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 2025-07-15 12:29:39	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 521 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-15 12:28:52	card stringlengths 11 1.01M
elopezlopez/distilbert-base-uncased_fold_5_binary_v1	elopezlopez	2022-08-02T23:02:16Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-02T22:48:50Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_5_binary_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_fold_5_binary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6980 - F1: 0.8110 ## Model description More information needed ## 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: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 288 \| 0.4412 \| 0.7981 \| \| 0.396 \| 2.0 \| 576 \| 0.4419 \| 0.8078 \| \| 0.396 \| 3.0 \| 864 \| 0.4955 \| 0.8166 \| \| 0.2019 \| 4.0 \| 1152 \| 0.6341 \| 0.8075 \| \| 0.2019 \| 5.0 \| 1440 \| 1.0351 \| 0.7979 \| \| 0.0808 \| 6.0 \| 1728 \| 1.1818 \| 0.7844 \| \| 0.0315 \| 7.0 \| 2016 \| 1.2530 \| 0.8051 \| \| 0.0315 \| 8.0 \| 2304 \| 1.3568 \| 0.7937 \| \| 0.0143 \| 9.0 \| 2592 \| 1.4009 \| 0.8045 \| \| 0.0143 \| 10.0 \| 2880 \| 1.5333 \| 0.7941 \| \| 0.0066 \| 11.0 \| 3168 \| 1.5242 \| 0.7982 \| \| 0.0066 \| 12.0 \| 3456 \| 1.5752 \| 0.8050 \| \| 0.0091 \| 13.0 \| 3744 \| 1.5199 \| 0.8046 \| \| 0.0111 \| 14.0 \| 4032 \| 1.5319 \| 0.8117 \| \| 0.0111 \| 15.0 \| 4320 \| 1.5333 \| 0.8156 \| \| 0.0072 \| 16.0 \| 4608 \| 1.5461 \| 0.8192 \| \| 0.0072 \| 17.0 \| 4896 \| 1.5288 \| 0.8252 \| \| 0.0048 \| 18.0 \| 5184 \| 1.5725 \| 0.8078 \| \| 0.0048 \| 19.0 \| 5472 \| 1.5896 \| 0.8138 \| \| 0.0032 \| 20.0 \| 5760 \| 1.6917 \| 0.8071 \| \| 0.0028 \| 21.0 \| 6048 \| 1.6608 \| 0.8109 \| \| 0.0028 \| 22.0 \| 6336 \| 1.7013 \| 0.8122 \| \| 0.0029 \| 23.0 \| 6624 \| 1.6769 \| 0.8148 \| \| 0.0029 \| 24.0 \| 6912 \| 1.6906 \| 0.8100 \| \| 0.0006 \| 25.0 \| 7200 \| 1.6980 \| 0.8110 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
elopezlopez/distilbert-base-uncased_fold_4_binary_v1	elopezlopez	2022-08-02T22:47:30Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-02T22:34:06Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_4_binary_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_fold_4_binary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5144 - F1: 0.8245 ## Model description More information needed ## 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: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 289 \| 0.3756 \| 0.8175 \| \| 0.3977 \| 2.0 \| 578 \| 0.3672 \| 0.8336 \| \| 0.3977 \| 3.0 \| 867 \| 0.4997 \| 0.8276 \| \| 0.1972 \| 4.0 \| 1156 \| 0.6597 \| 0.8244 \| \| 0.1972 \| 5.0 \| 1445 \| 0.8501 \| 0.8195 \| \| 0.0824 \| 6.0 \| 1734 \| 1.0074 \| 0.8097 \| \| 0.037 \| 7.0 \| 2023 \| 1.1122 \| 0.8131 \| \| 0.037 \| 8.0 \| 2312 \| 1.0963 \| 0.8189 \| \| 0.0182 \| 9.0 \| 2601 \| 1.2511 \| 0.8125 \| \| 0.0182 \| 10.0 \| 2890 \| 1.2255 \| 0.8141 \| \| 0.0121 \| 11.0 \| 3179 \| 1.3120 \| 0.8187 \| \| 0.0121 \| 12.0 \| 3468 \| 1.4182 \| 0.8165 \| \| 0.0079 \| 13.0 \| 3757 \| 1.4142 \| 0.8218 \| \| 0.0081 \| 14.0 \| 4046 \| 1.4765 \| 0.8150 \| \| 0.0081 \| 15.0 \| 4335 \| 1.3510 \| 0.8187 \| \| 0.0109 \| 16.0 \| 4624 \| 1.3455 \| 0.8255 \| \| 0.0109 \| 17.0 \| 4913 \| 1.4157 \| 0.8234 \| \| 0.0022 \| 18.0 \| 5202 \| 1.4651 \| 0.8197 \| \| 0.0022 \| 19.0 \| 5491 \| 1.4388 \| 0.8267 \| \| 0.0017 \| 20.0 \| 5780 \| 1.4552 \| 0.8304 \| \| 0.0005 \| 21.0 \| 6069 \| 1.5357 \| 0.8248 \| \| 0.0005 \| 22.0 \| 6358 \| 1.4924 \| 0.8241 \| \| 0.0009 \| 23.0 \| 6647 \| 1.4865 \| 0.8248 \| \| 0.0009 \| 24.0 \| 6936 \| 1.4697 \| 0.8275 \| \| 0.0013 \| 25.0 \| 7225 \| 1.5144 \| 0.8245 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
elopezlopez/distilbert-base-uncased_fold_3_binary_v1	elopezlopez	2022-08-02T22:32:39Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-02T22:19:07Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_3_binary_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_fold_3_binary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9405 - F1: 0.7878 ## Model description More information needed ## 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: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 289 \| 0.4630 \| 0.7897 \| \| 0.3954 \| 2.0 \| 578 \| 0.4549 \| 0.7936 \| \| 0.3954 \| 3.0 \| 867 \| 0.6527 \| 0.7868 \| \| 0.1991 \| 4.0 \| 1156 \| 0.7510 \| 0.7951 \| \| 0.1991 \| 5.0 \| 1445 \| 0.9327 \| 0.8000 \| \| 0.095 \| 6.0 \| 1734 \| 1.0974 \| 0.7859 \| \| 0.0347 \| 7.0 \| 2023 \| 1.2692 \| 0.7919 \| \| 0.0347 \| 8.0 \| 2312 \| 1.3718 \| 0.7921 \| \| 0.0105 \| 9.0 \| 2601 \| 1.4679 \| 0.7999 \| \| 0.0105 \| 10.0 \| 2890 \| 1.5033 \| 0.8070 \| \| 0.0079 \| 11.0 \| 3179 \| 1.6074 \| 0.8008 \| \| 0.0079 \| 12.0 \| 3468 \| 1.6921 \| 0.7904 \| \| 0.0053 \| 13.0 \| 3757 \| 1.7079 \| 0.7945 \| \| 0.0054 \| 14.0 \| 4046 \| 1.8361 \| 0.7887 \| \| 0.0054 \| 15.0 \| 4335 \| 1.7695 \| 0.7873 \| \| 0.0046 \| 16.0 \| 4624 \| 1.7934 \| 0.7917 \| \| 0.0046 \| 17.0 \| 4913 \| 1.8036 \| 0.8008 \| \| 0.0064 \| 18.0 \| 5202 \| 1.8780 \| 0.7888 \| \| 0.0064 \| 19.0 \| 5491 \| 1.8943 \| 0.7923 \| \| 0.0032 \| 20.0 \| 5780 \| 1.8694 \| 0.7905 \| \| 0.002 \| 21.0 \| 6069 \| 1.9348 \| 0.7869 \| \| 0.002 \| 22.0 \| 6358 \| 1.9578 \| 0.7804 \| \| 0.0036 \| 23.0 \| 6647 \| 1.9438 \| 0.7827 \| \| 0.0036 \| 24.0 \| 6936 \| 1.9386 \| 0.7878 \| \| 0.0011 \| 25.0 \| 7225 \| 1.9405 \| 0.7878 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
elopezlopez/distilbert-base-uncased_fold_2_binary_v1	elopezlopez	2022-08-02T22:17:49Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-02T22:03:59Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_2_binary_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_fold_2_binary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8833 - F1: 0.7841 ## Model description More information needed ## 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: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 290 \| 0.4060 \| 0.8070 \| \| 0.3981 \| 2.0 \| 580 \| 0.4534 \| 0.8072 \| \| 0.3981 \| 3.0 \| 870 \| 0.5460 \| 0.7961 \| \| 0.1985 \| 4.0 \| 1160 \| 0.8684 \| 0.7818 \| \| 0.1985 \| 5.0 \| 1450 \| 0.9009 \| 0.7873 \| \| 0.0844 \| 6.0 \| 1740 \| 1.1529 \| 0.7825 \| \| 0.0329 \| 7.0 \| 2030 \| 1.3185 \| 0.7850 \| \| 0.0329 \| 8.0 \| 2320 \| 1.4110 \| 0.7862 \| \| 0.0109 \| 9.0 \| 2610 \| 1.4751 \| 0.7784 \| \| 0.0109 \| 10.0 \| 2900 \| 1.6276 \| 0.7723 \| \| 0.0071 \| 11.0 \| 3190 \| 1.6779 \| 0.7861 \| \| 0.0071 \| 12.0 \| 3480 \| 1.6258 \| 0.7850 \| \| 0.0041 \| 13.0 \| 3770 \| 1.6324 \| 0.7903 \| \| 0.0109 \| 14.0 \| 4060 \| 1.7563 \| 0.7932 \| \| 0.0109 \| 15.0 \| 4350 \| 1.6740 \| 0.7906 \| \| 0.0079 \| 16.0 \| 4640 \| 1.7468 \| 0.7944 \| \| 0.0079 \| 17.0 \| 4930 \| 1.7095 \| 0.7879 \| \| 0.0067 \| 18.0 \| 5220 \| 1.7293 \| 0.7912 \| \| 0.0021 \| 19.0 \| 5510 \| 1.7875 \| 0.7848 \| \| 0.0021 \| 20.0 \| 5800 \| 1.7462 \| 0.7906 \| \| 0.0026 \| 21.0 \| 6090 \| 1.8549 \| 0.7815 \| \| 0.0026 \| 22.0 \| 6380 \| 1.8314 \| 0.7860 \| \| 0.0021 \| 23.0 \| 6670 \| 1.8577 \| 0.7839 \| \| 0.0021 \| 24.0 \| 6960 \| 1.8548 \| 0.7883 \| \| 0.0001 \| 25.0 \| 7250 \| 1.8833 \| 0.7841 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
sgraf202/finetuning-sentiment-model-3000-samples	sgraf202	2022-08-02T21:32:52Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-18T10:41:11Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples 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. --> # finetuning-sentiment-model-3000-samples 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: 0.7404 - Accuracy: 0.4688 - F1: 0.5526 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
aujer/autotrain-not_interested_2-1213045881	aujer	2022-08-02T21:15:40Z	4	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "en", "dataset:aujer/autotrain-data-not_interested_2", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-02T21:14:05Z	--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - aujer/autotrain-data-not_interested_2 co2_eq_emissions: emissions: 1.695519133475222 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1213045881 - CO2 Emissions (in grams): 1.6955 ## Validation Metrics - Loss: 1.607 - Accuracy: 0.535 - Macro F1: 0.306 - Micro F1: 0.535 - Weighted F1: 0.440 - Macro Precision: 0.346 - Micro Precision: 0.535 - Weighted Precision: 0.435 - Macro Recall: 0.345 - Micro Recall: 0.535 - Weighted Recall: 0.535 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/aujer/autotrain-not_interested_2-1213045881 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("aujer/autotrain-not_interested_2-1213045881", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("aujer/autotrain-not_interested_2-1213045881", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
srcocotero/tiny-bert-qa	srcocotero	2022-08-02T19:58:09Z	6	2	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "endpoints_compatible", "region:us" ]	question-answering	2022-07-27T19:12:14Z	--- tags: - generated_from_trainer datasets: - squad model-index: - name: mini_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. --> # mini_model This model is a fine-tuned version of [nreimers/BERT-Tiny_L-2_H-128_A-2](https://huggingface.co/nreimers/BERT-Tiny_L-2_H-128_A-2) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 5 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
Rifky/indobert-hoax-classification	Rifky	2022-08-02T19:32:31Z	18	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-02T16:42:51Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: indobert-hoax-classification 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. --> # indobert-hoax-classification This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6230 - Accuracy: 0.8059 ## Model description More information needed ## 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: 4.2173070213315e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 30 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 85 \| 0.5540 \| 0.7029 \| \| No log \| 2.0 \| 170 \| 0.5432 \| 0.7029 \| \| No log \| 3.0 \| 255 \| 0.4963 \| 0.7441 \| \| No log \| 4.0 \| 340 \| 0.5791 \| 0.7971 \| \| No log \| 5.0 \| 425 \| 0.6230 \| 0.8059 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
elopezlopez/distilbert-base-uncased_fold_10_ternary_v1	elopezlopez	2022-08-02T18:22:45Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-02T18:09:19Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_10_ternary_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_fold_10_ternary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9887 - F1: 0.7797 ## Model description More information needed ## 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: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 290 \| 0.5701 \| 0.7463 \| \| 0.5651 \| 2.0 \| 580 \| 0.5359 \| 0.7748 \| \| 0.5651 \| 3.0 \| 870 \| 0.6043 \| 0.7847 \| \| 0.2605 \| 4.0 \| 1160 \| 1.0124 \| 0.7587 \| \| 0.2605 \| 5.0 \| 1450 \| 1.1140 \| 0.7599 \| \| 0.1223 \| 6.0 \| 1740 \| 1.2713 \| 0.7859 \| \| 0.0469 \| 7.0 \| 2030 \| 1.3188 \| 0.7822 \| \| 0.0469 \| 8.0 \| 2320 \| 1.3819 \| 0.7946 \| \| 0.0279 \| 9.0 \| 2610 \| 1.5444 \| 0.7847 \| \| 0.0279 \| 10.0 \| 2900 \| 1.5851 \| 0.7908 \| \| 0.0084 \| 11.0 \| 3190 \| 1.7003 \| 0.7822 \| \| 0.0084 \| 12.0 \| 3480 \| 1.8148 \| 0.7748 \| \| 0.007 \| 13.0 \| 3770 \| 1.7651 \| 0.7748 \| \| 0.008 \| 14.0 \| 4060 \| 1.8423 \| 0.7748 \| \| 0.008 \| 15.0 \| 4350 \| 1.7871 \| 0.7809 \| \| 0.0054 \| 16.0 \| 4640 \| 1.9324 \| 0.7748 \| \| 0.0054 \| 17.0 \| 4930 \| 1.8685 \| 0.7809 \| \| 0.0048 \| 18.0 \| 5220 \| 1.9901 \| 0.7797 \| \| 0.002 \| 19.0 \| 5510 \| 1.9273 \| 0.7785 \| \| 0.002 \| 20.0 \| 5800 \| 1.9945 \| 0.7809 \| \| 0.0018 \| 21.0 \| 6090 \| 1.9250 \| 0.7785 \| \| 0.0018 \| 22.0 \| 6380 \| 1.9929 \| 0.7822 \| \| 0.0032 \| 23.0 \| 6670 \| 1.9306 \| 0.7859 \| \| 0.0032 \| 24.0 \| 6960 \| 1.9603 \| 0.7847 \| \| 0.0029 \| 25.0 \| 7250 \| 1.9887 \| 0.7797 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
elopezlopez/distilbert-base-uncased_fold_9_ternary_v1	elopezlopez	2022-08-02T18:08:13Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-02T17:54:55Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_9_ternary_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_fold_9_ternary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9406 - F1: 0.7841 ## Model description More information needed ## 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: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 292 \| 0.5684 \| 0.7635 \| \| 0.5656 \| 2.0 \| 584 \| 0.5753 \| 0.7725 \| \| 0.5656 \| 3.0 \| 876 \| 0.6159 \| 0.7866 \| \| 0.2499 \| 4.0 \| 1168 \| 0.7743 \| 0.7828 \| \| 0.2499 \| 5.0 \| 1460 \| 0.9820 \| 0.7674 \| \| 0.1153 \| 6.0 \| 1752 \| 1.2383 \| 0.7738 \| \| 0.0547 \| 7.0 \| 2044 \| 1.2468 \| 0.7815 \| \| 0.0547 \| 8.0 \| 2336 \| 1.3480 \| 0.7622 \| \| 0.0233 \| 9.0 \| 2628 \| 1.3791 \| 0.7892 \| \| 0.0233 \| 10.0 \| 2920 \| 1.4344 \| 0.7841 \| \| 0.0142 \| 11.0 \| 3212 \| 1.4958 \| 0.7802 \| \| 0.0087 \| 12.0 \| 3504 \| 1.5714 \| 0.7674 \| \| 0.0087 \| 13.0 \| 3796 \| 1.6129 \| 0.7956 \| \| 0.0111 \| 14.0 \| 4088 \| 1.7799 \| 0.7751 \| \| 0.0111 \| 15.0 \| 4380 \| 1.7272 \| 0.7789 \| \| 0.0055 \| 16.0 \| 4672 \| 1.7696 \| 0.7866 \| \| 0.0055 \| 17.0 \| 4964 \| 1.8622 \| 0.7789 \| \| 0.003 \| 18.0 \| 5256 \| 1.8563 \| 0.7802 \| \| 0.0004 \| 19.0 \| 5548 \| 1.8993 \| 0.7815 \| \| 0.0004 \| 20.0 \| 5840 \| 1.9199 \| 0.7853 \| \| 0.0005 \| 21.0 \| 6132 \| 1.9003 \| 0.7879 \| \| 0.0005 \| 22.0 \| 6424 \| 1.9161 \| 0.7828 \| \| 0.0011 \| 23.0 \| 6716 \| 1.9691 \| 0.7815 \| \| 0.0017 \| 24.0 \| 7008 \| 1.9492 \| 0.7841 \| \| 0.0017 \| 25.0 \| 7300 \| 1.9406 \| 0.7841 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
ArunkumarCH/DeepLearning	ArunkumarCH	2022-08-02T17:54:25Z	0	0	null	[ "region:us" ]	null	2022-08-02T17:53:42Z	About this DeepLearning Model: We will build an front end application to upload the image and get the deeplearning model predicts the name of the object with acccuracy. Steps for building the Image classification model: 1. Image classification model using pretrained DL model 1.1 Define deeplearning model 2.2 Preprocess the data 3.3 Get prediction 1.1 Define deep learning model # import required modules import json import numpy as np from PIL import Image import matplotlib.pyplot as plt # import pytorch related modules import torch from torchvision import transforms from torchvision.models import densenet121 # define pretrained DL model model = densenet121(pretrained=True) model.eval(); 1.2 Preprocess data # load image using PIL input_image = Image.open(filename) # preprocess image according to the pretrained model preprocess = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) input_tensor = preprocess(input_image) # create a mini-batch as expected by the model input_batch = input_tensor.unsqueeze(0) # pass input batch to the model with torch.no_grad(): output = model(input_batch) 1.3 Get prediction pred = torch.nn.functional.softmax(output[0], dim=0).cpu().numpy() np.argmax(pred) # download classes on which the model was trained on !wget https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json # get the prediction accuracy print(classes[str(np.argmax(pred))][1], round(max(pred)100, 2)) 2. Deploying Image Classification model 1.1 Install required libraries 1.2 Setup DL model using streamlit 1.3 Deploy DL model on AWS/Colab/HF spaces 1.1 Install required libraries !pip install -q streamlit !pip install -q pyngrok 1.2 Setup DL model using streamlit %%writefile app.py ## create streamlit app # import required libraries and modules import json import numpy as np import matplotlib.pyplot as plt import torch from PIL import Image from torchvision import transforms from torchvision.models import densenet121 import streamlit as st # define prediction function def predict(image): # load DL model model = densenet121(pretrained=True) model.eval() # load classes with open('imagenet_class_index.json', 'r') as f: classes = json.load(f) # preprocess image preprocess = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) input_tensor = preprocess(input_image) input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model # get prediction with torch.no_grad(): output = model(input_batch) pred = torch.nn.functional.softmax(output[0], dim=0).cpu().numpy() # return confidence and label confidence = round(max(pred)100, 2) label = classes[str(np.argmax(pred))][1] return confidence, label # define image file uploader image = st.file_uploader("Upload image here") # define button for getting prediction if image is not None and st.button("Get prediction"): # load image using PIL input_image = Image.open(image) # show image st.image(input_image, use_column_width=True) # get prediction confidence, label = predict(input_image) # print results "Model is", confidence, "% confident that this image is of a", label 1.3 Deploy DL model # run streamlit app !streamlit run app.py &>/dev/null& # make streamlit app available publicly from pyngrok import ngrok public_url = ngrok.connect('8501'); public_url Model can be deployed on AWS/Colab/Flask/Hugging Spaces Hugging spaces model https://huggingface.co/spaces/ArunkumarCH/BirdClassification
