforcement-learning

2023-08-24T20:40:04Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: dimitarrskv/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

camenduru/StableSR

camenduru

2023-08-24T20:36:48Z

0

3

null

[ "image-to-image", "arxiv:2305.07015", "license:other", "region:us" ]

image-to-image

2023-08-24T20:29:35Z

--- license: other pipeline_tag: image-to-image --- # StableSR Model Card This model card focuses on the models associated with the StableSR, available [here](https://github.com/IceClear/StableSR). ## Model Details - **Developed by:** Jianyi Wang - **Model type:** Diffusion-based image super-resolution model - **License:** [S-Lab License 1.0](https://github.com/IceClear/StableSR/blob/main/LICENSE.txt) - **Model Description:** This is the model used in [Paper](https://arxiv.org/abs/2305.07015). - **Resources for more information:** [GitHub Repository](https://github.com/IceClear/StableSR). - **Cite as:** @InProceedings{wang2023exploiting, author = {Wang, Jianyi and Yue, Zongsheng and Zhou, Shangchen and Chan, Kelvin CK and Loy, Chen Change}, title = {Exploiting Diffusion Prior for Real-World Image Super-Resolution}, booktitle = {arXiv preprint arXiv:2305.07015}, year = {2023}, } # Uses Please refer to [S-Lab License 1.0](https://github.com/IceClear/StableSR/blob/main/LICENSE.txt) ## Limitations and Bias ### Limitations - StableSR still requires multiple steps for generating an image, which is much slower than GAN-based approaches, especially for large images beyond 512 or 768. - StableSR sometimes cannot keep 100% fidelity due to its generative nature. - StableSR sometimes cannot generate perfect details under complex real-world scenarios. ### Bias While our model is based on a pre-trained Stable Diffusion model, currently we do not observe obvious bias in generated results. We conjecture the main reason is that our model does not rely on text prompts but on low-resolution images. Such strong conditions make our model less likely to be affected. ## Training **Training Data** The model developer used the following dataset for training the model: - Our diffusion model is finetuned on DF2K (DIV2K and Flickr2K) + OST datasets, available [here](https://github.com/xinntao/Real-ESRGAN/blob/master/docs/Training.md). - We further generate 100k synthetic LR-HR pairs on DF2K_OST using the finetuned diffusion model for training the CFW module. **Training Procedure** StableSR is an image super-resolution model finetuned on [Stable Diffusion](https://github.com/Stability-AI/stablediffusion), further equipped with a time-aware encoder and a controllable feature wrapping (CFW) module. - Following Stable Diffusion, images are encoded through the fixed autoencoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4. - The latent representations are fed to the time-aware encoder as guidance. - The loss is the same as Stable Diffusion. - After finetuning the diffusion model, we further train the CFW module using the data generated by the finetuned diffusion model. - The autoencoder model is fixed and only CFW is trainable. - The loss is similar to training an autoencoder, except that we use a fixed adversarial loss weight of 0.025 rather than a self-adjustable one. We currently provide the following checkpoints: - [stablesr_000117.ckpt](https://huggingface.co/Iceclear/StableSR/resolve/main/stablesr_000117.ckpt): Diffusion model finetuned on [SD2.1-512base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base) with DF2K_OST dataset for 117 epochs. - [vqgan_cfw_00011.ckpt](https://huggingface.co/Iceclear/StableSR/resolve/main/vqgan_cfw_00011.ckpt): CFW module with fixed autoencoder trained on synthetic paired data for 11 epochs. - [stablesr_768v_000139.ckpt](https://huggingface.co/Iceclear/StableSR/blob/main/stablesr_768v_000139.ckpt): Diffusion model finetuned on [SD2.1-768v](https://huggingface.co/stabilityai/stable-diffusion-2-1) with DF2K_OST dataset for 139 epochs. ## Evaluation Results See [Paper](https://arxiv.org/abs/2305.07015) for details.

AmelieSchreiber/esm2_t6_8M_UR50D_cafa5_lora

AmelieSchreiber

2023-08-24T20:35:14Z

5

1

peft

[ "peft", "pytorch", "esm", "esm2", "ESM-2", "protein language model", "LoRA", "Low Rank Adaptation", "biology", "CAFA-5", "protein function prediction", "en", "dataset:AmelieSchreiber/cafa_5", "license:mit", "region:us" ]