elopezlopez/distilbert-base-uncased_fold_8_ternary_v1	elopezlopez	2022-08-02T17:53:47Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-02T17:40:31Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_8_ternary_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_fold_8_ternary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8474 - F1: 0.8022 ## Model description More information needed ## 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: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 289 \| 0.5398 \| 0.7838 \| \| 0.5509 \| 2.0 \| 578 \| 0.6062 \| 0.7703 \| \| 0.5509 \| 3.0 \| 867 \| 0.6563 \| 0.7666 \| \| 0.2366 \| 4.0 \| 1156 \| 0.7688 \| 0.7961 \| \| 0.2366 \| 5.0 \| 1445 \| 1.0968 \| 0.7690 \| \| 0.1247 \| 6.0 \| 1734 \| 1.1414 \| 0.7924 \| \| 0.0482 \| 7.0 \| 2023 \| 1.2159 \| 0.7875 \| \| 0.0482 \| 8.0 \| 2312 \| 1.2703 \| 0.7887 \| \| 0.0245 \| 9.0 \| 2601 \| 1.3401 \| 0.7985 \| \| 0.0245 \| 10.0 \| 2890 \| 1.4645 \| 0.7961 \| \| 0.0149 \| 11.0 \| 3179 \| 1.5632 \| 0.7801 \| \| 0.0149 \| 12.0 \| 3468 \| 1.5249 \| 0.7875 \| \| 0.0124 \| 13.0 \| 3757 \| 1.6263 \| 0.7948 \| \| 0.0038 \| 14.0 \| 4046 \| 1.8059 \| 0.7764 \| \| 0.0038 \| 15.0 \| 4335 \| 1.7649 \| 0.7776 \| \| 0.0061 \| 16.0 \| 4624 \| 1.8293 \| 0.7850 \| \| 0.0061 \| 17.0 \| 4913 \| 1.8316 \| 0.7887 \| \| 0.0022 \| 18.0 \| 5202 \| 1.7628 \| 0.7973 \| \| 0.0022 \| 19.0 \| 5491 \| 1.8763 \| 0.7862 \| \| 0.002 \| 20.0 \| 5780 \| 1.8409 \| 0.7899 \| \| 0.0026 \| 21.0 \| 6069 \| 1.8146 \| 0.8022 \| \| 0.0026 \| 22.0 \| 6358 \| 1.8420 \| 0.7973 \| \| 0.0008 \| 23.0 \| 6647 \| 1.8683 \| 0.8010 \| \| 0.0008 \| 24.0 \| 6936 \| 1.8571 \| 0.8010 \| \| 0.0015 \| 25.0 \| 7225 \| 1.8474 \| 0.8022 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
elopezlopez/distilbert-base-uncased_fold_5_ternary_v1	elopezlopez	2022-08-02T17:10:39Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-02T16:56:52Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_5_ternary_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_fold_5_ternary_v1 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: 2.1368 - F1: 0.7682 ## Model description More information needed ## 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: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 291 \| 0.6423 \| 0.7465 \| \| 0.5563 \| 2.0 \| 582 \| 0.6001 \| 0.7631 \| \| 0.5563 \| 3.0 \| 873 \| 0.6884 \| 0.7785 \| \| 0.2595 \| 4.0 \| 1164 \| 0.9920 \| 0.7439 \| \| 0.2595 \| 5.0 \| 1455 \| 1.1434 \| 0.7631 \| \| 0.1159 \| 6.0 \| 1746 \| 1.3289 \| 0.7606 \| \| 0.0473 \| 7.0 \| 2037 \| 1.3966 \| 0.7708 \| \| 0.0473 \| 8.0 \| 2328 \| 1.4761 \| 0.7606 \| \| 0.0282 \| 9.0 \| 2619 \| 1.6144 \| 0.7542 \| \| 0.0282 \| 10.0 \| 2910 \| 1.5642 \| 0.7695 \| \| 0.0134 \| 11.0 \| 3201 \| 1.7206 \| 0.7593 \| \| 0.0134 \| 12.0 \| 3492 \| 1.8008 \| 0.7542 \| \| 0.0059 \| 13.0 \| 3783 \| 1.8056 \| 0.7746 \| \| 0.002 \| 14.0 \| 4074 \| 1.9160 \| 0.7593 \| \| 0.002 \| 15.0 \| 4365 \| 2.0223 \| 0.7606 \| \| 0.0052 \| 16.0 \| 4656 \| 1.9112 \| 0.7810 \| \| 0.0052 \| 17.0 \| 4947 \| 1.9040 \| 0.7772 \| \| 0.0056 \| 18.0 \| 5238 \| 1.9852 \| 0.7734 \| \| 0.0061 \| 19.0 \| 5529 \| 2.0590 \| 0.7644 \| \| 0.0061 \| 20.0 \| 5820 \| 2.1078 \| 0.7631 \| \| 0.0044 \| 21.0 \| 6111 \| 2.1177 \| 0.7631 \| \| 0.0044 \| 22.0 \| 6402 \| 2.0983 \| 0.7644 \| \| 0.0012 \| 23.0 \| 6693 \| 2.1384 \| 0.7670 \| \| 0.0012 \| 24.0 \| 6984 \| 2.1467 \| 0.7657 \| \| 0.0018 \| 25.0 \| 7275 \| 2.1368 \| 0.7682 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
mrm8488/dqn-SpaceInvadersNoFrameskip-v4-2	mrm8488	2022-08-02T17:00:07Z	6	1	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-08-02T16:59:39Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 181.00 +/- 111.42 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mrm8488 -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mrm8488 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 1024), ('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', 800000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
elopezlopez/distilbert-base-uncased_fold_4_ternary_v1	elopezlopez	2022-08-02T16:55:47Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-02T16:42:24Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_4_ternary_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_fold_4_ternary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9355 - F1: 0.7891 ## Model description More information needed ## 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: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 289 \| 0.5637 \| 0.7485 \| \| 0.5729 \| 2.0 \| 578 \| 0.5305 \| 0.7805 \| \| 0.5729 \| 3.0 \| 867 \| 0.6948 \| 0.7670 \| \| 0.2548 \| 4.0 \| 1156 \| 0.8351 \| 0.7744 \| \| 0.2548 \| 5.0 \| 1445 \| 1.0005 \| 0.8027 \| \| 0.1157 \| 6.0 \| 1734 \| 1.1578 \| 0.7978 \| \| 0.0473 \| 7.0 \| 2023 \| 1.2275 \| 0.7953 \| \| 0.0473 \| 8.0 \| 2312 \| 1.3245 \| 0.7916 \| \| 0.0276 \| 9.0 \| 2601 \| 1.3728 \| 0.7953 \| \| 0.0276 \| 10.0 \| 2890 \| 1.4577 \| 0.7867 \| \| 0.0149 \| 11.0 \| 3179 \| 1.5832 \| 0.7731 \| \| 0.0149 \| 12.0 \| 3468 \| 1.5056 \| 0.7818 \| \| 0.0143 \| 13.0 \| 3757 \| 1.6263 \| 0.7904 \| \| 0.0066 \| 14.0 \| 4046 \| 1.6596 \| 0.7793 \| \| 0.0066 \| 15.0 \| 4335 \| 1.6795 \| 0.7941 \| \| 0.0022 \| 16.0 \| 4624 \| 1.8443 \| 0.7744 \| \| 0.0022 \| 17.0 \| 4913 \| 1.7160 \| 0.7953 \| \| 0.0034 \| 18.0 \| 5202 \| 1.7819 \| 0.7781 \| \| 0.0034 \| 19.0 \| 5491 \| 1.7931 \| 0.7904 \| \| 0.0036 \| 20.0 \| 5780 \| 1.8447 \| 0.7818 \| \| 0.0014 \| 21.0 \| 6069 \| 1.9975 \| 0.7707 \| \| 0.0014 \| 22.0 \| 6358 \| 1.9324 \| 0.7830 \| \| 0.0008 \| 23.0 \| 6647 \| 1.9086 \| 0.7842 \| \| 0.0008 \| 24.0 \| 6936 \| 1.9507 \| 0.7867 \| \| 0.0002 \| 25.0 \| 7225 \| 1.9355 \| 0.7891 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
jinghan/deberta-base-finetuned-wnli	jinghan	2022-08-02T15:47:58Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "deberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-02T14:56:26Z	--- license: mit tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: deberta-base-finetuned-wnli results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: wnli split: train args: wnli metrics: - name: Accuracy type: accuracy value: 0.5633802816901409 --- <!-- 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. --> # deberta-base-finetuned-wnli This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6926 - Accuracy: 0.5634 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 40 \| 0.6926 \| 0.5634 \| \| No log \| 2.0 \| 80 \| 0.6911 \| 0.5634 \| \| No log \| 3.0 \| 120 \| 0.6903 \| 0.5634 \| \| No log \| 4.0 \| 160 \| 0.6905 \| 0.5634 \| \| No log \| 5.0 \| 200 \| 0.6904 \| 0.5634 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
ligerre/xlm-roberta-base-finetuned-panx-fr	ligerre	2022-08-02T15:32:07Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-08-02T15:15:14Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.8299296953465015 --- <!-- 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-fr 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: 0.2848 - F1: 0.8299 ## Model description More information needed ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.5989 \| 1.0 \| 191 \| 0.3383 \| 0.7928 \| \| 0.2617 \| 2.0 \| 382 \| 0.2966 \| 0.8318 \| \| 0.1672 \| 3.0 \| 573 \| 0.2848 \| 0.8299 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
huggingtweets/iamsamirarora-naval-vivek_investor	huggingtweets	2022-08-02T15:16:48Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-08-02T15:15:22Z	--- language: en thumbnail: http://www.huggingtweets.com/iamsamirarora-naval-vivek_investor/1659453403535/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1256841238298292232/ycqwaMI2_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/853146176295759872/YiAPXQ0s_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1479277051802574853/qs6u-imt_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Naval & Samir Arora & Vivek</div> <div style="text-align: center; font-size: 14px;">@iamsamirarora-naval-vivek_investor</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Naval & Samir Arora & Vivek. \| Data \| Naval \| Samir Arora \| Vivek \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3211 \| 3250 \| 3250 \| \| Retweets \| 195 \| 76 \| 96 \| \| Short tweets \| 612 \| 973 \| 601 \| \| Tweets kept \| 2404 \| 2201 \| 2553 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1oa4j8zi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @iamsamirarora-naval-vivek_investor's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/21s56oiv) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/21s56oiv/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/iamsamirarora-naval-vivek_investor') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
ligerre/xlm-roberta-base-finetuned-panx-de-fr	ligerre	2022-08-02T15:09:38Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-08-02T14:48:44Z	--- license: mit 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: 0.1654 - F1: 0.8590 ## Model description More information needed ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2845 \| 1.0 \| 715 \| 0.1831 \| 0.8249 \| \| 0.1449 \| 2.0 \| 1430 \| 0.1643 \| 0.8479 \| \| 0.0929 \| 3.0 \| 2145 \| 0.1654 \| 0.8590 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
ligerre/xlm-roberta-base-finetuned-panx-de	ligerre	2022-08-02T14:39:34Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-08-02T14:16:11Z	--- license: mit 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 args: PAN-X.de metrics: - name: F1 type: f1 value: 0.863677639046538 --- <!-- 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: 0.1343 - F1: 0.8637 ## Model description More information needed ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2578 \| 1.0 \| 525 \| 0.1562 \| 0.8273 \| \| 0.1297 \| 2.0 \| 1050 \| 0.1330 \| 0.8474 \| \| 0.0809 \| 3.0 \| 1575 \| 0.1343 \| 0.8637 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
aliromagnoli/distilbert-base-uncased-finetuned-emotion	aliromagnoli	2022-08-02T14:26:32Z	10	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-02T13:13:25Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.924 - name: F1 type: f1 value: 0.9238827602069696 --- <!-- 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: 0.2145 - Accuracy: 0.924 - F1: 0.9239 ## Model description More information needed ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8235 \| 1.0 \| 250 \| 0.3050 \| 0.9085 \| 0.9063 \| \| 0.2456 \| 2.0 \| 500 \| 0.2145 \| 0.924 \| 0.9239 \| ### Framework versions - Transformers 4.13.0 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
anjleeg/roberta-base-finetuned-cola	anjleeg	2022-08-02T14:02:49Z	4	0	transformers	[ "transformers", "pytorch", "optimum_graphcore", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-02T12:51:41Z	--- license: mit tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: roberta-base-finetuned-cola 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. --> # roberta-base-finetuned-cola This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4497 - Matthews Correlation: 0.6272 ## Model description More information needed ## 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: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - total_eval_batch_size: 20 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - training precision: Mixed Precision ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.4453 \| 1.0 \| 133 \| 0.4348 \| 0.5391 \| \| 0.3121 \| 2.0 \| 266 \| 0.3938 \| 0.5827 \| \| 0.1149 \| 3.0 \| 399 \| 0.4497 \| 0.6272 \| \| 0.1194 \| 4.0 \| 532 \| 0.5005 \| 0.6076 \| \| 0.1639 \| 5.0 \| 665 \| 0.5645 \| 0.5943 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cpu - Datasets 2.4.0 - Tokenizers 0.12.1
s-nlp/GenChal_2022_nigula	s-nlp	2022-08-02T13:43:11Z	11	2	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "feedback comment generation for writing learning", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-07-08T15:17:59Z	--- language: - en tags: - feedback comment generation for writing learning licenses: - cc-by-nc-sa --- ## Model overview This model was trained in terms of [GenChal 2022: Feedback Comment Generation for Writing Learning](https://fcg.sharedtask.org/) shared task In this task, the model gets the string with text with the error and the exact span of the error and should return the comment in natural language, which explains the nature of the error. ## How to use ```python !pip install feedback_generation_nigula from feedback_generation_nigula.generator import FeedbackGenerator fg = FeedbackGenerator(cuda_index = 0) text_with_error = "The smoke flow my face ." error_span = (10,17) fg.get_feedback([text_with_error ], [error_span ]) # expected output ["When the <verb> <<flow>> is used as an <intransitive verb> to express'' to move in a stream'', a <preposition> needs to be placed to indicate the direction"] ``` ## Model training details #### Data The data was provided in the following way ``` input sentence [\t] offset range [\t] feedback comment ``` Here are some examples ``` The smoke flow my face . 10:17 When the <verb> <<flow>> is used as an <intransitive verb> to express ''to move in a stream'', a <preposition> needs to be placed to indicate the direction. 'To' and 'towards' are <prepositions> that indicate direction. I want to stop smoking during driving bicycle . 23:29 A <gerund> does not normally follow the <preposition> <<during>>. Think of an expression using the <conjunction> 'while' instead of a <preposition>. ``` Grammar termins are highlighted with '< ... >' marks and word examples - with '<< ... >>' #### Data preprocessing We lowercased the text, split it from any punctuation, including task specific marks (<< >>) and explicitly pointed out the error in the original text using << >>. ``` the smoke < < flow > > < < my > > face . 10:17 When the < verb > < < flow > > is used as an < intransitive verb > to express '' to move in a stream '', a < preposition > needs to be placed to indicate the direction. ' to ' and ' towards ' are < prepositions > that indicate direction . i want to stop smoking < < during > > driving bicycle . 23:29 a < gerund > does not normally follow the < preposition > < < during > > . think of an expression using the < conjunction > ' while ' instead of a < preposition > . ``` #### Data augmentation The main feature of our training pipeline was data augmentation. The idea of the augmentation is as follows: we cut the existing text with error after the last word which was syntactically connected to the words inside the error span (syntactic dependencies were automatically parsed with spacy) and this cut version of the text with error was used as a prompt for language model (we used [GPT-Neo 1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B)). Using both initial and augmented data we fine-tuned [t5-large](https://huggingface.co/t5-large). ## Licensing Information [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. [![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] [cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ [cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png
dandelin/vilt-b32-finetuned-vqa	dandelin	2022-08-02T13:03:04Z	105,438	400	transformers	[ "transformers", "pytorch", "vilt", "visual-question-answering", "arxiv:2102.03334", "license:apache-2.0", "endpoints_compatible", "region:us" ]	visual-question-answering	2022-03-02T23:29:05Z	--- tags: - visual-question-answering license: apache-2.0 widget: - text: "What's the animal doing?" src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg" - text: "What is on top of the building?" src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg" --- # Vision-and-Language Transformer (ViLT), fine-tuned on VQAv2 Vision-and-Language Transformer (ViLT) model fine-tuned on [VQAv2](https://visualqa.org/). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT). Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Intended uses & limitations You can use the raw model for visual question answering. ### How to use Here is how to use this model in PyTorch: ```python from transformers import ViltProcessor, ViltForQuestionAnswering import requests from PIL import Image # prepare image + question url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) text = "How many cats are there?" processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa") model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa") # prepare inputs encoding = processor(image, text, return_tensors="pt") # forward pass outputs = model(**encoding) logits = outputs.logits idx = logits.argmax(-1).item() print("Predicted answer:", model.config.id2label[idx]) ``` ## Training data (to do) ## Training procedure ### Preprocessing (to do) ### Pretraining (to do) ## Evaluation results (to do) ### BibTeX entry and citation info ```bibtex @misc{kim2021vilt, title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision}, author={Wonjae Kim and Bokyung Son and Ildoo Kim}, year={2021}, eprint={2102.03334}, archivePrefix={arXiv}, primaryClass={stat.ML} } ```
sepidmnorozy/finetuned-sentiment-withGPU	sepidmnorozy	2022-08-02T12:33:09Z	7	1	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-04T13:26:21Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: finetuning-sentiment-model_withGPU 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. --> # finetuning-sentiment-model-10-samples_withGPU This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3893 - Accuracy: 0.8744 - F1: 0.8684 - Precision: 0.9126 - Recall: 0.8283 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.3631 \| 1.0 \| 7088 \| 0.3622 \| 0.8638 \| 0.8519 \| 0.9334 \| 0.7835 \| \| 0.35 \| 2.0 \| 14176 \| 0.3875 \| 0.8714 \| 0.8622 \| 0.9289 \| 0.8044 \| \| 0.3262 \| 3.0 \| 21264 \| 0.3893 \| 0.8744 \| 0.8684 \| 0.9126 \| 0.8283 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0 - Datasets 2.0.0 - Tokenizers 0.11.6
pannaga/wav2vec2-base-timit-demo-google-colab-testing	pannaga	2022-08-02T12:18:36Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-07-21T10:06:12Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-google-colab-testing results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-google-colab-testing This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.12.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1
wenkai-li/distilbert-base-uncased-finetuned-wikiandmark_epoch50	wenkai-li	2022-08-02T12:11:19Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-02T11:02:56Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-wikiandmark_epoch50 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-wikiandmark_epoch50 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: - eval_loss: 0.0049 - eval_accuracy: 0.9995 - eval_runtime: 29.1585 - eval_samples_per_second: 127.613 - eval_steps_per_second: 4.013 - epoch: 6.0 - step: 4656 ## Model description More information needed ## 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: 50 ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
dfsj/distilbert-base-uncased-distilled-clinc	dfsj	2022-08-02T11:38:29Z	4	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-01T00:46:22Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9448387096774193 --- <!-- 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-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.3163 - Accuracy: 0.9448 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 9 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 318 \| 2.3518 \| 0.7510 \| \| 2.7559 \| 2.0 \| 636 \| 1.2235 \| 0.8506 \| \| 2.7559 \| 3.0 \| 954 \| 0.6786 \| 0.9168 \| \| 1.0767 \| 4.0 \| 1272 \| 0.4668 \| 0.9368 \| \| 0.4584 \| 5.0 \| 1590 \| 0.3810 \| 0.9410 \| \| 0.4584 \| 6.0 \| 1908 \| 0.3479 \| 0.9435 \| \| 0.2876 \| 7.0 \| 2226 \| 0.3282 \| 0.9455 \| \| 0.2285 \| 8.0 \| 2544 \| 0.3201 \| 0.9452 \| \| 0.2285 \| 9.0 \| 2862 \| 0.3163 \| 0.9448 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu102 - Datasets 2.0.0 - Tokenizers 0.12.1
spacestar1705/Reinforce-CartPole-v1	spacestar1705	2022-08-02T10:58:23Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2022-08-02T10:50:12Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - metrics: - type: mean_reward value: 92.70 +/- 7.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 --- # 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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
Tarkan/cikolata-finetuned-hastalik	Tarkan	2022-08-02T10:47:46Z	4	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "token-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-08-02T10:07:32Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Tarkan/cikolata-finetuned-hastalik 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. --> # Tarkan/cikolata-finetuned-hastalik 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: 0.1853 - Validation Loss: 0.0921 - Train Precision: 0.6410 - Train Recall: 0.7388 - Train F1: 0.6864 - Train Accuracy: 0.9686 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 339, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Precision \| Train Recall \| Train F1 \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:---------------:\|:------------:\|:--------:\|:--------------:\|:-----:\| \| 0.1853 \| 0.0921 \| 0.6410 \| 0.7388 \| 0.6864 \| 0.9686 \| 0 \| ### Framework versions - Transformers 4.21.0 - TensorFlow 2.8.2 - Datasets 2.4.0 - Tokenizers 0.12.1
JmPaunlagui/Improve	JmPaunlagui	2022-08-02T10:17:55Z	0	0	keras	[ "keras", "tf-keras", "region:us" ]	null	2022-08-02T09:42:09Z	--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: \| name \| learning_rate \| decay \| beta_1 \| beta_2 \| epsilon \| amsgrad \| training_precision \| \|----\|-------------\|-----\|------\|------\|-------\|-------\|------------------\| \|Adam\|0.001\|0.0\|0.9\|0.999\|1e-07\|False\|float32\|
DrY/bert-finetuned-squad	DrY	2022-08-02T10:16:43Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-08-02T07:52:45Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bert-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
gustavhartz/roberta-base-cuad-finetuned	gustavhartz	2022-08-02T09:11:38Z	320	1	transformers	[ "transformers", "pytorch", "roberta", "question-answering", "en", "dataset:cuad", "endpoints_compatible", "region:us" ]	question-answering	2022-06-26T13:51:35Z	--- language: en datasets: - cuad --- # Finetuned legal contract review QA model based 👩‍⚖️ 📑 Best model presented in the master thesis [Exploring CUAD using RoBERTa span-selection QA models for legal contract review](https://github.com/gustavhartz/transformers-legal-tasks) for QA on the Contract Understanding Atticus Dataset. Full training logic and associated thesis available through link. Outperform the most popular HF cuad model [Rakib/roberta-base-on-cuad](https://huggingface.co/Rakib/roberta-base-on-cuad) and is the best model for CUAD on Hugging Face 26/06/2022 \| Model name \| Top 1 Has Ans F1 \| Top 3 Has Ans F1 \| \|-----------------------------------------\|----------------------\|----------------------\| \| gustavhartz/roberta-base-cuad-finetuned \| 85.68 \| 94.06 \| \| Rakib/roberta-base-on-cuad \| 81.26 \| 92.48 \| For questions etc. go through the Github repo :) ### Citation If you found the code of thesis helpful you can please cite it :) ``` @thesis{ha2022, author = {Hartz, Gustav Selfort}, title = {Exploring CUAD using RoBERTa span-selection QA models for legal contract review}, language = {English}, format = {thesis}, year = {2022}, publisher = {DTU Department of Applied Mathematics and Computer Science} } ```
ysnow9876/alephbert-base-finetuned-for-shut	ysnow9876	2022-08-02T09:11:19Z	4	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "language model", "he", "dataset:responsa", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-30T11:28:30Z	--- language: - he tags: - language model datasets: - responsa --- AlephBERT-base-finetuned-for-shut Hebrew Language Model Based on alephbert-base: https://huggingface.co/onlplab/alephbert-base#alephbert How to use: from transformers import AutoModelForMaskedLM, AutoTokenizer checkpoint = 'ysnow9876/alephbert-base-finetuned-for-shut' tokenizer = AutoTokenizer.from_pretrained(checkpoint) model= AutoModelForMaskedLM.from_pretrained(checkpoint) #if not finetuning - disable dropout model.eval() Training Data about 26,000 different responsa from different rabbis from the past few hundred years
th1s1s1t/Reinforce-cartbole-v1	th1s1s1t	2022-08-02T09:07:16Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2022-08-02T09:02:44Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartbole-v1 results: - metrics: - type: mean_reward value: 255.40 +/- 10.01 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 --- # 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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
jinghan/roberta-base-finetuned-wnli	jinghan	2022-08-02T09:04:37Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-02T08:49:05Z	--- license: mit tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: roberta-base-finetuned-wnli results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: wnli split: train args: wnli metrics: - name: Accuracy type: accuracy value: 0.5633802816901409 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-finetuned-wnli This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6880 - Accuracy: 0.5634 ## Model description More information needed ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 40 \| 0.6880 \| 0.5634 \| \| No log \| 2.0 \| 80 \| 0.6851 \| 0.5634 \| \| No log \| 3.0 \| 120 \| 0.6961 \| 0.4366 \| \| No log \| 4.0 \| 160 \| 0.6906 \| 0.5634 \| \| No log \| 5.0 \| 200 \| 0.6891 \| 0.5634 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
commanderstrife/PV-Bio_clinicalBERT-superset	commanderstrife	2022-08-02T08:58:17Z	7	3	transformers	[ "transformers", "pytorch", "tensorboard", "electra", "token-classification", "generated_from_trainer", "dataset:pv_dataset", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-08-02T05:36:04Z	--- tags: - generated_from_trainer datasets: - pv_dataset metrics: - precision - recall - f1 - accuracy model-index: - name: PV-Bio_clinicalBERT-superset results: - task: name: Token Classification type: token-classification dataset: name: pv_dataset type: pv_dataset config: PVDatasetCorpus split: train args: PVDatasetCorpus metrics: - name: Precision type: precision value: 0.7055946686730801 - name: Recall type: recall value: 0.7473672226333467 - name: F1 type: f1 value: 0.7258804666334938 - name: Accuracy type: accuracy value: 0.9656573815513143 --- <!-- 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. --> # PV-Bio_clinicalBERT-superset This model is a fine-tuned version of [giacomomiolo/electramed_base_scivocab_1M](https://huggingface.co/giacomomiolo/electramed_base_scivocab_1M) on the pv_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.2082 - Precision: 0.7056 - Recall: 0.7474 - F1: 0.7259 - Accuracy: 0.9657 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.063 \| 1.0 \| 1813 \| 0.1061 \| 0.6453 \| 0.7306 \| 0.6853 \| 0.9623 \| \| 0.0086 \| 2.0 \| 3626 \| 0.1068 \| 0.6620 \| 0.7516 \| 0.7040 \| 0.9647 \| \| 0.0089 \| 3.0 \| 5439 \| 0.1265 \| 0.7026 \| 0.7300 \| 0.7160 \| 0.9657 \| \| 0.004 \| 4.0 \| 7252 \| 0.1369 \| 0.6820 \| 0.7601 \| 0.7189 \| 0.9638 \| \| 0.0004 \| 5.0 \| 9065 \| 0.1573 \| 0.6937 \| 0.7602 \| 0.7254 \| 0.9656 \| \| 0.0184 \| 6.0 \| 10878 \| 0.1707 \| 0.7078 \| 0.7475 \| 0.7271 \| 0.9662 \| \| 0.0009 \| 7.0 \| 12691 \| 0.1787 \| 0.7116 \| 0.7398 \| 0.7254 \| 0.9662 \| \| 0.0006 \| 8.0 \| 14504 \| 0.1874 \| 0.6979 \| 0.7576 \| 0.7265 \| 0.9655 \| \| 0.0008 \| 9.0 \| 16317 \| 0.1970 \| 0.7083 \| 0.7475 \| 0.7273 \| 0.9660 \| \| 0.0003 \| 10.0 \| 18130 \| 0.2082 \| 0.7056 \| 0.7474 \| 0.7259 \| 0.9657 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
silviacamplani/distilbert-base-uncased-finetuned-ner-conll2003_100train	silviacamplani	2022-08-02T08:55:52Z	4	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "token-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-08-02T08:54:21Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: silviacamplani/distilbert-base-uncased-finetuned-ner-conll2003_100train 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. --> # silviacamplani/distilbert-base-uncased-finetuned-ner-conll2003_100train 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: 1.4072 - Validation Loss: 1.4582 - Train Precision: 0.0 - Train Recall: 0.0 - Train F1: 0.0 - Train Accuracy: 0.7920 - 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Train Precision \| Train Recall \| Train F1 \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:---------------:\|:------------:\|:--------:\|:--------------:\|:-----:\| \| 2.0837 \| 1.8526 \| 0.0013 \| 0.0015 \| 0.0014 \| 0.7006 \| 0 \| \| 1.6450 \| 1.5672 \| 0.0 \| 0.0 \| 0.0 \| 0.7916 \| 1 \| \| 1.4072 \| 1.4582 \| 0.0 \| 0.0 \| 0.0 \| 0.7920 \| 2 \| ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1