null

2023-08-22T05:29:29Z

--- library_name: peft tags: - esm - esm2 - ESM-2 - protein language model - LoRA - Low Rank Adaptation - biology - CAFA-5 - protein function prediction datasets: - AmelieSchreiber/cafa_5 license: mit language: - en --- # ESM-2 LoRA for CAFA-5 Protein Function Prediction This is a Low Rank Adaptation (LoRA) of [cafa_5_protein_function_prediction](https://huggingface.co/AmelieSchreiber/cafa_5_protein_function_prediction), which is a fine-tuned (without LoRA) version of `facebook/esm2_t6_8M_UR50D`, for the same task. For more information on training a sequence classifier langauge model with LoRA [see here](https://github.com/huggingface/peft/blob/main/examples/sequence_classification/LoRA.ipynb). Note, this is for natural language processing and must be adapted to our use case using a protein language model like ESM-2. ## Training procedure Using Hugging Face's Parameter Efficient Fine-Tuning (PEFT) library, a Low Rank Adaptation was trained for 3 epochs on the CAFA-5 protein sequences dataset at an 80/20 train/test split. The dataset can be [found here](https://huggingface.co/datasets/AmelieSchreiber/cafa_5). Somewhat naively, the model was trained on the `train_sequences.fasta` file of protein sequences, with the `train_terms.tsv` file serving as the labels. The gene ontology used is a hierarchy, and so the labels lower in the hierchay should be weighted more, or the graph structure should be taken into account. The model achieved the following metrics: ``` Epoch: 3, Validation Loss: 0.0031, Validation Micro F1: 0.3752, Validation Macro F1: 0.9968, Validation Micro Precision: 0.5287, Validation Macro Precision: 0.9992, Validation Micro Recall: 0.2911, Validation Macro Recall: 0.9968 ``` Future iterations of this model will likely need to take into account class weighting. ### Framework versions - PEFT 0.4.0 ## Using the Model To use the model, try downloading the data [from here](https://huggingface.co/datasets/AmelieSchreiber/cafa_5), adjust the paths to the files in the code below to their local paths on your machine, and try running: ```python import os import numpy as np import torch from transformers import AutoTokenizer, EsmForSequenceClassification, AdamW from torch.nn.functional import binary_cross_entropy_with_logits from sklearn.model_selection import train_test_split from sklearn.metrics import f1_score, precision_score, recall_score from accelerate import Accelerator from Bio import SeqIO # Step 1: Data Preprocessing fasta_file = "data/Train/train_sequences.fasta" tsv_file = "data/Train/train_terms.tsv" fasta_data = {} tsv_data = {} for record in SeqIO.parse(fasta_file, "fasta"): fasta_data[record.id] = str(record.seq) with open(tsv_file, 'r') as f: for line in f: parts = line.strip().split("\t") tsv_data[parts[0]] = parts[1:] unique_terms = list(set(term for terms in tsv_data.values() for term in terms)) def parse_fasta(file_path): """ Parses a FASTA file and returns a list of sequences. """ with open(file_path, 'r') as f: content = f.readlines() sequences = [] current_sequence = "" for line in content: if line.startswith(">"): if current_sequence: sequences.append(current_sequence) current_sequence = "" else: current_sequence += line.strip() if current_sequence: sequences.append(current_sequence) return sequences # Parse the provided FASTA file fasta_file_path = "data/Test/testsuperset.fasta" protein_sequences = parse_fasta(fasta_file_path) # protein_sequences[:3] # Displaying the first 3 sequences for verification import torch from transformers import AutoTokenizer, EsmForSequenceClassification from sklearn.metrics import precision_recall_fscore_support # 1. Parsing the go-basic.obo file (Assuming this is still needed) def parse_obo_file(file_path): with open(file_path, 'r') as f: data = f.read().split("[Term]") terms = [] for entry in data[1:]: lines = entry.strip().split("\n") term = {} for line in lines: if line.startswith("id:"): term["id"] = line.split("id:")[1].strip() elif line.startswith("name:"): term["name"] = line.split("name:")[1].strip() elif line.startswith("namespace:"): term["namespace"] = line.split("namespace:")[1].strip() elif line.startswith("def:"): term["definition"] = line.split("def:")[1].split('"')[1] terms.append(term) return terms # Let's assume the path to go-basic.obo is as follows (please modify if different) obo_file_path = "data/Train/go-basic.obo" parsed_terms = parse_obo_file("data/Train/go-basic.obo") # Replace with your path # 2. Load the saved model and tokenizer # Assuming the model path provided is correct from transformers import AutoTokenizer, AutoModelForSequenceClassification from peft import PeftModel, PeftConfig # Load the tokenizer and model model_id = "AmelieSchreiber/esm2_t6_8M_UR50D_cafa5_lora" # Replace with your Hugging Face hub model name tokenizer = AutoTokenizer.from_pretrained(model_id) # First, we load the underlying base model base_model = AutoModelForSequenceClassification.from_pretrained(model_id) # Then, we load the model with PEFT model = PeftModel.from_pretrained(base_model, model_id) loaded_model = model loaded_tokenizer = AutoTokenizer.from_pretrained(model_id) # 3. The predict_protein_function function def predict_protein_function(sequence, model, tokenizer, go_terms): inputs = tokenizer(sequence, return_tensors="pt", padding=True, truncation=True, max_length=1022) model.eval() with torch.no_grad(): outputs = model(**inputs) predictions = torch.sigmoid(outputs.logits) predicted_indices = torch.where(predictions > 0.05)[1].tolist() functions = [] for idx in predicted_indices: term_id = unique_terms[idx] # Use the unique_terms list from your training script for term in go_terms: if term["id"] == term_id: functions.append(term["name"]) break return functions # 4. Predicting protein function for the sequences in the FASTA file protein_functions = {} for seq in protein_sequences[:20]: # Using only the first 3 sequences for demonstration predicted_functions = predict_protein_function(seq, loaded_model, loaded_tokenizer, parsed_terms) protein_functions[seq[:20] + "..."] = predicted_functions # Using first 20 characters as key protein_functions ```

sidroy/bloom-560m

sidroy

2023-08-24T20:33:24Z

0

peft

[ "peft", "region:us" ]

null

2023-08-24T20:25:46Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0