ajders/nl_electra	ajders	2022-08-02T08:43:24Z	6	0	transformers	[ "transformers", "pytorch", "electra", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-07-07T13:48:39Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: nl_electra 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. --> # nl_electra This model is a pretrained version of [ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra) on the Dutch subset of the [CC100](https://huggingface.co/datasets/cc100) dataset. It achieves the following results on the evaluation set: - Loss: 2.4650 - Accuracy: 0.5392 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 703 - gradient_accumulation_steps: 32 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 8000 - num_epochs: 400.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:------:\|:---------------:\|:--------:\| \| No log \| 0.67 \| 500 \| 9.9977 \| 0.0486 \| \| No log \| 1.35 \| 1000 \| 9.5620 \| 0.0543 \| \| No log \| 2.02 \| 1500 \| 8.9306 \| 0.0741 \| \| No log \| 2.69 \| 2000 \| 8.2617 \| 0.0826 \| \| No log \| 3.36 \| 2500 \| 7.6880 \| 0.0792 \| \| No log \| 4.04 \| 3000 \| 7.3316 \| 0.0757 \| \| No log \| 4.71 \| 3500 \| 7.1944 \| 0.0747 \| \| No log \| 5.38 \| 4000 \| 7.1349 \| 0.0802 \| \| No log \| 6.06 \| 4500 \| 7.0752 \| 0.0887 \| \| 8.201 \| 6.73 \| 5000 \| 7.0046 \| 0.1021 \| \| 8.201 \| 7.4 \| 5500 \| 6.9295 \| 0.1090 \| \| 8.201 \| 8.08 \| 6000 \| 6.8483 \| 0.1132 \| \| 8.201 \| 8.75 \| 6500 \| 6.7750 \| 0.1171 \| \| 8.201 \| 9.42 \| 7000 \| 6.7116 \| 0.1187 \| \| 8.201 \| 10.09 \| 7500 \| 6.6560 \| 0.1218 \| \| 8.201 \| 10.77 \| 8000 \| 6.6178 \| 0.1239 \| \| 8.201 \| 11.44 \| 8500 \| 6.5824 \| 0.1255 \| \| 8.201 \| 12.11 \| 9000 \| 6.5521 \| 0.1273 \| \| 8.201 \| 12.79 \| 9500 \| 6.5203 \| 0.1292 \| \| 6.7257 \| 13.46 \| 10000 \| 6.5027 \| 0.1303 \| \| 6.7257 \| 14.13 \| 10500 \| 6.4809 \| 0.1314 \| \| 6.7257 \| 14.8 \| 11000 \| 6.4631 \| 0.1322 \| \| 6.7257 \| 15.48 \| 11500 \| 6.4483 \| 0.1329 \| \| 6.7257 \| 16.15 \| 12000 \| 6.4320 \| 0.1338 \| \| 6.7257 \| 16.82 \| 12500 \| 6.4169 \| 0.1348 \| \| 6.7257 \| 17.5 \| 13000 \| 6.4067 \| 0.1359 \| \| 6.7257 \| 18.17 \| 13500 \| 6.3994 \| 0.1359 \| \| 6.7257 \| 18.84 \| 14000 \| 6.3823 \| 0.1368 \| \| 6.7257 \| 19.52 \| 14500 \| 6.3759 \| 0.1373 \| \| 6.4502 \| 20.19 \| 15000 \| 6.3629 \| 0.1374 \| \| 6.4502 \| 20.86 \| 15500 \| 6.3638 \| 0.1373 \| \| 6.4502 \| 21.53 \| 16000 \| 6.3505 \| 0.1382 \| \| 6.4502 \| 22.21 \| 16500 \| 6.3416 \| 0.1387 \| \| 6.4502 \| 22.88 \| 17000 \| 6.3420 \| 0.1383 \| \| 6.4502 \| 23.55 \| 17500 \| 6.3330 \| 0.1389 \| \| 6.4502 \| 24.23 \| 18000 \| 6.3289 \| 0.1388 \| \| 6.4502 \| 24.9 \| 18500 \| 6.3184 \| 0.1389 \| \| 6.4502 \| 25.57 \| 19000 \| 6.3099 \| 0.1396 \| \| 6.4502 \| 26.24 \| 19500 \| 6.2789 \| 0.1405 \| \| 6.3474 \| 26.92 \| 20000 \| 6.2398 \| 0.1404 \| \| 6.3474 \| 27.59 \| 20500 \| 6.2012 \| 0.1412 \| \| 6.3474 \| 28.26 \| 21000 \| 6.1803 \| 0.1414 \| \| 6.3474 \| 28.94 \| 21500 \| 6.1579 \| 0.1414 \| \| 6.3474 \| 29.61 \| 22000 \| 6.1403 \| 0.1431 \| \| 6.3474 \| 30.28 \| 22500 \| 6.1316 \| 0.1423 \| \| 6.3474 \| 30.96 \| 23000 \| 6.1102 \| 0.1435 \| \| 6.3474 \| 31.63 \| 23500 \| 6.0998 \| 0.1439 \| \| 6.3474 \| 32.3 \| 24000 \| 6.0867 \| 0.1446 \| \| 6.3474 \| 32.97 \| 24500 \| 6.0700 \| 0.1451 \| \| 6.1758 \| 33.65 \| 25000 \| 6.0554 \| 0.1457 \| \| 6.1758 \| 34.32 \| 25500 \| 6.0487 \| 0.1457 \| \| 6.1758 \| 34.99 \| 26000 \| 6.0328 \| 0.1469 \| \| 6.1758 \| 35.67 \| 26500 \| 6.0265 \| 0.1469 \| \| 6.1758 \| 36.34 \| 27000 \| 5.9992 \| 0.1486 \| \| 6.1758 \| 37.01 \| 27500 \| 5.9934 \| 0.1485 \| \| 6.1758 \| 37.68 \| 28000 \| 5.9702 \| 0.1501 \| \| 6.1758 \| 38.36 \| 28500 \| 5.9661 \| 0.1503 \| \| 6.1758 \| 39.03 \| 29000 \| 5.9558 \| 0.1512 \| \| 6.1758 \| 39.7 \| 29500 \| 5.9321 \| 0.1528 \| \| 6.052 \| 40.38 \| 30000 \| 5.9147 \| 0.1532 \| \| 6.052 \| 41.05 \| 30500 \| 5.8975 \| 0.1545 \| \| 6.052 \| 41.72 \| 31000 \| 5.8784 \| 0.1566 \| \| 6.052 \| 42.4 \| 31500 \| 5.8584 \| 0.1586 \| \| 6.052 \| 43.07 \| 32000 \| 5.8325 \| 0.1603 \| \| 6.052 \| 43.74 \| 32500 \| 5.7583 \| 0.1664 \| \| 6.052 \| 44.41 \| 33000 \| 5.6158 \| 0.1787 \| \| 6.052 \| 45.09 \| 33500 \| 5.4580 \| 0.1917 \| \| 6.052 \| 45.76 \| 34000 \| 5.3396 \| 0.2010 \| \| 6.052 \| 46.43 \| 34500 \| 5.2568 \| 0.2082 \| \| 5.7995 \| 47.11 \| 35000 \| 5.1775 \| 0.2146 \| \| 5.7995 \| 47.78 \| 35500 \| 5.1076 \| 0.2204 \| \| 5.7995 \| 48.45 \| 36000 \| 5.0457 \| 0.2258 \| \| 5.7995 \| 49.13 \| 36500 \| 4.9932 \| 0.2313 \| \| 5.7995 \| 49.8 \| 37000 \| 4.9164 \| 0.2384 \| \| 5.7995 \| 50.47 \| 37500 \| 4.7844 \| 0.2521 \| \| 5.7995 \| 51.14 \| 38000 \| 4.6598 \| 0.2642 \| \| 5.7995 \| 51.82 \| 38500 \| 4.5472 \| 0.2757 \| \| 5.7995 \| 52.49 \| 39000 \| 4.4374 \| 0.2871 \| \| 5.7995 \| 53.16 \| 39500 \| 4.3399 \| 0.2982 \| \| 5.0341 \| 53.84 \| 40000 \| 4.2549 \| 0.3083 \| \| 5.0341 \| 54.51 \| 40500 \| 4.1795 \| 0.3170 \| \| 5.0341 \| 55.18 \| 41000 \| 4.1017 \| 0.3274 \| \| 5.0341 \| 55.85 \| 41500 \| 4.0308 \| 0.3375 \| \| 5.0341 \| 56.53 \| 42000 \| 3.9673 \| 0.3462 \| \| 5.0341 \| 57.2 \| 42500 \| 3.9130 \| 0.3538 \| \| 5.0341 \| 57.87 \| 43000 \| 3.8672 \| 0.3599 \| \| 5.0341 \| 58.55 \| 43500 \| 3.8249 \| 0.3656 \| \| 5.0341 \| 59.22 \| 44000 \| 3.7748 \| 0.3728 \| \| 5.0341 \| 59.89 \| 44500 \| 3.7459 \| 0.3768 \| \| 4.2119 \| 60.57 \| 45000 \| 3.7089 \| 0.3808 \| \| 4.2119 \| 61.24 \| 45500 \| 3.6732 \| 0.3857 \| \| 4.2119 \| 61.91 \| 46000 \| 3.6546 \| 0.3881 \| \| 4.2119 \| 62.58 \| 46500 \| 3.6205 \| 0.3921 \| \| 4.2119 \| 63.26 \| 47000 \| 3.5908 \| 0.3960 \| \| 4.2119 \| 63.93 \| 47500 \| 3.5627 \| 0.3991 \| \| 4.2119 \| 64.6 \| 48000 \| 3.5466 \| 0.4019 \| \| 4.2119 \| 65.28 \| 48500 \| 3.5262 \| 0.4039 \| \| 4.2119 \| 65.95 \| 49000 \| 3.4987 \| 0.4074 \| \| 4.2119 \| 66.62 \| 49500 \| 3.4817 \| 0.4093 \| \| 3.8182 \| 67.29 \| 50000 \| 3.4608 \| 0.4119 \| \| 3.8182 \| 67.97 \| 50500 \| 3.4467 \| 0.4142 \| \| 3.8182 \| 68.64 \| 51000 \| 3.4280 \| 0.4163 \| \| 3.8182 \| 69.31 \| 51500 \| 3.4165 \| 0.4175 \| \| 3.8182 \| 69.99 \| 52000 \| 3.3970 \| 0.4199 \| \| 3.8182 \| 70.66 \| 52500 \| 3.3738 \| 0.4227 \| \| 3.8182 \| 71.33 \| 53000 \| 3.3640 \| 0.4242 \| \| 3.8182 \| 72.01 \| 53500 \| 3.3583 \| 0.4252 \| \| 3.8182 \| 72.68 \| 54000 \| 3.3319 \| 0.4279 \| \| 3.8182 \| 73.35 \| 54500 \| 3.3153 \| 0.4303 \| \| 3.5946 \| 74.02 \| 55000 \| 3.3098 \| 0.4304 \| \| 3.5946 \| 74.7 \| 55500 \| 3.2949 \| 0.4328 \| \| 3.5946 \| 75.37 \| 56000 \| 3.2820 \| 0.4335 \| \| 3.5946 \| 76.04 \| 56500 \| 3.2686 \| 0.4355 \| \| 3.5946 \| 76.72 \| 57000 \| 3.2663 \| 0.4359 \| \| 3.5946 \| 77.39 \| 57500 \| 3.2482 \| 0.4379 \| \| 3.5946 \| 78.06 \| 58000 \| 3.2344 \| 0.4393 \| \| 3.5946 \| 78.73 \| 58500 \| 3.2281 \| 0.4407 \| \| 3.5946 \| 79.41 \| 59000 \| 3.2172 \| 0.4412 \| \| 3.5946 \| 80.08 \| 59500 \| 3.2110 \| 0.4420 \| \| 3.4435 \| 80.75 \| 60000 \| 3.1940 \| 0.4443 \| \| 3.4435 \| 81.43 \| 60500 \| 3.1837 \| 0.4455 \| \| 3.4435 \| 82.1 \| 61000 \| 3.1744 \| 0.4469 \| \| 3.4435 \| 82.77 \| 61500 \| 3.1611 \| 0.4483 \| \| 3.4435 \| 83.45 \| 62000 \| 3.1531 \| 0.4496 \| \| 3.4435 \| 84.12 \| 62500 \| 3.1524 \| 0.4499 \| \| 3.4435 \| 84.79 \| 63000 \| 3.1431 \| 0.4501 \| \| 3.4435 \| 85.46 \| 63500 \| 3.1287 \| 0.4527 \| \| 3.4435 \| 86.14 \| 64000 \| 3.1192 \| 0.4533 \| \| 3.4435 \| 86.81 \| 64500 \| 3.1107 \| 0.4547 \| \| 3.3301 \| 87.48 \| 65000 \| 3.1041 \| 0.4553 \| \| 3.3301 \| 88.16 \| 65500 \| 3.0999 \| 0.4562 \| \| 3.3301 \| 88.83 \| 66000 \| 3.0882 \| 0.4576 \| \| 3.3301 \| 89.5 \| 66500 \| 3.0777 \| 0.4589 \| \| 3.3301 \| 90.17 \| 67000 \| 3.0726 \| 0.4588 \| \| 3.3301 \| 90.85 \| 67500 \| 3.0676 \| 0.4601 \| \| 3.3301 \| 91.52 \| 68000 \| 3.0616 \| 0.4602 \| \| 3.3301 \| 92.19 \| 68500 \| 3.0523 \| 0.4621 \| \| 3.3301 \| 92.87 \| 69000 \| 3.0464 \| 0.4624 \| \| 3.3301 \| 93.54 \| 69500 \| 3.0405 \| 0.4635 \| \| 3.2418 \| 94.21 \| 70000 \| 3.0312 \| 0.4649 \| \| 3.2418 \| 94.89 \| 70500 \| 3.0209 \| 0.4653 \| \| 3.2418 \| 95.56 \| 71000 \| 3.0202 \| 0.4657 \| \| 3.2418 \| 96.23 \| 71500 \| 3.0101 \| 0.4676 \| \| 3.2418 \| 96.9 \| 72000 \| 3.0105 \| 0.4666 \| \| 3.2418 \| 97.58 \| 72500 \| 3.0023 \| 0.4685 \| \| 3.2418 \| 98.25 \| 73000 \| 3.0008 \| 0.4680 \| \| 3.2418 \| 98.92 \| 73500 \| 2.9882 \| 0.4691 \| \| 3.2418 \| 99.6 \| 74000 \| 2.9855 \| 0.4702 \| \| 3.2418 \| 100.27 \| 74500 \| 2.9790 \| 0.4709 \| \| 3.1698 \| 100.94 \| 75000 \| 2.9680 \| 0.4716 \| \| 3.1698 \| 101.61 \| 75500 \| 2.9667 \| 0.4724 \| \| 3.1698 \| 102.29 \| 76000 \| 2.9657 \| 0.4726 \| \| 3.1698 \| 102.96 \| 76500 \| 2.9623 \| 0.4731 \| \| 3.1698 \| 103.63 \| 77000 \| 2.9515 \| 0.4745 \| \| 3.1698 \| 104.31 \| 77500 \| 2.9471 \| 0.4753 \| \| 3.1698 \| 104.98 \| 78000 \| 2.9407 \| 0.4756 \| \| 3.1698 \| 105.65 \| 78500 \| 2.9388 \| 0.4761 \| \| 3.1698 \| 106.33 \| 79000 \| 2.9369 \| 0.4766 \| \| 3.1698 \| 107.0 \| 79500 \| 2.9297 \| 0.4762 \| \| 3.1101 \| 107.67 \| 80000 \| 2.9291 \| 0.4776 \| \| 3.1101 \| 108.34 \| 80500 \| 2.9139 \| 0.4788 \| \| 3.1101 \| 109.02 \| 81000 \| 2.9113 \| 0.4790 \| \| 3.1101 \| 109.69 \| 81500 \| 2.9057 \| 0.4798 \| \| 3.1101 \| 110.36 \| 82000 \| 2.9058 \| 0.4804 \| \| 3.1101 \| 111.04 \| 82500 \| 2.9019 \| 0.4807 \| \| 3.1101 \| 111.71 \| 83000 \| 2.8934 \| 0.4818 \| \| 3.1101 \| 112.38 \| 83500 \| 2.8864 \| 0.4825 \| \| 3.1101 \| 113.06 \| 84000 \| 2.8926 \| 0.4815 \| \| 3.1101 \| 113.73 \| 84500 \| 2.8812 \| 0.4830 \| \| 3.058 \| 114.4 \| 85000 \| 2.8740 \| 0.4840 \| \| 3.058 \| 115.07 \| 85500 \| 2.8730 \| 0.4840 \| \| 3.058 \| 115.75 \| 86000 \| 2.8694 \| 0.4847 \| \| 3.058 \| 116.42 \| 86500 \| 2.8694 \| 0.4848 \| \| 3.058 \| 117.09 \| 87000 \| 2.8601 \| 0.4862 \| \| 3.058 \| 117.77 \| 87500 \| 2.8547 \| 0.4862 \| \| 3.058 \| 118.44 \| 88000 \| 2.8538 \| 0.4861 \| \| 3.058 \| 119.11 \| 88500 \| 2.8494 \| 0.4876 \| \| 3.058 \| 119.78 \| 89000 \| 2.8430 \| 0.4882 \| \| 3.058 \| 120.46 \| 89500 \| 2.8436 \| 0.4875 \| \| 3.0129 \| 121.13 \| 90000 \| 2.8402 \| 0.4884 \| \| 3.0129 \| 121.8 \| 90500 \| 2.8353 \| 0.4888 \| \| 3.0129 \| 122.48 \| 91000 \| 2.8271 \| 0.4896 \| \| 3.0129 \| 123.15 \| 91500 \| 2.8236 \| 0.4900 \| \| 3.0129 \| 123.82 \| 92000 \| 2.8199 \| 0.4913 \| \| 3.0129 \| 124.5 \| 92500 \| 2.8119 \| 0.4916 \| \| 3.0129 \| 125.17 \| 93000 \| 2.8138 \| 0.4916 \| \| 3.0129 \| 125.84 \| 93500 \| 2.8089 \| 0.4925 \| \| 3.0129 \| 126.51 \| 94000 \| 2.8067 \| 0.4928 \| \| 3.0129 \| 127.19 \| 94500 \| 2.8010 \| 0.4939 \| \| 2.9701 \| 127.86 \| 95000 \| 2.7992 \| 0.4938 \| \| 2.9701 \| 128.53 \| 95500 \| 2.7953 \| 0.4948 \| \| 2.9701 \| 129.21 \| 96000 \| 2.7964 \| 0.4942 \| \| 2.9701 \| 129.88 \| 96500 \| 2.7838 \| 0.4955 \| \| 2.9701 \| 130.55 \| 97000 \| 2.7770 \| 0.4968 \| \| 2.9701 \| 131.22 \| 97500 \| 2.7800 \| 0.4962 \| \| 2.9701 \| 131.9 \| 98000 \| 2.7743 \| 0.4972 \| \| 2.9701 \| 132.57 \| 98500 \| 2.7696 \| 0.4973 \| \| 2.9701 \| 133.24 \| 99000 \| 2.7691 \| 0.4980 \| \| 2.9701 \| 133.92 \| 99500 \| 2.7612 \| 0.4989 \| \| 2.9289 \| 134.59 \| 100000 \| 2.7606 \| 0.4987 \| \| 2.9289 \| 135.26 \| 100500 \| 2.7545 \| 0.4993 \| \| 2.9289 \| 135.94 \| 101000 \| 2.7544 \| 0.4999 \| \| 2.9289 \| 136.61 \| 101500 \| 2.7550 \| 0.4999 \| \| 2.9289 \| 137.28 \| 102000 \| 2.7510 \| 0.5001 \| \| 2.9289 \| 137.95 \| 102500 \| 2.7480 \| 0.5002 \| \| 2.9289 \| 138.63 \| 103000 \| 2.7422 \| 0.5012 \| \| 2.9289 \| 139.3 \| 103500 \| 2.7419 \| 0.5014 \| \| 2.9289 \| 139.97 \| 104000 \| 2.7416 \| 0.5009 \| \| 2.9289 \| 140.65 \| 104500 \| 2.7412 \| 0.5017 \| \| 2.8968 \| 141.32 \| 105000 \| 2.7356 \| 0.5023 \| \| 2.8968 \| 141.99 \| 105500 \| 2.7303 \| 0.5027 \| \| 2.8968 \| 142.66 \| 106000 \| 2.7359 \| 0.5029 \| \| 2.8968 \| 143.34 \| 106500 \| 2.7283 \| 0.5032 \| \| 2.8968 \| 144.01 \| 107000 \| 2.7226 \| 0.5033 \| \| 2.8968 \| 144.68 \| 107500 \| 2.7247 \| 0.5039 \| \| 2.8968 \| 145.36 \| 108000 \| 2.7209 \| 0.5044 \| \| 2.8968 \| 146.03 \| 108500 \| 2.7210 \| 0.5039 \| \| 2.8968 \| 146.7 \| 109000 \| 2.7135 \| 0.5046 \| \| 2.8968 \| 147.38 \| 109500 \| 2.7139 \| 0.5048 \| \| 2.8697 \| 148.05 \| 110000 \| 2.7167 \| 0.5050 \| \| 2.8697 \| 148.72 \| 110500 \| 2.7125 \| 0.5058 \| \| 2.8697 \| 149.39 \| 111000 \| 2.7064 \| 0.5060 \| \| 2.8697 \| 150.07 \| 111500 \| 2.7024 \| 0.5067 \| \| 2.8697 \| 150.74 \| 112000 \| 2.7035 \| 0.5067 \| \| 2.8697 \| 151.41 \| 112500 \| 2.7034 \| 0.5067 \| \| 2.8697 \| 152.09 \| 113000 \| 2.6967 \| 0.5073 \| \| 2.8697 \| 152.76 \| 113500 \| 2.6982 \| 0.5070 \| \| 2.8697 \| 153.43 \| 114000 \| 2.6948 \| 0.5079 \| \| 2.8697 \| 154.1 \| 114500 \| 2.6946 \| 0.5076 \| \| 2.8457 \| 154.78 \| 115000 \| 2.6918 \| 0.5078 \| \| 2.8457 \| 155.45 \| 115500 \| 2.6917 \| 0.5078 \| \| 2.8457 \| 156.12 \| 116000 \| 2.6868 \| 0.5086 \| \| 2.8457 \| 156.8 \| 116500 \| 2.6870 \| 0.5084 \| \| 2.8457 \| 157.47 \| 117000 \| 2.6830 \| 0.5091 \| \| 2.8457 \| 158.14 \| 117500 \| 2.6824 \| 0.5090 \| \| 2.8457 \| 158.82 \| 118000 \| 2.6812 \| 0.5092 \| \| 2.8457 \| 159.49 \| 118500 \| 2.6747 \| 0.5098 \| \| 2.8457 \| 160.16 \| 119000 \| 2.6747 \| 0.5105 \| \| 2.8457 \| 160.83 \| 119500 \| 2.6750 \| 0.5102 \| \| 2.825 \| 161.51 \| 120000 \| 2.6761 \| 0.5102 \| \| 2.825 \| 162.18 \| 120500 \| 2.6670 \| 0.5115 \| \| 2.825 \| 162.85 \| 121000 \| 2.6740 \| 0.5104 \| \| 2.825 \| 163.53 \| 121500 \| 2.6700 \| 0.5106 \| \| 2.825 \| 164.2 \| 122000 \| 2.6629 \| 0.5119 \| \| 2.825 \| 164.87 \| 122500 \| 2.6642 \| 0.5117 \| \| 2.825 \| 165.54 \| 123000 \| 2.6664 \| 0.5109 \| \| 2.825 \| 166.22 \| 123500 \| 2.6626 \| 0.5117 \| \| 2.825 \| 166.89 \| 124000 \| 2.6561 \| 0.5130 \| \| 2.825 \| 167.56 \| 124500 \| 2.6612 \| 0.5125 \| \| 2.8059 \| 168.24 \| 125000 \| 2.6594 \| 0.5123 \| \| 2.8059 \| 168.91 \| 125500 \| 2.6508 \| 0.5132 \| \| 2.8059 \| 169.58 \| 126000 \| 2.6477 \| 0.5134 \| \| 2.8059 \| 170.26 \| 126500 \| 2.6527 \| 0.5133 \| \| 2.8059 \| 170.93 \| 127000 \| 2.6519 \| 0.5136 \| \| 2.8059 \| 171.6 \| 127500 \| 2.6456 \| 0.5141 \| \| 2.8059 \| 172.27 \| 128000 \| 2.6473 \| 0.5139 \| \| 2.8059 \| 172.95 \| 128500 \| 2.6426 \| 0.5144 \| \| 2.8059 \| 173.62 \| 129000 \| 2.6454 \| 0.5137 \| \| 2.8059 \| 174.29 \| 129500 \| 2.6427 \| 0.5147 \| \| 2.788 \| 174.97 \| 130000 \| 2.6376 \| 0.5150 \| \| 2.788 \| 175.64 \| 130500 \| 2.6366 \| 0.5154 \| \| 2.788 \| 176.31 \| 131000 \| 2.6327 \| 0.5156 \| \| 2.788 \| 176.98 \| 131500 \| 2.6328 \| 0.5157 \| \| 2.788 \| 177.66 \| 132000 \| 2.6335 \| 0.5156 \| \| 2.788 \| 178.33 \| 132500 \| 2.6302 \| 0.5166 \| \| 2.788 \| 179.0 \| 133000 \| 2.6333 \| 0.5160 \| \| 2.788 \| 179.68 \| 133500 \| 2.6253 \| 0.5171 \| \| 2.788 \| 180.35 \| 134000 \| 2.6237 \| 0.5167 \| \| 2.788 \| 181.02 \| 134500 \| 2.6269 \| 0.5165 \| \| 2.7723 \| 181.7 \| 135000 \| 2.6283 \| 0.5164 \| \| 2.7723 \| 182.37 \| 135500 \| 2.6255 \| 0.5174 \| \| 2.7723 \| 183.04 \| 136000 \| 2.6254 \| 0.5175 \| \| 2.7723 \| 183.71 \| 136500 \| 2.6231 \| 0.5172 \| \| 2.7723 \| 184.39 \| 137000 \| 2.6181 \| 0.5173 \| \| 2.7723 \| 185.06 \| 137500 \| 2.6260 \| 0.5168 \| \| 2.7723 \| 185.73 \| 138000 \| 2.6183 \| 0.5176 \| \| 2.7723 \| 186.41 \| 138500 \| 2.6174 \| 0.5182 \| \| 2.7723 \| 187.08 \| 139000 \| 2.6144 \| 0.5182 \| \| 2.7723 \| 187.75 \| 139500 \| 2.6152 \| 0.5186 \| \| 2.7575 \| 188.43 \| 140000 \| 2.6150 \| 0.5183 \| \| 2.7575 \| 189.1 \| 140500 \| 2.6110 \| 0.5190 \| \| 2.7575 \| 189.77 \| 141000 \| 2.6044 \| 0.5194 \| \| 2.7575 \| 190.44 \| 141500 \| 2.6083 \| 0.5186 \| \| 2.7575 \| 191.12 \| 142000 \| 2.6102 \| 0.5189 \| \| 2.7575 \| 191.79 \| 142500 \| 2.6063 \| 0.5195 \| \| 2.7575 \| 192.46 \| 143000 \| 2.6071 \| 0.5198 \| \| 2.7575 \| 193.14 \| 143500 \| 2.6090 \| 0.5191 \| \| 2.7575 \| 193.81 \| 144000 \| 2.6068 \| 0.5200 \| \| 2.7575 \| 194.48 \| 144500 \| 2.6032 \| 0.5200 \| \| 2.7445 \| 195.15 \| 145000 \| 2.6031 \| 0.5200 \| \| 2.7445 \| 195.83 \| 145500 \| 2.6007 \| 0.5201 \| \| 2.7445 \| 196.5 \| 146000 \| 2.5998 \| 0.5203 \| \| 2.7445 \| 197.17 \| 146500 \| 2.5980 \| 0.5208 \| \| 2.7445 \| 197.85 \| 147000 \| 2.5952 \| 0.5211 \| \| 2.7445 \| 198.52 \| 147500 \| 2.5977 \| 0.5210 \| \| 2.7445 \| 199.19 \| 148000 \| 2.5922 \| 0.5212 \| \| 2.7445 \| 199.87 \| 148500 \| 2.5936 \| 0.5211 \| \| 2.7445 \| 200.54 \| 149000 \| 2.5933 \| 0.5219 \| \| 2.7445 \| 201.21 \| 149500 \| 2.5887 \| 0.5219 \| \| 2.7324 \| 201.88 \| 150000 \| 2.5911 \| 0.5215 \| \| 2.7324 \| 202.56 \| 150500 \| 2.5892 \| 0.5219 \| \| 2.7324 \| 203.23 \| 151000 \| 2.5875 \| 0.5218 \| \| 2.7324 \| 203.9 \| 151500 \| 2.5898 \| 0.5220 \| \| 2.7324 \| 204.58 \| 152000 \| 2.5872 \| 0.5223 \| \| 2.7324 \| 205.25 \| 152500 \| 2.5805 \| 0.5226 \| \| 2.7324 \| 205.92 \| 153000 \| 2.5861 \| 0.5225 \| \| 2.7324 \| 206.59 \| 153500 \| 2.5839 \| 0.5223 \| \| 2.7324 \| 207.27 \| 154000 \| 2.5804 \| 0.5232 \| \| 2.7324 \| 207.94 \| 154500 \| 2.5766 \| 0.5235 \| \| 2.7212 \| 208.61 \| 155000 \| 2.5764 \| 0.5233 \| \| 2.7212 \| 209.29 \| 155500 \| 2.5801 \| 0.5233 \| \| 2.7212 \| 209.96 \| 156000 \| 2.5737 \| 0.5241 \| \| 2.7212 \| 210.63 \| 156500 \| 2.5769 \| 0.5236 \| \| 2.7212 \| 211.31 \| 157000 \| 2.5769 \| 0.5237 \| \| 2.7212 \| 211.98 \| 157500 \| 2.5748 \| 0.5239 \| \| 2.7212 \| 212.65 \| 158000 \| 2.5745 \| 0.5230 \| \| 2.7212 \| 213.32 \| 158500 \| 2.5725 \| 0.5240 \| \| 2.7212 \| 214.0 \| 159000 \| 2.5736 \| 0.5239 \| \| 2.7212 \| 214.67 \| 159500 \| 2.5675 \| 0.5252 \| \| 2.7103 \| 215.34 \| 160000 \| 2.5678 \| 0.5245 \| \| 2.7103 \| 216.02 \| 160500 \| 2.5691 \| 0.5250 \| \| 2.7103 \| 216.69 \| 161000 \| 2.5688 \| 0.5245 \| \| 2.7103 \| 217.36 \| 161500 \| 2.5681 \| 0.5251 \| \| 2.7103 \| 218.03 \| 162000 \| 2.5582 \| 0.5255 \| \| 2.7103 \| 218.71 \| 162500 \| 2.5675 \| 0.5247 \| \| 2.7103 \| 219.38 \| 163000 \| 2.5609 \| 0.5259 \| \| 2.7103 \| 220.05 \| 163500 \| 2.5625 \| 0.5254 \| \| 2.7103 \| 220.73 \| 164000 \| 2.5630 \| 0.5254 \| \| 2.7103 \| 221.4 \| 164500 \| 2.5607 \| 0.5265 \| \| 2.7003 \| 222.07 \| 165000 \| 2.5615 \| 0.5260 \| \| 2.7003 \| 222.75 \| 165500 \| 2.5660 \| 0.5248 \| \| 2.7003 \| 223.42 \| 166000 \| 2.5569 \| 0.5263 \| \| 2.7003 \| 224.09 \| 166500 \| 2.5610 \| 0.5255 \| \| 2.7003 \| 224.76 \| 167000 \| 2.5569 \| 0.5263 \| \| 2.7003 \| 225.44 \| 167500 \| 2.5534 \| 0.5265 \| \| 2.7003 \| 226.11 \| 168000 \| 2.5573 \| 0.5259 \| \| 2.7003 \| 226.78 \| 168500 \| 2.5559 \| 0.5264 \| \| 2.7003 \| 227.46 \| 169000 \| 2.5508 \| 0.5277 \| \| 2.7003 \| 228.13 \| 169500 \| 2.5500 \| 0.5276 \| \| 2.6915 \| 228.8 \| 170000 \| 2.5501 \| 0.5270 \| \| 2.6915 \| 229.47 \| 170500 \| 2.5508 \| 0.5273 \| \| 2.6915 \| 230.15 \| 171000 \| 2.5523 \| 0.5267 \| \| 2.6915 \| 230.82 \| 171500 \| 2.5464 \| 0.5276 \| \| 2.6915 \| 231.49 \| 172000 \| 2.5482 \| 0.5271 \| \| 2.6915 \| 232.17 \| 172500 \| 2.5486 \| 0.5270 \| \| 2.6915 \| 232.84 \| 173000 \| 2.5474 \| 0.5275 \| \| 2.6915 \| 233.51 \| 173500 \| 2.5483 \| 0.5270 \| \| 2.6915 \| 234.19 \| 174000 \| 2.5480 \| 0.5276 \| \| 2.6915 \| 234.86 \| 174500 \| 2.5486 \| 0.5278 \| \| 2.6833 \| 235.53 \| 175000 \| 2.5484 \| 0.5273 \| \| 2.6833 \| 236.2 \| 175500 \| 2.5436 \| 0.5277 \| \| 2.6833 \| 236.88 \| 176000 \| 2.5448 \| 0.5278 \| \| 2.6833 \| 237.55 \| 176500 \| 2.5430 \| 0.5284 \| \| 2.6833 \| 238.22 \| 177000 \| 2.5433 \| 0.5279 \| \| 2.6833 \| 238.9 \| 177500 \| 2.5398 \| 0.5288 \| \| 2.6833 \| 239.57 \| 178000 \| 2.5424 \| 0.5282 \| \| 2.6833 \| 240.24 \| 178500 \| 2.5371 \| 0.5291 \| \| 2.6833 \| 240.91 \| 179000 \| 2.5372 \| 0.5294 \| \| 2.6833 \| 241.59 \| 179500 \| 2.5368 \| 0.5290 \| \| 2.6753 \| 242.26 \| 180000 \| 2.5383 \| 0.5289 \| \| 2.6753 \| 242.93 \| 180500 \| 2.5387 \| 0.5289 \| \| 2.6753 \| 243.61 \| 181000 \| 2.5351 \| 0.5295 \| \| 2.6753 \| 244.28 \| 181500 \| 2.5340 \| 0.5296 \| \| 2.6753 \| 244.95 \| 182000 \| 2.5349 \| 0.5289 \| \| 2.6753 \| 245.63 \| 182500 \| 2.5358 \| 0.5295 \| \| 2.6753 \| 246.3 \| 183000 \| 2.5333 \| 0.5299 \| \| 2.6753 \| 246.97 \| 183500 \| 2.5363 \| 0.5292 \| \| 2.6753 \| 247.64 \| 184000 \| 2.5323 \| 0.5298 \| \| 2.6753 \| 248.32 \| 184500 \| 2.5286 \| 0.5299 \| \| 2.6679 \| 248.99 \| 185000 \| 2.5276 \| 0.5306 \| \| 2.6679 \| 249.66 \| 185500 \| 2.5249 \| 0.5308 \| \| 2.6679 \| 250.34 \| 186000 \| 2.5308 \| 0.5302 \| \| 2.6679 \| 251.01 \| 186500 \| 2.5307 \| 0.5297 \| \| 2.6679 \| 251.68 \| 187000 \| 2.5293 \| 0.5305 \| \| 2.6679 \| 252.36 \| 187500 \| 2.5255 \| 0.5306 \| \| 2.6679 \| 253.03 \| 188000 \| 2.5244 \| 0.5312 \| \| 2.6679 \| 253.7 \| 188500 \| 2.5278 \| 0.5305 \| \| 2.6679 \| 254.37 \| 189000 \| 2.5212 \| 0.5317 \| \| 2.6679 \| 255.05 \| 189500 \| 2.5256 \| 0.5307 \| \| 2.6611 \| 255.72 \| 190000 \| 2.5273 \| 0.5303 \| \| 2.6611 \| 256.39 \| 190500 \| 2.5222 \| 0.5310 \| \| 2.6611 \| 257.07 \| 191000 \| 2.5237 \| 0.5311 \| \| 2.6611 \| 257.74 \| 191500 \| 2.5258 \| 0.5309 \| \| 2.6611 \| 258.41 \| 192000 \| 2.5219 \| 0.5313 \| \| 2.6611 \| 259.08 \| 192500 \| 2.5243 \| 0.5314 \| \| 2.6611 \| 259.76 \| 193000 \| 2.5203 \| 0.5319 \| \| 2.6611 \| 260.43 \| 193500 \| 2.5205 \| 0.5313 \| \| 2.6611 \| 261.1 \| 194000 \| 2.5205 \| 0.5322 \| \| 2.6611 \| 261.78 \| 194500 \| 2.5196 \| 0.5317 \| \| 2.655 \| 262.45 \| 195000 \| 2.5199 \| 0.5315 \| \| 2.655 \| 263.12 \| 195500 \| 2.5226 \| 0.5315 \| \| 2.655 \| 263.8 \| 196000 \| 2.5175 \| 0.5316 \| \| 2.655 \| 264.47 \| 196500 \| 2.5160 \| 0.5322 \| \| 2.655 \| 265.14 \| 197000 \| 2.5185 \| 0.5316 \| \| 2.655 \| 265.81 \| 197500 \| 2.5133 \| 0.5322 \| \| 2.655 \| 266.49 \| 198000 \| 2.5163 \| 0.5318 \| \| 2.655 \| 267.16 \| 198500 \| 2.5135 \| 0.5325 \| \| 2.655 \| 267.83 \| 199000 \| 2.5132 \| 0.5326 \| \| 2.655 \| 268.51 \| 199500 \| 2.5148 \| 0.5323 \| \| 2.6486 \| 269.18 \| 200000 \| 2.5194 \| 0.5317 \| \| 2.6486 \| 269.85 \| 200500 \| 2.5162 \| 0.5321 \| \| 2.6486 \| 270.52 \| 201000 \| 2.5090 \| 0.5332 \| \| 2.6486 \| 271.2 \| 201500 \| 2.5126 \| 0.5325 \| \| 2.6486 \| 271.87 \| 202000 \| 2.5155 \| 0.5320 \| \| 2.6486 \| 272.54 \| 202500 \| 2.5099 \| 0.5329 \| \| 2.6486 \| 273.22 \| 203000 \| 2.5130 \| 0.5325 \| \| 2.6486 \| 273.89 \| 203500 \| 2.5064 \| 0.5329 \| \| 2.6486 \| 274.56 \| 204000 \| 2.5154 \| 0.5319 \| \| 2.6486 \| 275.24 \| 204500 \| 2.5097 \| 0.5329 \| \| 2.6433 \| 275.91 \| 205000 \| 2.5075 \| 0.5334 \| \| 2.6433 \| 276.58 \| 205500 \| 2.5120 \| 0.5325 \| \| 2.6433 \| 277.25 \| 206000 \| 2.5100 \| 0.5329 \| \| 2.6433 \| 277.93 \| 206500 \| 2.5115 \| 0.5332 \| \| 2.6433 \| 278.6 \| 207000 \| 2.5071 \| 0.5332 \| \| 2.6433 \| 279.27 \| 207500 \| 2.5075 \| 0.5335 \| \| 2.6433 \| 279.95 \| 208000 \| 2.5020 \| 0.5338 \| \| 2.6433 \| 280.62 \| 208500 \| 2.5025 \| 0.5340 \| \| 2.6433 \| 281.29 \| 209000 \| 2.5064 \| 0.5333 \| \| 2.6433 \| 281.96 \| 209500 \| 2.5038 \| 0.5336 \| \| 2.6383 \| 282.64 \| 210000 \| 2.5041 \| 0.5340 \| \| 2.6383 \| 283.31 \| 210500 \| 2.5075 \| 0.5336 \| \| 2.6383 \| 283.98 \| 211000 \| 2.5028 \| 0.5333 \| \| 2.6383 \| 284.66 \| 211500 \| 2.5008 \| 0.5340 \| \| 2.6383 \| 285.33 \| 212000 \| 2.5005 \| 0.5345 \| \| 2.6383 \| 286.0 \| 212500 \| 2.5020 \| 0.5334 \| \| 2.6383 \| 286.68 \| 213000 \| 2.5011 \| 0.5344 \| \| 2.6383 \| 287.35 \| 213500 \| 2.5028 \| 0.5338 \| \| 2.6383 \| 288.02 \| 214000 \| 2.4970 \| 0.5340 \| \| 2.6383 \| 288.69 \| 214500 \| 2.4995 \| 0.5343 \| \| 2.6336 \| 289.37 \| 215000 \| 2.5010 \| 0.5343 \| \| 2.6336 \| 290.04 \| 215500 \| 2.5060 \| 0.5336 \| \| 2.6336 \| 290.71 \| 216000 \| 2.4955 \| 0.5347 \| \| 2.6336 \| 291.39 \| 216500 \| 2.4972 \| 0.5349 \| \| 2.6336 \| 292.06 \| 217000 \| 2.4977 \| 0.5349 \| \| 2.6336 \| 292.73 \| 217500 \| 2.4973 \| 0.5346 \| \| 2.6336 \| 293.4 \| 218000 \| 2.4981 \| 0.5346 \| \| 2.6336 \| 294.08 \| 218500 \| 2.4941 \| 0.5346 \| \| 2.6336 \| 294.75 \| 219000 \| 2.4978 \| 0.5350 \| \| 2.6336 \| 295.42 \| 219500 \| 2.4990 \| 0.5343 \| \| 2.6288 \| 296.1 \| 220000 \| 2.4929 \| 0.5347 \| \| 2.6288 \| 296.77 \| 220500 \| 2.4937 \| 0.5349 \| \| 2.6288 \| 297.44 \| 221000 \| 2.4938 \| 0.5349 \| \| 2.6288 \| 298.12 \| 221500 \| 2.4938 \| 0.5343 \| \| 2.6288 \| 298.79 \| 222000 \| 2.4924 \| 0.5354 \| \| 2.6288 \| 299.46 \| 222500 \| 2.4953 \| 0.5348 \| \| 2.6288 \| 300.13 \| 223000 \| 2.4930 \| 0.5347 \| \| 2.6288 \| 300.81 \| 223500 \| 2.4934 \| 0.5353 \| \| 2.6288 \| 301.48 \| 224000 \| 2.4942 \| 0.5348 \| \| 2.6288 \| 302.15 \| 224500 \| 2.4960 \| 0.5344 \| \| 2.6246 \| 302.83 \| 225000 \| 2.4875 \| 0.5357 \| \| 2.6246 \| 303.5 \| 225500 \| 2.4898 \| 0.5355 \| \| 2.6246 \| 304.17 \| 226000 \| 2.4847 \| 0.5366 \| \| 2.6246 \| 304.84 \| 226500 \| 2.4970 \| 0.5348 \| \| 2.6246 \| 305.52 \| 227000 \| 2.4905 \| 0.5356 \| \| 2.6246 \| 306.19 \| 227500 \| 2.4873 \| 0.5361 \| \| 2.6246 \| 306.86 \| 228000 \| 2.4939 \| 0.5350 \| \| 2.6246 \| 307.54 \| 228500 \| 2.4910 \| 0.5360 \| \| 2.6246 \| 308.21 \| 229000 \| 2.4886 \| 0.5355 \| \| 2.6246 \| 308.88 \| 229500 \| 2.4890 \| 0.5369 \| \| 2.6207 \| 309.56 \| 230000 \| 2.4900 \| 0.5360 \| \| 2.6207 \| 310.23 \| 230500 \| 2.4885 \| 0.5354 \| \| 2.6207 \| 310.9 \| 231000 \| 2.4895 \| 0.5358 \| \| 2.6207 \| 311.57 \| 231500 \| 2.4871 \| 0.5358 \| \| 2.6207 \| 312.25 \| 232000 \| 2.4914 \| 0.5352 \| \| 2.6207 \| 312.92 \| 232500 \| 2.4843 \| 0.5366 \| \| 2.6207 \| 313.59 \| 233000 \| 2.4837 \| 0.5365 \| \| 2.6207 \| 314.27 \| 233500 \| 2.4883 \| 0.5360 \| \| 2.6207 \| 314.94 \| 234000 \| 2.4839 \| 0.5366 \| \| 2.6207 \| 315.61 \| 234500 \| 2.4854 \| 0.5366 \| \| 2.6171 \| 316.29 \| 235000 \| 2.4833 \| 0.5367 \| \| 2.6171 \| 316.96 \| 235500 \| 2.4783 \| 0.5374 \| \| 2.6171 \| 317.63 \| 236000 \| 2.4807 \| 0.5370 \| \| 2.6171 \| 318.3 \| 236500 \| 2.4824 \| 0.5366 \| \| 2.6171 \| 318.98 \| 237000 \| 2.4857 \| 0.5361 \| \| 2.6171 \| 319.65 \| 237500 \| 2.4817 \| 0.5366 \| \| 2.6171 \| 320.32 \| 238000 \| 2.4855 \| 0.5364 \| \| 2.6171 \| 321.0 \| 238500 \| 2.4834 \| 0.5367 \| \| 2.6171 \| 321.67 \| 239000 \| 2.4831 \| 0.5363 \| \| 2.6171 \| 322.34 \| 239500 \| 2.4806 \| 0.5370 \| \| 2.6134 \| 323.01 \| 240000 \| 2.4842 \| 0.5365 \| \| 2.6134 \| 323.69 \| 240500 \| 2.4830 \| 0.5364 \| \| 2.6134 \| 324.36 \| 241000 \| 2.4822 \| 0.5367 \| \| 2.6134 \| 325.03 \| 241500 \| 2.4805 \| 0.5373 \| \| 2.6134 \| 325.71 \| 242000 \| 2.4838 \| 0.5365 \| \| 2.6134 \| 326.38 \| 242500 \| 2.4776 \| 0.5371 \| \| 2.6134 \| 327.05 \| 243000 \| 2.4786 \| 0.5376 \| \| 2.6134 \| 327.73 \| 243500 \| 2.4824 \| 0.5371 \| \| 2.6134 \| 328.4 \| 244000 \| 2.4842 \| 0.5363 \| \| 2.6134 \| 329.07 \| 244500 \| 2.4790 \| 0.5375 \| \| 2.6107 \| 329.74 \| 245000 \| 2.4770 \| 0.5372 \| \| 2.6107 \| 330.42 \| 245500 \| 2.4805 \| 0.5375 \| \| 2.6107 \| 331.09 \| 246000 \| 2.4839 \| 0.5370 \| \| 2.6107 \| 331.76 \| 246500 \| 2.4802 \| 0.5367 \| \| 2.6107 \| 332.44 \| 247000 \| 2.4796 \| 0.5373 \| \| 2.6107 \| 333.11 \| 247500 \| 2.4736 \| 0.5377 \| \| 2.6107 \| 333.78 \| 248000 \| 2.4789 \| 0.5374 \| \| 2.6107 \| 334.45 \| 248500 \| 2.4761 \| 0.5375 \| \| 2.6107 \| 335.13 \| 249000 \| 2.4728 \| 0.5379 \| \| 2.6107 \| 335.8 \| 249500 \| 2.4702 \| 0.5386 \| \| 2.608 \| 336.47 \| 250000 \| 2.4764 \| 0.5377 \| \| 2.608 \| 337.15 \| 250500 \| 2.4738 \| 0.5380 \| \| 2.608 \| 337.82 \| 251000 \| 2.4795 \| 0.5371 \| \| 2.608 \| 338.49 \| 251500 \| 2.4702 \| 0.5387 \| \| 2.608 \| 339.17 \| 252000 \| 2.4823 \| 0.5369 \| \| 2.608 \| 339.84 \| 252500 \| 2.4741 \| 0.5382 \| \| 2.608 \| 340.51 \| 253000 \| 2.4718 \| 0.5382 \| \| 2.608 \| 341.18 \| 253500 \| 2.4731 \| 0.5378 \| \| 2.608 \| 341.86 \| 254000 \| 2.4780 \| 0.5373 \| \| 2.608 \| 342.53 \| 254500 \| 2.4706 \| 0.5388 \| \| 2.6058 \| 343.2 \| 255000 \| 2.4707 \| 0.5386 \| \| 2.6058 \| 343.88 \| 255500 \| 2.4725 \| 0.5380 \| \| 2.6058 \| 344.55 \| 256000 \| 2.4744 \| 0.5382 \| \| 2.6058 \| 345.22 \| 256500 \| 2.4766 \| 0.5374 \| \| 2.6058 \| 345.89 \| 257000 \| 2.4736 \| 0.5378 \| \| 2.6058 \| 346.57 \| 257500 \| 2.4731 \| 0.5383 \| \| 2.6058 \| 347.24 \| 258000 \| 2.4754 \| 0.5377 \| \| 2.6058 \| 347.91 \| 258500 \| 2.4749 \| 0.5382 \| \| 2.6058 \| 348.59 \| 259000 \| 2.4735 \| 0.5378 \| \| 2.6058 \| 349.26 \| 259500 \| 2.4716 \| 0.5384 \| \| 2.6027 \| 349.93 \| 260000 \| 2.4726 \| 0.5378 \| \| 2.6027 \| 350.61 \| 260500 \| 2.4733 \| 0.5378 \| \| 2.6027 \| 351.28 \| 261000 \| 2.4698 \| 0.5386 \| \| 2.6027 \| 351.95 \| 261500 \| 2.4702 \| 0.5388 \| \| 2.6027 \| 352.62 \| 262000 \| 2.4673 \| 0.5390 \| \| 2.6027 \| 353.3 \| 262500 \| 2.4683 \| 0.5390 \| \| 2.6027 \| 353.97 \| 263000 \| 2.4739 \| 0.5379 \| \| 2.6027 \| 354.64 \| 263500 \| 2.4743 \| 0.5382 \| \| 2.6027 \| 355.32 \| 264000 \| 2.4694 \| 0.5388 \| \| 2.6027 \| 355.99 \| 264500 \| 2.4671 \| 0.5391 \| \| 2.6009 \| 356.66 \| 265000 \| 2.4747 \| 0.5383 \| \| 2.6009 \| 357.34 \| 265500 \| 2.4703 \| 0.5382 \| \| 2.6009 \| 358.01 \| 266000 \| 2.4695 \| 0.5388 \| \| 2.6009 \| 358.68 \| 266500 \| 2.4725 \| 0.5380 \| \| 2.6009 \| 359.35 \| 267000 \| 2.4639 \| 0.5397 \| \| 2.6009 \| 360.03 \| 267500 \| 2.4686 \| 0.5385 \| \| 2.6009 \| 360.7 \| 268000 \| 2.4698 \| 0.5386 \| \| 2.6009 \| 361.37 \| 268500 \| 2.4694 \| 0.5386 \| \| 2.6009 \| 362.05 \| 269000 \| 2.4680 \| 0.5390 \| \| 2.6009 \| 362.72 \| 269500 \| 2.4728 \| 0.5383 \| \| 2.5989 \| 363.39 \| 270000 \| 2.4697 \| 0.5385 \| \| 2.5989 \| 364.06 \| 270500 \| 2.4701 \| 0.5387 \| \| 2.5989 \| 364.74 \| 271000 \| 2.4702 \| 0.5387 \| \| 2.5989 \| 365.41 \| 271500 \| 2.4687 \| 0.5390 \| \| 2.5989 \| 366.08 \| 272000 \| 2.4725 \| 0.5382 \| \| 2.5989 \| 366.76 \| 272500 \| 2.4673 \| 0.5384 \| \| 2.5989 \| 367.43 \| 273000 \| 2.4659 \| 0.5390 \| \| 2.5989 \| 368.1 \| 273500 \| 2.4686 \| 0.5389 \| \| 2.5989 \| 368.78 \| 274000 \| 2.4677 \| 0.5382 \| \| 2.5989 \| 369.45 \| 274500 \| 2.4632 \| 0.5389 \| \| 2.5977 \| 370.12 \| 275000 \| 2.4676 \| 0.5385 \| \| 2.5977 \| 370.79 \| 275500 \| 2.4699 \| 0.5388 \| \| 2.5977 \| 371.47 \| 276000 \| 2.4629 \| 0.5394 \| \| 2.5977 \| 372.14 \| 276500 \| 2.4720 \| 0.5380 \| \| 2.5977 \| 372.81 \| 277000 \| 2.4678 \| 0.5391 \| \| 2.5977 \| 373.49 \| 277500 \| 2.4643 \| 0.5396 \| \| 2.5977 \| 374.16 \| 278000 \| 2.4654 \| 0.5395 \| \| 2.5977 \| 374.83 \| 278500 \| 2.4645 \| 0.5395 \| \| 2.5977 \| 375.5 \| 279000 \| 2.4649 \| 0.5391 \| \| 2.5977 \| 376.18 \| 279500 \| 2.4639 \| 0.5392 \| \| 2.5961 \| 376.85 \| 280000 \| 2.4659 \| 0.5389 \| \| 2.5961 \| 377.52 \| 280500 \| 2.4681 \| 0.5385 \| \| 2.5961 \| 378.2 \| 281000 \| 2.4641 \| 0.5390 \| \| 2.5961 \| 378.87 \| 281500 \| 2.4658 \| 0.5393 \| \| 2.5961 \| 379.54 \| 282000 \| 2.4687 \| 0.5388 \| \| 2.5961 \| 380.22 \| 282500 \| 2.4690 \| 0.5385 \| \| 2.5961 \| 380.89 \| 283000 \| 2.4679 \| 0.5391 \| \| 2.5961 \| 381.56 \| 283500 \| 2.4612 \| 0.5395 \| \| 2.5961 \| 382.23 \| 284000 \| 2.4624 \| 0.5395 \| \| 2.5961 \| 382.91 \| 284500 \| 2.4668 \| 0.5390 \| \| 2.5947 \| 383.58 \| 285000 \| 2.4663 \| 0.5389 \| \| 2.5947 \| 384.25 \| 285500 \| 2.4654 \| 0.5387 \| \| 2.5947 \| 384.93 \| 286000 \| 2.4708 \| 0.5385 \| \| 2.5947 \| 385.6 \| 286500 \| 2.4669 \| 0.5388 \| \| 2.5947 \| 386.27 \| 287000 \| 2.4612 \| 0.5396 \| \| 2.5947 \| 386.94 \| 287500 \| 2.4666 \| 0.5392 \| \| 2.5947 \| 387.62 \| 288000 \| 2.4653 \| 0.5393 \| \| 2.5947 \| 388.29 \| 288500 \| 2.4666 \| 0.5390 \| \| 2.5947 \| 388.96 \| 289000 \| 2.4684 \| 0.5388 \| \| 2.5947 \| 389.64 \| 289500 \| 2.4660 \| 0.5394 \| \| 2.5936 \| 390.31 \| 290000 \| 2.4642 \| 0.5395 \| \| 2.5936 \| 390.98 \| 290500 \| 2.4627 \| 0.5403 \| \| 2.5936 \| 391.66 \| 291000 \| 2.4683 \| 0.5389 \| \| 2.5936 \| 392.33 \| 291500 \| 2.4667 \| 0.5387 \| \| 2.5936 \| 393.0 \| 292000 \| 2.4660 \| 0.5389 \| \| 2.5936 \| 393.67 \| 292500 \| 2.4673 \| 0.5390 \| \| 2.5936 \| 394.35 \| 293000 \| 2.4645 \| 0.5391 \| \| 2.5936 \| 395.02 \| 293500 \| 2.4693 \| 0.5389 \| \| 2.5936 \| 395.69 \| 294000 \| 2.4692 \| 0.5385 \| \| 2.5936 \| 396.37 \| 294500 \| 2.4653 \| 0.5385 \| \| 2.5934 \| 397.04 \| 295000 \| 2.4661 \| 0.5390 \| \| 2.5934 \| 397.71 \| 295500 \| 2.4630 \| 0.5394 \| \| 2.5934 \| 398.38 \| 296000 \| 2.4641 \| 0.5390 \| \| 2.5934 \| 399.06 \| 296500 \| 2.4636 \| 0.5392 \| \| 2.5934 \| 399.73 \| 297000 \| 2.4650 \| 0.5392 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1 ### Additional configurations ``` data: dataset_name: cc100 lang: nl overwrite_cache: False validation_split_percentage: 5 max_seq_length: 512 preprocessing_num_workers: 8 mlm_probability: 0.15 line_by_line: False pad_to_max_length: False max_train_samples: -1 max_eval_samples: -1 training: do_train: True do_eval: True do_predict: True resume_from_checkpoint: False evaluation_strategy: steps eval_steps: 500 per_device_train_batch_size: 16 per_device_eval_batch_size: 16 gradient_accumulation_steps: 32 eval_accumulation_steps: 1 learning_rate: 5e-5 weight_decay: 0.0 adam_beta1: 0.9 adam_beta2: 0.999 adam_epsilon: 1e-8 max_grad_norm: 1.0 num_train_epochs: 400.0 lr_scheduler_type: linear fp16: False warmup_steps: 8000 seed: 703 ```
kyoumiaoi/wav2vec2-base-timit-demo-google-colab	kyoumiaoi	2022-08-02T08:28:06Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-08-02T06:15:34Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-google-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5499 - Wer: 0.3435 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 3.599 \| 1.0 \| 500 \| 2.1267 \| 0.9976 \| \| 1.016 \| 2.01 \| 1000 \| 0.6193 \| 0.5443 \| \| 0.5299 \| 3.01 \| 1500 \| 0.5324 \| 0.4889 \| \| 0.3626 \| 4.02 \| 2000 \| 0.4525 \| 0.4402 \| \| 0.2854 \| 5.02 \| 2500 \| 0.4266 \| 0.4233 \| \| 0.2373 \| 6.02 \| 3000 \| 0.4713 \| 0.4082 \| \| 0.1979 \| 7.03 \| 3500 \| 0.4778 \| 0.4018 \| \| 0.1761 \| 8.03 \| 4000 \| 0.4585 \| 0.3947 \| \| 0.1537 \| 9.04 \| 4500 \| 0.5297 \| 0.3946 \| \| 0.1379 \| 10.04 \| 5000 \| 0.4988 \| 0.3856 \| \| 0.124 \| 11.04 \| 5500 \| 0.5262 \| 0.3852 \| \| 0.11 \| 12.05 \| 6000 \| 0.5545 \| 0.3854 \| \| 0.106 \| 13.05 \| 6500 \| 0.5196 \| 0.3805 \| \| 0.0918 \| 14.06 \| 7000 \| 0.4515 \| 0.3655 \| \| 0.0829 \| 15.06 \| 7500 \| 0.5087 \| 0.3722 \| \| 0.0775 \| 16.06 \| 8000 \| 0.4980 \| 0.3781 \| \| 0.0685 \| 17.07 \| 8500 \| 0.5564 \| 0.3650 \| \| 0.0655 \| 18.07 \| 9000 \| 0.5323 \| 0.3672 \| \| 0.0578 \| 19.08 \| 9500 \| 0.5675 \| 0.3637 \| \| 0.052 \| 20.08 \| 10000 \| 0.5604 \| 0.3664 \| \| 0.0512 \| 21.08 \| 10500 \| 0.5922 \| 0.3804 \| \| 0.0431 \| 22.09 \| 11000 \| 0.6379 \| 0.3754 \| \| 0.0428 \| 23.09 \| 11500 \| 0.5905 \| 0.3764 \| \| 0.0393 \| 24.1 \| 12000 \| 0.5667 \| 0.3542 \| \| 0.0326 \| 25.1 \| 12500 \| 0.5612 \| 0.3537 \| \| 0.0289 \| 26.1 \| 13000 \| 0.5618 \| 0.3475 \| \| 0.0298 \| 27.11 \| 13500 \| 0.5578 \| 0.3439 \| \| 0.0264 \| 28.11 \| 14000 \| 0.5547 \| 0.3433 \| \| 0.026 \| 29.12 \| 14500 \| 0.5499 \| 0.3435 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.12.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1
psroy/wav2vec2-base-timit-demo-google-colab	psroy	2022-08-02T07:12:02Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-07-29T04:40:18Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-google-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5366 - Wer: 0.3452 ## Model description More information needed ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.5499 \| 2.01 \| 500 \| 1.9780 \| 0.9933 \| \| 0.7517 \| 4.02 \| 1000 \| 0.4654 \| 0.4720 \| \| 0.2953 \| 6.02 \| 1500 \| 0.4202 \| 0.4049 \| \| 0.1809 \| 8.03 \| 2000 \| 0.4276 \| 0.3759 \| \| 0.1335 \| 10.04 \| 2500 \| 0.4458 \| 0.3774 \| \| 0.107 \| 12.05 \| 3000 \| 0.4559 \| 0.3707 \| \| 0.0923 \| 14.06 \| 3500 \| 0.4607 \| 0.3659 \| \| 0.0753 \| 16.06 \| 4000 \| 0.4699 \| 0.3531 \| \| 0.0658 \| 18.07 \| 4500 \| 0.4507 \| 0.3588 \| \| 0.0569 \| 20.08 \| 5000 \| 0.5089 \| 0.3532 \| \| 0.0493 \| 22.09 \| 5500 \| 0.5481 \| 0.3515 \| \| 0.043 \| 24.1 \| 6000 \| 0.5066 \| 0.3528 \| \| 0.0388 \| 26.1 \| 6500 \| 0.5418 \| 0.3534 \| \| 0.034 \| 28.11 \| 7000 \| 0.5566 \| 0.3524 \| \| 0.03 \| 30.12 \| 7500 \| 0.4994 \| 0.3437 \| \| 0.0274 \| 32.13 \| 8000 \| 0.5588 \| 0.3520 \| \| 0.0239 \| 34.14 \| 8500 \| 0.5328 \| 0.3458 \| \| 0.0212 \| 36.14 \| 9000 \| 0.5221 \| 0.3467 \| \| 0.0186 \| 38.15 \| 9500 \| 0.5366 \| 0.3452 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.12.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1
JAlexis/bert001	JAlexis	2022-08-02T02:59:29Z	5	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "en", "endpoints_compatible", "region:us" ]	question-answering	2022-08-02T01:35:56Z	--- language: en #epoch 7 #batch size 14 #lr 5e-5 widget: - text: "How can I protect myself against covid-19?" context: "Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model and measures of Liao et al. " - text: "How can I protect myself against covid-19?" context: " " --- ## Model description This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset. ## How to use ```python from transformers.pipelines import pipeline model_name = "JAlexis/PruebaBert" nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) inputs = { 'question': 'How can I protect myself against covid-19?', 'context': 'Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model and measures of Liao et al. [6] to the current context of the COVID-19 pandemic and the culture of the USA. Applying this model in a different time and context provides an opportunity to make comparisons of reactions to information sources across a decade of evolving attitudes toward media and government, between two cultures (Hong Kong vs. the USA), and between two considerably different global pandemics (H1N1 vs. COVID-19). ', } nlp(inputs) ```
JAlexis/PruebaBert	JAlexis	2022-08-02T01:46:49Z	27	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "en", "dataset:squad2", "dataset:cord19", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:04Z	--- language: en tags: - pytorch - question-answering datasets: - squad2 - cord19 metrics: - EM (exact match) widget: - text: "How can I protect myself against covid-19?" context: "Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model and measures of Liao et al. [6] to the current context of the COVID-19 pandemic and the culture of the USA. Applying this model in a different time and context provides an opportunity to make comparisons of reactions to information sources across a decade of evolving attitudes toward media and government, between two cultures (Hong Kong vs. the USA), and between two considerably different global pandemics (H1N1 vs. COVID-19)." - text: "How can I protect myself against covid-19?" context: " " --- ## Model description This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset. ## How to use ```python from transformers.pipelines import pipeline model_name = "JAlexis/PruebaBert" nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) inputs = { 'question': 'How can I protect myself against covid-19?', 'context': 'Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model and measures of Liao et al. [6] to the current context of the COVID-19 pandemic and the culture of the USA. Applying this model in a different time and context provides an opportunity to make comparisons of reactions to information sources across a decade of evolving attitudes toward media and government, between two cultures (Hong Kong vs. the USA), and between two considerably different global pandemics (H1N1 vs. COVID-19). ', 'question': 'How can I protect myself against covid-19?', 'context': ' ', } nlp(inputs) ``` ## Overview ``` Language model: deepset/bert-base-cased-squad2 Language: English Downstream-task: Q&A Datasets: CORD-19 from 31rd January 2022 Code: Haystack and FARM Infrastructure: Tesla T4 ``` ## Hyperparameters ``` batch_size = 8 n_epochs = 9 max_seq_len = max_length learning_rate = AdamW: 1e-5 ```
rdruce/ddpm-cheese-32	rdruce	2022-08-02T00:34:19Z	0	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-08-02T00:05:54Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-cheese-32 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/rdruce/ddpm-cheese-32/tensorboard?#scalars)
muhtasham/bert-tiny-finetuned-xglue-ner	muhtasham	2022-08-01T23:20:07Z	5	1	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:xglue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-08-01T23:13:29Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - xglue metrics: - precision - recall - f1 - accuracy model-index: - name: bert-tiny-finetuned-xglue-ner results: - task: name: Token Classification type: token-classification dataset: name: xglue type: xglue config: ner split: train args: ner metrics: - name: Precision type: precision value: 0.630759453447728 - name: Recall type: recall value: 0.6681252103668799 - name: F1 type: f1 value: 0.6489048708728343 - name: Accuracy type: accuracy value: 0.9274310133922189 --- <!-- 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-tiny-finetuned-xglue-ner This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the xglue dataset. It achieves the following results on the evaluation set: - Loss: 0.2489 - Precision: 0.6308 - Recall: 0.6681 - F1: 0.6489 - Accuracy: 0.9274 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.4082 \| 1.0 \| 1756 \| 0.3326 \| 0.5600 \| 0.5798 \| 0.5697 \| 0.9118 \| \| 0.2974 \| 2.0 \| 3512 \| 0.2635 \| 0.6143 \| 0.6562 \| 0.6346 \| 0.9248 \| \| 0.2741 \| 3.0 \| 5268 \| 0.2489 \| 0.6308 \| 0.6681 \| 0.6489 \| 0.9274 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
SharpAI/mal-tls-mobilebert	SharpAI	2022-08-01T22:53:41Z	4	0	transformers	[ "transformers", "pytorch", "tf", "mobilebert", "text-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-01T22:45:11Z	--- tags: - generated_from_keras_callback model-index: - name: mal_tls-mobilebert 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. --> # mal_tls-mobilebert This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1
cansen88/turkishReviews_5_topic	cansen88	2022-08-01T22:13:12Z	4	0	transformers	[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-08-01T21:21:12Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: turkishReviews_5_topic 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. --> # turkishReviews_5_topic This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 6.8939 - Validation Loss: 6.8949 - 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 756, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 8.0049 \| 6.8949 \| 0 \| \| 6.8943 \| 6.8949 \| 1 \| \| 6.8939 \| 6.8949 \| 2 \| ### Framework versions - Transformers 4.21.0 - TensorFlow 2.8.2 - Datasets 2.4.0 - Tokenizers 0.12.1
Intel/bert-large-uncased-squadv1.1-sparse-80-1x4-block-pruneofa	Intel	2022-08-01T21:04:22Z	75	1	transformers	[ "transformers", "pytorch", "onnx", "bert", "question-answering", "en", "arxiv:2111.05754", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-03-27T20:17:27Z	--- language: en license: apache-2.0 --- # 80% 1x4 Block Sparse BERT-Large (uncased) Fine Tuned on SQuADv1.1 This model is a result of fine-tuning a Prune OFA 80% 1x4 block sparse pre-trained BERT-Large combined with knowledge distillation. This model yields the following results on SQuADv1.1 development set:<br> `{"exact_match": 84.673, "f1": 91.174}` For further details see our paper, [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754), and our open source implementation available [here](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all).
mrm8488/pyramidsrnd	mrm8488	2022-08-01T20:36:43Z	9	1	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2022-08-01T20:36:37Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # 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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: mrm8488/pyramidsrnd 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
muhtasham/bert-tiny-finetuned-pile-of-law-tos	muhtasham	2022-08-01T20:24:25Z	6	1	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-08-01T18:22:18Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-tiny-finetuned-pile-of-law-tos 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-tiny-finetuned-pile-of-law-tos This model is a MLM fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the [pile-of-law/tos](https://huggingface.co/datasets/pile-of-law/pile-of-law) dataset. It achieves the following results on the evaluation set: - Loss: 3.3545 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 264 \| 3.5896 \| \| 3.8119 \| 2.0 \| 528 \| 3.5598 \| \| 3.8119 \| 3.0 \| 792 \| 3.5263 \| \| 3.7028 \| 4.0 \| 1056 \| 3.4982 \| \| 3.7028 \| 5.0 \| 1320 \| 3.5170 \| \| 3.6286 \| 6.0 \| 1584 \| 3.5143 \| \| 3.6286 \| 7.0 \| 1848 \| 3.4477 \| \| 3.553 \| 8.0 \| 2112 \| 3.4044 \| \| 3.553 \| 9.0 \| 2376 \| 3.4670 \| \| 3.5179 \| 10.0 \| 2640 \| 3.3991 \| \| 3.5179 \| 11.0 \| 2904 \| 3.4330 \| \| 3.4784 \| 12.0 \| 3168 \| 3.4671 \| \| 3.4784 \| 13.0 \| 3432 \| 3.3489 \| \| 3.4535 \| 14.0 \| 3696 \| 3.4354 \| \| 3.4535 \| 15.0 \| 3960 \| 3.4023 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
SharpAI/mal-tls-bert-base-relu-w8a8	SharpAI	2022-08-01T20:23:16Z	4	0	transformers	[ "transformers", "pytorch", "tf", "bert", "text-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-01T20:22:51Z	--- tags: - generated_from_keras_callback model-index: - name: mal_tls-bert-base-relu-w8a8 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. --> # mal_tls-bert-base-relu-w8a8 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.15.0 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.10.3
arize-ai/resnet-50-fashion-mnist-quality-drift	arize-ai	2022-08-01T19:55:57Z	182	4	transformers	[ "transformers", "pytorch", "tensorboard", "resnet", "image-classification", "generated_from_trainer", "dataset:fashion_mnist_quality_drift", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-08-01T19:32:07Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - fashion_mnist_quality_drift metrics: - accuracy - f1 model-index: - name: resnet-50-fashion-mnist-quality-drift results: - task: name: Image Classification type: image-classification dataset: name: fashion_mnist_quality_drift type: fashion_mnist_quality_drift config: default split: training args: default metrics: - name: Accuracy type: accuracy value: 0.73 - name: F1 type: f1 value: 0.7289360255705818 --- <!-- 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. --> # resnet-50-fashion-mnist-quality-drift This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the fashion_mnist_quality_drift dataset. It achieves the following results on the evaluation set: - Loss: 0.7473 - Accuracy: 0.73 - F1: 0.7289 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 1.5138 \| 1.0 \| 750 \| 0.9237 \| 0.684 \| 0.6826 \| \| 0.9377 \| 2.0 \| 1500 \| 0.7861 \| 0.722 \| 0.7253 \| \| 0.8276 \| 3.0 \| 2250 \| 0.7473 \| 0.73 \| 0.7289 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
SharpAI/mal-tls-bert-base-relu	SharpAI	2022-08-01T19:54:00Z	5	0	transformers	[ "transformers", "pytorch", "tf", "bert", "text-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-01T19:53:07Z	--- tags: - generated_from_keras_callback model-index: - name: mal_tls-bert-base-relu 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. --> # mal_tls-bert-base-relu This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1
mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es	mrm8488	2022-08-01T19:41:40Z	1,199	3	sentence-transformers	[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "es", "dataset:stsb_multi_mt", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- language: es thumbnail: https://imgur.com/a/G77ZqQN pipeline_tag: sentence-similarity datasets: - stsb_multi_mt tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # Distiluse-m-v2 fine-tuned on stsb_multi_mt for Spanish Semantic Textual Similarity This is a [sentence-transformers](https://www.SBERT.net) model (distiluse-base-multilingual-cased-v2): 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 = ["Nerea va a comprar un cuadro usando bitcoins", "Se puede comprar arte con bitcoins"] model = SentenceTransformer('mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es') 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 = ["Nerea va a comprar un cuadro usando bitcoins", "Se puede comprar arte con bitcoins"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es') model = AutoModel.from_pretrained('mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es') # 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) ``` ## How to evaluate ```py from datasets import load_dataset from sentence_transformers import SentenceTransformer, InputExample from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator test_data = load_dataset('stsb_multi_mt', 'es', split='test') test_data = test_data.rename_columns({'similarity_score': 'label'}) test_data = test_data.map(lambda x: {'label': x['label'] / 5.0}) samples = [] for sample in test_data: samples.append(InputExample( texts=[sample['sentence1'], sample['sentence2']], label=sample['label'] )) evaluator = EmbeddingSimilarityEvaluator.from_input_examples( samples, write_csv=False ) model = SentenceTransformer('mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es') evaluator(model) # It outputs: 0.7604056195656299 ``` ## Evaluation Results Spearman’s rank correlation: 0.7604056195656299** 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: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 906 with parameters: ``` {'batch_size': 16} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 271, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (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 -->
vidyavenkappa/pegasus-samsum	vidyavenkappa	2022-08-01T18:30:17Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "pegasus", "text2text-generation", "generated_from_trainer", "dataset:samsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-07-30T12:10:24Z	--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pegasus-samsum This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.3086 ## Model description More information needed ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.6151 \| 0.54 \| 500 \| 1.4238 \| \| 1.3357 \| 1.09 \| 1000 \| 1.3629 \| \| 1.4423 \| 1.63 \| 1500 \| 1.3380 \| \| 1.3747 \| 2.17 \| 2000 \| 1.3218 \| \| 1.3397 \| 2.72 \| 2500 \| 1.3124 \| \| 1.2706 \| 3.26 \| 3000 \| 1.3149 \| \| 1.1849 \| 3.8 \| 3500 \| 1.3120 \| \| 1.2222 \| 4.35 \| 4000 \| 1.3120 \| \| 1.2339 \| 4.89 \| 4500 \| 1.3086 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
turhancan97/Reinforce-2	turhancan97	2022-08-01T16:45:20Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2022-08-01T16:44:23Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-2 results: - metrics: - type: mean_reward value: 9.40 +/- 13.66 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
silviacamplani/twitter-roberta-base-finetuned-ner-wnut	silviacamplani	2022-08-01T16:26:39Z	5	0	transformers	[ "transformers", "tf", "tensorboard", "roberta", "token-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-08-01T15:50:19Z	--- tags: - generated_from_keras_callback model-index: - name: silviacamplani/twitter-roberta-base-finetuned-ner-wnut 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. --> # silviacamplani/twitter-roberta-base-finetuned-ner-wnut This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0812 - Validation Loss: 0.2553 - Train Precision: 0.6263 - Train Recall: 0.5191 - Train F1: 0.5677 - Train Accuracy: 0.9398 - 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: {'inner_optimizer': {'class_name': 'Adam', 'config': {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Train Precision \| Train Recall \| Train F1 \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:---------------:\|:------------:\|:--------:\|:--------------:\|:-----:\| \| 0.0813 \| 0.2553 \| 0.6263 \| 0.5191 \| 0.5677 \| 0.9398 \| 0 \| \| 0.0815 \| 0.2553 \| 0.6263 \| 0.5191 \| 0.5677 \| 0.9398 \| 1 \| \| 0.0812 \| 0.2553 \| 0.6263 \| 0.5191 \| 0.5677 \| 0.9398 \| 2 \| ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1
thocheat/v2-fine-tune-wav2vec2-Vietnamese-ARS-demo	thocheat	2022-08-01T16:01:15Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-08-01T14:23:01Z	--- license: cc-by-nc-4.0 tags: - generated_from_trainer model-index: - name: v2-fine-tune-wav2vec2-Vietnamese-ARS-demo 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. --> # v2-fine-tune-wav2vec2-Vietnamese-ARS-demo This model is a fine-tuned version of [nguyenvulebinh/wav2vec2-base-vietnamese-250h](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2515 - Wer: 0.2235 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 8.8651 \| 0.34 \| 500 \| 3.6919 \| 0.9999 \| \| 3.54 \| 0.69 \| 1000 \| 3.3584 \| 1.0 \| \| 2.9478 \| 1.03 \| 1500 \| 2.2535 \| 0.9885 \| \| 1.9147 \| 1.37 \| 2000 \| 0.9977 \| 0.7260 \| \| 1.1667 \| 1.71 \| 2500 \| 0.5577 \| 0.4746 \| \| 0.844 \| 2.06 \| 3000 \| 0.4129 \| 0.3581 \| \| 0.6968 \| 2.4 \| 3500 \| 0.3566 \| 0.3090 \| \| 0.6273 \| 2.74 \| 4000 \| 0.3243 \| 0.2813 \| \| 0.5434 \| 3.09 \| 4500 \| 0.3076 \| 0.2631 \| \| 0.5069 \| 3.43 \| 5000 \| 0.2902 \| 0.2539 \| \| 0.4842 \| 3.77 \| 5500 \| 0.2752 \| 0.2432 \| \| 0.4318 \| 4.12 \| 6000 \| 0.2854 \| 0.2384 \| \| 0.3951 \| 4.46 \| 6500 \| 0.2674 \| 0.2350 \| \| 0.3954 \| 4.8 \| 7000 \| 0.2628 \| 0.2322 \| \| 0.3763 \| 5.14 \| 7500 \| 0.2609 \| 0.2284 \| \| 0.3652 \| 5.49 \| 8000 \| 0.2508 \| 0.2249 \| \| 0.3703 \| 5.83 \| 8500 \| 0.2515 \| 0.2235 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.12.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1
silviacamplani/distilbert-base-uncased-finetuned-ner-wnut	silviacamplani	2022-08-01T14:53:43Z	4	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "token-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-08-01T10:37:08Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: silviacamplani/distilbert-base-uncased-finetuned-ner-wnut 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. --> # silviacamplani/distilbert-base-uncased-finetuned-ner-wnut 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: 0.1241 - Validation Loss: 0.3433 - Train Precision: 0.5677 - Train Recall: 0.3660 - Train F1: 0.4451 - Train Accuracy: 0.9215 - 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: {'inner_optimizer': {'class_name': 'Adam', 'config': {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Train Precision \| Train Recall \| Train F1 \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:---------------:\|:------------:\|:--------:\|:--------------:\|:-----:\| \| 0.3454 \| 0.4475 \| 0.0 \| 0.0 \| 0.0 \| 0.8961 \| 0 \| \| 0.1637 \| 0.3637 \| 0.6297 \| 0.2990 \| 0.4055 \| 0.9154 \| 1 \| \| 0.1241 \| 0.3433 \| 0.5677 \| 0.3660 \| 0.4451 \| 0.9215 \| 2 \| ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1
dminiotas05/distilbert-base-uncased-finetuned-ft750_reg5	dminiotas05	2022-08-01T14:18:11Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-01T13:57:20Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-ft750_reg5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ft750_reg5 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: - Loss: 0.6298 - Mse: 0.6298 - Mae: 0.6087 - R2: 0.4072 - Accuracy: 0.4973 ## Model description More information needed ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mse \| Mae \| R2 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:--------:\| \| 1.8617 \| 1.0 \| 188 \| 0.7482 \| 0.7482 \| 0.6639 \| 0.2957 \| 0.4707 \| \| 0.5667 \| 2.0 \| 376 \| 0.6017 \| 0.6017 \| 0.5978 \| 0.4336 \| 0.5127 \| \| 0.5038 \| 3.0 \| 564 \| 0.6298 \| 0.6298 \| 0.6087 \| 0.4072 \| 0.4973 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
muhtasham/bert-tiny-finetuned-finer-tf	muhtasham	2022-08-01T13:41:59Z	4	0	transformers	[ "transformers", "tf", "bert", "token-classification", "generated_from_keras_callback", "dataset:nlpaueb/finer-139", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-07-29T22:13:44Z	--- license: apache-2.0 datasets: - nlpaueb/finer-139 tags: - generated_from_keras_callback model-index: - name: muhtasham/bert-tiny-finetuned-finer-tf 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. --> # muhtasham/bert-tiny-finetuned-finer-tf This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0372 - Validation Loss: 0.0296 - 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 168822, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.1188 \| 0.0420 \| 0 \| \| 0.0438 \| 0.0313 \| 1 \| \| 0.0372 \| 0.0296 \| 2 \| ### Framework versions - Transformers 4.21.0 - TensorFlow 2.8.2 - Datasets 2.4.0 - Tokenizers 0.12.1
rdruce/ddpm-butterflies-128	rdruce	2022-08-01T12:46:38Z	0	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:huggan/smithsonian_butterflies_subset", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-08-01T11:33:05Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/rdruce/ddpm-butterflies-128/tensorboard?#scalars)
sumba/covid-twitter-bert-v2-no_description-stance-loss-hyp	sumba	2022-08-01T12:16:28Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-31T12:21:31Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: covid-twitter-bert-v2-no_description-stance-loss-hyp 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. --> # covid-twitter-bert-v2-no_description-stance-loss-hyp This model is a fine-tuned version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6202 - Accuracy: 0.0829 ## Model description More information needed ## 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: 1.4275469935864394e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.8211 \| 1.0 \| 632 \| 0.6258 \| 0.1153 \| \| 0.5742 \| 2.0 \| 1264 \| 0.6202 \| 0.0829 \| \| 0.4456 \| 3.0 \| 1896 \| 0.6340 \| 0.0627 \| \| 0.2163 \| 4.0 \| 2528 \| 0.7645 \| 0.0470 \| ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu102 - Datasets 2.2.1 - Tokenizers 0.12.1
BekirTaha/q-FrozenLake-v1-4x4-noSlippery	BekirTaha	2022-08-01T12:12:18Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-08-01T12:01:10Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # Q-Learning Agent playing FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Beyko7/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
turhancan97/testpyramidsrnd	turhancan97	2022-08-01T12:08:01Z	6	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2022-08-01T12:07:56Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # 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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: turhancan97/testpyramidsrnd 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
dminiotas05/distilbert-base-uncased-finetuned-ft750_reg3	dminiotas05	2022-08-01T11:51:26Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-01T11:22:10Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-ft750_reg3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ft750_reg3 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: - Loss: 0.6143 - Mse: 0.6143 - Mae: 0.6022 - R2: 0.4218 - Accuracy: 0.52 ## Model description More information needed ## 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mse \| Mae \| R2 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:--------:\| \| 0.5241 \| 1.0 \| 188 \| 0.6143 \| 0.6143 \| 0.6022 \| 0.4218 \| 0.52 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
lewiswu1209/Winnie	lewiswu1209	2022-08-01T10:52:48Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-07-24T03:09:47Z	--- license: mit --- # Winnie Winnie是基于[cambridgeltl/simctg_lccc_dialogue](https://huggingface.co/cambridgeltl/simctg_lccc_dialogue)训练的我修改了vocab.txt, 新增了`[NAME][NICK][GENDER][YEAROFBIRTH][MONTHOFBIRTH][DAYOFBIRTH][ZODIAC][AGE]`几个special_token，然后搞了些类似 ``` 你是谁？我是[NAME]。你叫什么？我叫[NAME]。你多大啦？我[AGE]岁了。 ``` 的语料。第一次训练的时候起名叫Vicky，然后把Vicky的脑子训瓦特了，只能摸索新的办法了。后来利用了[50W闲聊语料](https://github.com/yangjianxin1/GPT2-chitchat#%E9%97%B2%E8%81%8A%E8%AF%AD%E6%96%99%E5%88%86%E4%BA%AB)搭配新增的语料按照大约19:1的比例进行训练，感觉效果还可以。
dminiotas05/camembert-base-finetuned-ft750_reg2	dminiotas05	2022-08-01T10:10:20Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "camembert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-28T11:03:55Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: camembert-base-finetuned-ft750_reg2 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. --> # camembert-base-finetuned-ft750_reg2 This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6449 - Mse: 0.6449 - Mae: 0.6171 - R2: 0.3929 - Accuracy: 0.504 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mse \| Mae \| R2 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:--------:\| \| 0.6283 \| 1.0 \| 750 \| 0.6074 \| 0.6074 \| 0.6086 \| 0.4282 \| 0.4887 \| \| 0.5007 \| 2.0 \| 1500 \| 0.6449 \| 0.6449 \| 0.6171 \| 0.3929 \| 0.504 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
lakshaywadhwa1993/ner_hindi_bert	lakshaywadhwa1993	2022-08-01T09:14:58Z	8	1	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "dataset:wikiann", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-08-01T09:05:27Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wikiann model-index: - name: ner_hindi_bert 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. --> # ner_hindi_bert This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.3713 - Overall Precision: 0.8942 - Overall Recall: 0.8972 - Overall F1: 0.8957 - Overall Accuracy: 0.9367 - Loc F1: 0.8766 - Org F1: 0.8489 - Per F1: 0.9454 ## Model description More information needed ## 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: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| Loc F1 \| Org F1 \| Per F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\|:------:\|:------:\|:------:\| \| 0.2993 \| 3.19 \| 1000 \| 0.3230 \| 0.8779 \| 0.8786 \| 0.8782 \| 0.9244 \| 0.8535 \| 0.8270 \| 0.9358 \| \| 0.0641 \| 6.39 \| 2000 \| 0.3713 \| 0.8942 \| 0.8972 \| 0.8957 \| 0.9367 \| 0.8766 \| 0.8489 \| 0.9454 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
psroy/wav2vec2-base-timit-demo-colab	psroy	2022-08-01T08:59:16Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-07-29T10:16:59Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4772 - Wer: 0.2821 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 3.6949 \| 0.87 \| 500 \| 2.4599 \| 0.9999 \| \| 0.9858 \| 1.73 \| 1000 \| 0.5249 \| 0.4674 \| \| 0.4645 \| 2.6 \| 1500 \| 0.4604 \| 0.3900 \| \| 0.3273 \| 3.46 \| 2000 \| 0.3939 \| 0.3612 \| \| 0.2474 \| 4.33 \| 2500 \| 0.4150 \| 0.3560 \| \| 0.2191 \| 5.19 \| 3000 \| 0.3855 \| 0.3344 \| \| 0.1662 \| 6.06 \| 3500 \| 0.3779 \| 0.3258 \| \| 0.1669 \| 6.92 \| 4000 \| 0.4841 \| 0.3286 \| \| 0.151 \| 7.79 \| 4500 \| 0.4182 \| 0.3219 \| \| 0.1175 \| 8.65 \| 5000 \| 0.4194 \| 0.3107 \| \| 0.1103 \| 9.52 \| 5500 \| 0.4256 \| 0.3129 \| \| 0.1 \| 10.38 \| 6000 \| 0.4352 \| 0.3089 \| \| 0.0949 \| 11.25 \| 6500 \| 0.4649 \| 0.3160 \| \| 0.0899 \| 12.11 \| 7000 \| 0.4472 \| 0.3065 \| \| 0.0787 \| 12.98 \| 7500 \| 0.4763 \| 0.3128 \| \| 0.0742 \| 13.84 \| 8000 \| 0.4321 \| 0.3034 \| \| 0.067 \| 14.71 \| 8500 \| 0.4562 \| 0.3076 \| \| 0.063 \| 15.57 \| 9000 \| 0.4541 \| 0.3102 \| \| 0.0624 \| 16.44 \| 9500 \| 0.5113 \| 0.3040 \| \| 0.0519 \| 17.3 \| 10000 \| 0.4925 \| 0.3008 \| \| 0.0525 \| 18.17 \| 10500 \| 0.4710 \| 0.2987 \| \| 0.046 \| 19.03 \| 11000 \| 0.4781 \| 0.2977 \| \| 0.0455 \| 19.9 \| 11500 \| 0.4572 \| 0.2969 \| \| 0.0394 \| 20.76 \| 12000 \| 0.5256 \| 0.2966 \| \| 0.0373 \| 21.63 \| 12500 \| 0.4723 \| 0.2921 \| \| 0.0375 \| 22.49 \| 13000 \| 0.4640 \| 0.2847 \| \| 0.0334 \| 23.36 \| 13500 \| 0.4740 \| 0.2917 \| \| 0.0304 \| 24.22 \| 14000 \| 0.4817 \| 0.2874 \| \| 0.0291 \| 25.09 \| 14500 \| 0.4722 \| 0.2896 \| \| 0.0247 \| 25.95 \| 15000 \| 0.4765 \| 0.2870 \| \| 0.0223 \| 26.82 \| 15500 \| 0.4728 \| 0.2821 \| \| 0.0223 \| 27.68 \| 16000 \| 0.4690 \| 0.2834 \| \| 0.0207 \| 28.55 \| 16500 \| 0.4706 \| 0.2825 \| \| 0.0186 \| 29.41 \| 17000 \| 0.4772 \| 0.2821 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.12.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1
lakshaywadhwa1993/ner_marathi_bert	lakshaywadhwa1993	2022-08-01T08:39:52Z	3	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "dataset:wikiann", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-06-09T21:00:19Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wikiann model-index: - name: ner_marathi_bert 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. --> # ner_marathi_bert This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.3606 - Overall Precision: 0.8939 - Overall Recall: 0.9030 - Overall F1: 0.8984 - Overall Accuracy: 0.9347 - Loc F1: 0.8823 - Org F1: 0.8555 - Per F1: 0.9435 ## Model description More information needed ## 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: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| Loc F1 \| Org F1 \| Per F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\|:------:\|:------:\|:------:\| \| 0.2961 \| 3.19 \| 1000 \| 0.3496 \| 0.8720 \| 0.8841 \| 0.8780 \| 0.9229 \| 0.8599 \| 0.8210 \| 0.9343 \| \| 0.0613 \| 6.39 \| 2000 \| 0.3606 \| 0.8939 \| 0.9030 \| 0.8984 \| 0.9347 \| 0.8823 \| 0.8555 \| 0.9435 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
meln1k/a2c-AntBulletEnv-v0	meln1k	2022-08-01T08:04:22Z	5	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-08-01T08:03:39Z	--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - metrics: - type: mean_reward value: 2061.72 +/- 70.57 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 --- # A2C Agent playing AntBulletEnv-v0 This is a trained model of a A2C agent playing AntBulletEnv-v0 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
BekirTaha/ppo-LunarLander-v2	BekirTaha	2022-08-01T07:53:28Z	4	0	stable-baselines3	[ "stable-baselines3", "deep-reinforcement-learning", "reinforcement-learning", "region:us" ]	reinforcement-learning	2022-08-01T06:40:27Z	--- tags: - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 --- # "Beyko7/ppo-LunarLander-v2" This is a pre-trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library. ### Usage (with Stable-baselines3) Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed: ``` pip install stable-baselines3 pip install huggingface_sb3 ``` Then, you can use the model like this: ```python import gym from huggingface_sb3 import load_from_hub from stable_baselines3 import PPO from stable_baselines3.common.evaluation import evaluate_policy # Retrieve the model from the hub ## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name}) ## filename = name of the model zip file from the repository checkpoint = load_from_hub(repo_id="Beyko7/ppo-LunarLander-v2", filename="LunarLander-v2.zip") model = PPO.load(checkpoint) # Evaluate the agent eval_env = gym.make('LunarLander-v2') mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True) print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") # Watch the agent play obs = env.reset() for i in range(1000): action, _state = model.predict(obs) obs, reward, done, info = env.step(action) env.render() if done: obs = env.reset() env.close() ``` ### Evaluation Results Mean_reward: 248.30 +/- 23.32882124373712 ---
huggingtweets/kantegory	huggingtweets	2022-08-01T07:26:39Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-08-01T07:26:04Z	--- language: en thumbnail: http://www.huggingtweets.com/kantegory/1659338795219/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1122432883036172288/mYZ4acNy_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">David Dobryakov</div> <div style="text-align: center; font-size: 14px;">@kantegory</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from David Dobryakov. \| Data \| David Dobryakov \| \| --- \| --- \| \| Tweets downloaded \| 3017 \| \| Retweets \| 90 \| \| Short tweets \| 256 \| \| Tweets kept \| 2671 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1g9yc7mp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @kantegory's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2aeg6rk1) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2aeg6rk1/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/kantegory') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
KDHyun08/TAACO_STS	KDHyun08	2022-08-01T05:00:14Z	2,406	2	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "TAACO", "ko", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-07-25T08:19:31Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - transformers - TAACO language: ko --- # TAACO_Similarity 본 모델은 [Sentence-transformers](https://www.SBERT.net)를 기반으로 하며 KLUE의 STS(Sentence Textual Similarity) 데이터셋을 통해 훈련을 진행한 모델입니다. 필자가 제작하고 있는 한국어 문장간 결속성 측정 도구인 K-TAACO(가제)의 지표 중 하나인 문장 간 의미적 결속성을 측정하기 위해 본 모델을 제작하였습니다. 또한 모두의 말뭉치의 문장간 유사도 데이터 등 다양한 데이터를 구해 추가 훈련을 진행할 예정입니다. ## Train Data KLUE-sts-v1.1._train.json NLI-sts-train.tsv ## Usage (Sentence-Transformers) 본 모델을 사용하기 위해서는 [Sentence-transformers](https://www.SBERT.net)를 설치하여야 합니다. ``` pip install -U sentence-transformers ``` 모델을 사용하기 위해서는 아래 코드를 참조하시길 바랍니다. ```python from sentence_transformers import SentenceTransformer, models sentences = ["This is an example sentence", "Each sentence is converted"] embedding_model = models.Transformer( model_name_or_path="KDHyun08/TAACO_STS", max_seq_length=256, do_lower_case=True ) pooling_model = models.Pooling( embedding_model.get_word_embedding_dimension(), pooling_mode_mean_tokens=True, pooling_mode_cls_token=False, pooling_mode_max_tokens=False, ) model = SentenceTransformer(modules=[embedding_model, pooling_model]) embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (실제 문장 간 유사도 비교) [Sentence-transformers](https://www.SBERT.net) 를 설치한 후 아래 내용과 같이 문장 간 유사도를 비교할 수 있습니다. query 변수는 비교 기준이 되는 문장(Source Sentence)이고 비교를 진행할 문장은 docs에 list 형식으로 구성하시면 됩니다. ```python from sentence_transformers import SentenceTransformer, models embedding_model = models.Transformer( model_name_or_path="KDHyun08/TAACO_STS", max_seq_length=256, do_lower_case=True ) pooling_model = models.Pooling( embedding_model.get_word_embedding_dimension(), pooling_mode_mean_tokens=True, pooling_mode_cls_token=False, pooling_mode_max_tokens=False, ) model = SentenceTransformer(modules=[embedding_model, pooling_model]) docs = ['어제는 아내의 생일이었다', '생일을 맞이하여 아침을 준비하겠다고 오전 8시 30분부터 음식을 준비하였다. 주된 메뉴는 스테이크와 낙지볶음, 미역국, 잡채, 소야 등이었다', '스테이크는 자주 하는 음식이어서 자신이 준비하려고 했다', '앞뒤도 1분씩 3번 뒤집고 래스팅을 잘 하면 육즙이 가득한 스테이크가 준비되다', '아내도 그런 스테이크를 좋아한다. 그런데 상상도 못한 일이 벌이지고 말았다', '보통 시즈닝이 되지 않은 원육을 사서 스테이크를 했는데, 이번에는 시즈닝이 된 부챗살을 구입해서 했다', '그런데 케이스 안에 방부제가 들어있는 것을 인지하지 못하고 방부제와 동시에 프라이팬에 올려놓을 것이다', '그것도 인지 못한 체... 앞면을 센 불에 1분을 굽고 뒤집는 순간 방부제가 함께 구어진 것을 알았다', '아내의 생일이라 맛있게 구워보고 싶었는데 어처구니없는 상황이 발생한 것이다', '방부제가 센 불에 녹아서 그런지 물처럼 흘러내렸다', ' 고민을 했다. 방부제가 묻은 부문만 제거하고 다시 구울까 했는데 방부제에 절대 먹지 말라는 문구가 있어서 아깝지만 버리는 방향을 했다', '너무나 안타까웠다', '아침 일찍 아내가 좋아하는 스테이크를 준비하고 그것을 맛있게 먹는 아내의 모습을 보고 싶었는데 전혀 생각지도 못한 상황이 발생해서... 하지만 정신을 추스르고 바로 다른 메뉴로 변경했다', '소야, 소시지 야채볶음..', '아내가 좋아하는지 모르겠지만 냉장고 안에 있는 후랑크소세지를 보니 바로 소야를 해야겠다는 생각이 들었다. 음식은 성공적으로 완성이 되었다', '40번째를 맞이하는 아내의 생일은 성공적으로 준비가 되었다', '맛있게 먹어 준 아내에게도 감사했다', '매년 아내의 생일에 맞이하면 아침마다 생일을 차려야겠다. 오늘도 즐거운 하루가 되었으면 좋겠다', '생일이니까~'] #각 문장의 vector값 encoding document_embeddings = model.encode(docs) query = '생일을 맞이하여 아침을 준비하겠다고 오전 8시 30분부터 음식을 준비하였다' query_embedding = model.encode(query) top_k = min(10, len(docs)) # 코사인 유사도 계산 후, cos_scores = util.pytorch_cos_sim(query_embedding, document_embeddings)[0] # 코사인 유사도 순으로 문장 추출 top_results = torch.topk(cos_scores, k=top_k) print(f"입력 문장: {query}") print(f"\n<입력 문장과 유사한 {top_k} 개의 문장>\n") for i, (score, idx) in enumerate(zip(top_results[0], top_results[1])): print(f"{i+1}: {docs[idx]} {'(유사도: {:.4f})'.format(score)}\n") ``` ## Evaluation Results 위 Usage를 실행하게 되면 아래와 같은 결과가 도출됩니다. 1에 가까울수록 유사한 문장입니다. ``` 입력 문장: 생일을 맞이하여 아침을 준비하겠다고 오전 8시 30분부터 음식을 준비하였다 <입력 문장과 유사한 10 개의 문장> 1: 생일을 맞이하여 아침을 준비하겠다고 오전 8시 30분부터 음식을 준비하였다. 주된 메뉴는 스테이크와 낙지볶음, 미역국, 잡채, 소야 등이었다 (유사도: 0.6687) 2: 매년 아내의 생일에 맞이하면 아침마다 생일을 차려야겠다. 오늘도 즐거운 하루가 되었으면 좋겠다 (유사도: 0.6468) 3: 40번째를 맞이하는 아내의 생일은 성공적으로 준비가 되었다 (유사도: 0.4647) 4: 아내의 생일이라 맛있게 구워보고 싶었는데 어처구니없는 상황이 발생한 것이다 (유사도: 0.4469) 5: 생일이니까~ (유사도: 0.4218) 6: 어제는 아내의 생일이었다 (유사도: 0.4192) 7: 아침 일찍 아내가 좋아하는 스테이크를 준비하고 그것을 맛있게 먹는 아내의 모습을 보고 싶었는데 전혀 생각지도 못한 상황이 발생해서... 하지만 정신을 추스르고 바로 다른 메뉴로 변경했다 (유사도: 0.4156) 8: 맛있게 먹어 준 아내에게도 감사했다 (유사도: 0.3093) 9: 아내가 좋아하는지 모르겠지만 냉장고 안에 있는 후랑크소세지를 보니 바로 소야를 해야겠다는 생각이 들었다. 음식은 성공적으로 완성이 되었다 (유사도: 0.2259) 10: 아내도 그런 스테이크를 좋아한다. 그런데 상상도 못한 일이 벌이지고 말았다 (유사도: 0.1967) ``` DataLoader: `torch.utils.data.dataloader.DataLoader` of length 142 with parameters: ``` {'batch_size': 32, '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": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
wenkai-li/distilroberta-base-finetuned-marktextepoch_n200	wenkai-li	2022-08-01T04:07:13Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-07-31T18:33:57Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-marktextepoch_n200 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. --> # distilroberta-base-finetuned-marktextepoch_n200 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0531 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 2.2313 \| 1.0 \| 1500 \| 2.1592 \| \| 2.1731 \| 2.0 \| 3000 \| 2.1277 \| \| 2.153 \| 3.0 \| 4500 \| 2.1144 \| \| 2.1469 \| 4.0 \| 6000 \| 2.1141 \| \| 2.1281 \| 5.0 \| 7500 \| 2.1374 \| \| 2.1043 \| 6.0 \| 9000 \| 2.1069 \| \| 2.0834 \| 7.0 \| 10500 \| 2.0993 \| \| 2.0602 \| 8.0 \| 12000 \| 2.0817 \| \| 2.024 \| 9.0 \| 13500 \| 2.0918 \| \| 2.0261 \| 10.0 \| 15000 \| 2.0793 \| \| 1.9889 \| 11.0 \| 16500 \| 2.0567 \| \| 1.9915 \| 12.0 \| 18000 \| 2.0700 \| \| 1.9532 \| 13.0 \| 19500 \| 2.0436 \| \| 1.9362 \| 14.0 \| 21000 \| 2.0596 \| \| 1.9024 \| 15.0 \| 22500 \| 2.0189 \| \| 1.9262 \| 16.0 \| 24000 \| 2.0435 \| \| 1.8883 \| 17.0 \| 25500 \| 2.0430 \| \| 1.8867 \| 18.0 \| 27000 \| 2.0416 \| \| 1.8807 \| 19.0 \| 28500 \| 2.0051 \| \| 1.8517 \| 20.0 \| 30000 \| 2.0338 \| \| 1.8357 \| 21.0 \| 31500 \| 2.0166 \| \| 1.8241 \| 22.0 \| 33000 \| 2.0355 \| \| 1.7985 \| 23.0 \| 34500 \| 2.0073 \| \| 1.8061 \| 24.0 \| 36000 \| 2.0473 \| \| 1.7996 \| 25.0 \| 37500 \| 2.0446 \| \| 1.7786 \| 26.0 \| 39000 \| 2.0086 \| \| 1.771 \| 27.0 \| 40500 \| 2.0294 \| \| 1.7549 \| 28.0 \| 42000 \| 2.0127 \| \| 1.7726 \| 29.0 \| 43500 \| 2.0191 \| \| 1.7275 \| 30.0 \| 45000 \| 2.0182 \| \| 1.708 \| 31.0 \| 46500 \| 2.0130 \| \| 1.7345 \| 32.0 \| 48000 \| 2.0155 \| \| 1.7044 \| 33.0 \| 49500 \| 1.9898 \| \| 1.7126 \| 34.0 \| 51000 \| 2.0166 \| \| 1.698 \| 35.0 \| 52500 \| 1.9879 \| \| 1.6637 \| 36.0 \| 54000 \| 2.0311 \| \| 1.6854 \| 37.0 \| 55500 \| 2.0355 \| \| 1.6585 \| 38.0 \| 57000 \| 2.0094 \| \| 1.6418 \| 39.0 \| 58500 \| 2.0042 \| \| 1.667 \| 40.0 \| 60000 \| 2.0116 \| \| 1.6507 \| 41.0 \| 61500 \| 2.0095 \| \| 1.622 \| 42.0 \| 63000 \| 2.0158 \| \| 1.6381 \| 43.0 \| 64500 \| 2.0339 \| \| 1.6099 \| 44.0 \| 66000 \| 2.0082 \| \| 1.6076 \| 45.0 \| 67500 \| 2.0207 \| \| 1.5805 \| 46.0 \| 69000 \| 2.0172 \| \| 1.5862 \| 47.0 \| 70500 \| 2.0132 \| \| 1.5806 \| 48.0 \| 72000 \| 2.0198 \| \| 1.574 \| 49.0 \| 73500 \| 2.0181 \| \| 1.5718 \| 50.0 \| 75000 \| 2.0086 \| \| 1.5591 \| 51.0 \| 76500 \| 1.9832 \| \| 1.5468 \| 52.0 \| 78000 \| 2.0167 \| \| 1.5637 \| 53.0 \| 79500 \| 2.0118 \| \| 1.5117 \| 54.0 \| 81000 \| 2.0290 \| \| 1.5363 \| 55.0 \| 82500 \| 2.0011 \| \| 1.4976 \| 56.0 \| 84000 \| 2.0160 \| \| 1.5129 \| 57.0 \| 85500 \| 2.0224 \| \| 1.4964 \| 58.0 \| 87000 \| 2.0219 \| \| 1.4906 \| 59.0 \| 88500 \| 2.0212 \| \| 1.4941 \| 60.0 \| 90000 \| 2.0255 \| \| 1.4876 \| 61.0 \| 91500 \| 2.0116 \| \| 1.4837 \| 62.0 \| 93000 \| 2.0176 \| \| 1.4661 \| 63.0 \| 94500 \| 2.0388 \| \| 1.4634 \| 64.0 \| 96000 \| 2.0165 \| \| 1.4449 \| 65.0 \| 97500 \| 2.0185 \| \| 1.468 \| 66.0 \| 99000 \| 2.0246 \| \| 1.4567 \| 67.0 \| 100500 \| 2.0244 \| \| 1.4367 \| 68.0 \| 102000 \| 2.0093 \| \| 1.4471 \| 69.0 \| 103500 \| 2.0101 \| \| 1.4255 \| 70.0 \| 105000 \| 2.0248 \| \| 1.4203 \| 71.0 \| 106500 \| 2.0224 \| \| 1.42 \| 72.0 \| 108000 \| 2.0279 \| \| 1.4239 \| 73.0 \| 109500 \| 2.0295 \| \| 1.4126 \| 74.0 \| 111000 \| 2.0196 \| \| 1.4038 \| 75.0 \| 112500 \| 2.0225 \| \| 1.3874 \| 76.0 \| 114000 \| 2.0456 \| \| 1.3758 \| 77.0 \| 115500 \| 2.0423 \| \| 1.3924 \| 78.0 \| 117000 \| 2.0184 \| \| 1.3744 \| 79.0 \| 118500 \| 2.0555 \| \| 1.3622 \| 80.0 \| 120000 \| 2.0387 \| \| 1.3653 \| 81.0 \| 121500 \| 2.0344 \| \| 1.3724 \| 82.0 \| 123000 \| 2.0184 \| \| 1.3684 \| 83.0 \| 124500 \| 2.0285 \| \| 1.3576 \| 84.0 \| 126000 \| 2.0544 \| \| 1.348 \| 85.0 \| 127500 \| 2.0412 \| \| 1.3387 \| 86.0 \| 129000 \| 2.0459 \| \| 1.3416 \| 87.0 \| 130500 \| 2.0329 \| \| 1.3421 \| 88.0 \| 132000 \| 2.0274 \| \| 1.3266 \| 89.0 \| 133500 \| 2.0233 \| \| 1.3183 \| 90.0 \| 135000 \| 2.0319 \| \| 1.322 \| 91.0 \| 136500 \| 2.0080 \| \| 1.32 \| 92.0 \| 138000 \| 2.0472 \| \| 1.304 \| 93.0 \| 139500 \| 2.0538 \| \| 1.3061 \| 94.0 \| 141000 \| 2.0340 \| \| 1.3199 \| 95.0 \| 142500 \| 2.0456 \| \| 1.2985 \| 96.0 \| 144000 \| 2.0167 \| \| 1.3021 \| 97.0 \| 145500 \| 2.0204 \| \| 1.2787 \| 98.0 \| 147000 \| 2.0645 \| \| 1.2879 \| 99.0 \| 148500 \| 2.0345 \| \| 1.2695 \| 100.0 \| 150000 \| 2.0340 \| \| 1.2884 \| 101.0 \| 151500 \| 2.0602 \| \| 1.2747 \| 102.0 \| 153000 \| 2.0667 \| \| 1.2607 \| 103.0 \| 154500 \| 2.0551 \| \| 1.2551 \| 104.0 \| 156000 \| 2.0544 \| \| 1.2557 \| 105.0 \| 157500 \| 2.0553 \| \| 1.2495 \| 106.0 \| 159000 \| 2.0370 \| \| 1.26 \| 107.0 \| 160500 \| 2.0568 \| \| 1.2499 \| 108.0 \| 162000 \| 2.0427 \| \| 1.2438 \| 109.0 \| 163500 \| 2.0184 \| \| 1.2496 \| 110.0 \| 165000 \| 2.0227 \| \| 1.2332 \| 111.0 \| 166500 \| 2.0621 \| \| 1.2231 \| 112.0 \| 168000 \| 2.0661 \| \| 1.211 \| 113.0 \| 169500 \| 2.0673 \| \| 1.217 \| 114.0 \| 171000 \| 2.0544 \| \| 1.2206 \| 115.0 \| 172500 \| 2.0542 \| \| 1.2083 \| 116.0 \| 174000 \| 2.0592 \| \| 1.2205 \| 117.0 \| 175500 \| 2.0451 \| \| 1.2065 \| 118.0 \| 177000 \| 2.0402 \| \| 1.1988 \| 119.0 \| 178500 \| 2.0615 \| \| 1.218 \| 120.0 \| 180000 \| 2.0374 \| \| 1.1917 \| 121.0 \| 181500 \| 2.0349 \| \| 1.1854 \| 122.0 \| 183000 \| 2.0790 \| \| 1.1819 \| 123.0 \| 184500 \| 2.0766 \| \| 1.2029 \| 124.0 \| 186000 \| 2.0364 \| \| 1.1851 \| 125.0 \| 187500 \| 2.0568 \| \| 1.1734 \| 126.0 \| 189000 \| 2.0445 \| \| 1.1701 \| 127.0 \| 190500 \| 2.0770 \| \| 1.1824 \| 128.0 \| 192000 \| 2.0566 \| \| 1.1604 \| 129.0 \| 193500 \| 2.0542 \| \| 1.1733 \| 130.0 \| 195000 \| 2.0525 \| \| 1.1743 \| 131.0 \| 196500 \| 2.0577 \| \| 1.1692 \| 132.0 \| 198000 \| 2.0723 \| \| 1.1519 \| 133.0 \| 199500 \| 2.0567 \| \| 1.1401 \| 134.0 \| 201000 \| 2.0795 \| \| 1.1692 \| 135.0 \| 202500 \| 2.0625 \| \| 1.157 \| 136.0 \| 204000 \| 2.0793 \| \| 1.1495 \| 137.0 \| 205500 \| 2.0782 \| \| 1.1479 \| 138.0 \| 207000 \| 2.0392 \| \| 1.1247 \| 139.0 \| 208500 \| 2.0796 \| \| 1.143 \| 140.0 \| 210000 \| 2.0369 \| \| 1.1324 \| 141.0 \| 211500 \| 2.0699 \| \| 1.1341 \| 142.0 \| 213000 \| 2.0694 \| \| 1.1317 \| 143.0 \| 214500 \| 2.0569 \| \| 1.1254 \| 144.0 \| 216000 \| 2.0545 \| \| 1.1156 \| 145.0 \| 217500 \| 2.0708 \| \| 1.1353 \| 146.0 \| 219000 \| 2.0767 \| \| 1.1312 \| 147.0 \| 220500 \| 2.0523 \| \| 1.1224 \| 148.0 \| 222000 \| 2.0565 \| \| 1.106 \| 149.0 \| 223500 \| 2.0696 \| \| 1.1069 \| 150.0 \| 225000 \| 2.0478 \| \| 1.1011 \| 151.0 \| 226500 \| 2.0475 \| \| 1.0985 \| 152.0 \| 228000 \| 2.0888 \| \| 1.1107 \| 153.0 \| 229500 \| 2.0756 \| \| 1.1058 \| 154.0 \| 231000 \| 2.0812 \| \| 1.1027 \| 155.0 \| 232500 \| 2.0597 \| \| 1.0996 \| 156.0 \| 234000 \| 2.0684 \| \| 1.0987 \| 157.0 \| 235500 \| 2.0629 \| \| 1.0881 \| 158.0 \| 237000 \| 2.0701 \| \| 1.1143 \| 159.0 \| 238500 \| 2.0740 \| \| 1.0823 \| 160.0 \| 240000 \| 2.0869 \| \| 1.0925 \| 161.0 \| 241500 \| 2.0567 \| \| 1.1034 \| 162.0 \| 243000 \| 2.0833 \| \| 1.0759 \| 163.0 \| 244500 \| 2.0585 \| \| 1.0998 \| 164.0 \| 246000 \| 2.0293 \| \| 1.0891 \| 165.0 \| 247500 \| 2.0608 \| \| 1.1036 \| 166.0 \| 249000 \| 2.0831 \| \| 1.076 \| 167.0 \| 250500 \| 2.0979 \| \| 1.0895 \| 168.0 \| 252000 \| 2.0882 \| \| 1.0825 \| 169.0 \| 253500 \| 2.0742 \| \| 1.0793 \| 170.0 \| 255000 \| 2.0841 \| \| 1.079 \| 171.0 \| 256500 \| 2.0829 \| \| 1.0653 \| 172.0 \| 258000 \| 2.0888 \| \| 1.0834 \| 173.0 \| 259500 \| 2.0784 \| \| 1.0721 \| 174.0 \| 261000 \| 2.0859 \| \| 1.0712 \| 175.0 \| 262500 \| 2.0810 \| \| 1.0494 \| 176.0 \| 264000 \| 2.0605 \| \| 1.0654 \| 177.0 \| 265500 \| 2.0623 \| \| 1.077 \| 178.0 \| 267000 \| 2.0756 \| \| 1.056 \| 179.0 \| 268500 \| 2.0782 \| \| 1.0523 \| 180.0 \| 270000 \| 2.0966 \| \| 1.0656 \| 181.0 \| 271500 \| 2.0750 \| \| 1.0636 \| 182.0 \| 273000 \| 2.0769 \| \| 1.0851 \| 183.0 \| 274500 \| 2.0872 \| \| 1.0562 \| 184.0 \| 276000 \| 2.0893 \| \| 1.0534 \| 185.0 \| 277500 \| 2.0661 \| \| 1.0514 \| 186.0 \| 279000 \| 2.0712 \| \| 1.062 \| 187.0 \| 280500 \| 2.0769 \| \| 1.0683 \| 188.0 \| 282000 \| 2.0765 \| \| 1.0606 \| 189.0 \| 283500 \| 2.0735 \| \| 1.0555 \| 190.0 \| 285000 \| 2.0710 \| \| 1.0568 \| 191.0 \| 286500 \| 2.0860 \| \| 1.0502 \| 192.0 \| 288000 \| 2.0587 \| \| 1.0437 \| 193.0 \| 289500 \| 2.0998 \| \| 1.0534 \| 194.0 \| 291000 \| 2.0418 \| \| 1.062 \| 195.0 \| 292500 \| 2.0724 \| \| 1.0457 \| 196.0 \| 294000 \| 2.0612 \| \| 1.0501 \| 197.0 \| 295500 \| 2.1012 \| \| 1.0728 \| 198.0 \| 297000 \| 2.0721 \| \| 1.0413 \| 199.0 \| 298500 \| 2.0535 \| \| 1.0461 \| 200.0 \| 300000 \| 2.0531 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
Izarel/distilbert-base-uncased_fine_tuned_body_text	Izarel	2022-08-01T03:52:20Z	4	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-31T19:03:36Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - recall - precision - f1 model-index: - name: distilbert-base-uncased_fine_tuned_body_text results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_fine_tuned_body_text 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: - Loss: 1.2153 - Accuracy: {'accuracy': 0.8827265261428963} - Recall: {'recall': 0.8641975308641975} - Precision: {'precision': 0.8900034993584509} - F1: {'f1': 0.8769106999195494} ## Model description More information needed ## 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Recall \| Precision \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------------------------------:\|:------------------------------:\|:---------------------------------:\|:--------------------------:\| \| 0.3056 \| 1.0 \| 2284 \| 0.3040 \| {'accuracy': 0.8874897344648235} \| {'recall': 0.8466417487824216} \| {'precision': 0.914261252446184} \| {'f1': 0.8791531902381653} \| \| 0.2279 \| 2.0 \| 4568 \| 0.2891 \| {'accuracy': 0.8908294552422666} \| {'recall': 0.8606863744478424} \| {'precision': 0.9086452230060983} \| {'f1': 0.8840158213122382} \| \| 0.1467 \| 3.0 \| 6852 \| 0.3580 \| {'accuracy': 0.8882562277580072} \| {'recall': 0.8452825914599615} \| {'precision': 0.9170557876628164} \| {'f1': 0.8797076678257796} \| \| 0.0921 \| 4.0 \| 9136 \| 0.4560 \| {'accuracy': 0.8754448398576512} \| {'recall': 0.8948918337297542} \| {'precision': 0.8543468858131488} \| {'f1': 0.8741494717043756} \| \| 0.0587 \| 5.0 \| 11420 \| 0.5701 \| {'accuracy': 0.8768135778811935} \| {'recall': 0.8139087099331748} \| {'precision': 0.9221095855254716} \| {'f1': 0.8646372277704246} \| \| 0.0448 \| 6.0 \| 13704 \| 0.6738 \| {'accuracy': 0.8767040788393101} \| {'recall': 0.8794880507418734} \| {'precision': 0.8673070479168994} \| {'f1': 0.873355078168935} \| \| 0.0289 \| 7.0 \| 15988 \| 0.7965 \| {'accuracy': 0.8798248015329866} \| {'recall': 0.8491335372069317} \| {'precision': 0.8967703349282297} \| {'f1': 0.8723020536389552} \| \| 0.0214 \| 8.0 \| 18272 \| 0.8244 \| {'accuracy': 0.8811387900355871} \| {'recall': 0.8576282704723072} \| {'precision': 0.8922931887815225} \| {'f1': 0.8746173837712965} \| \| 0.0147 \| 9.0 \| 20556 \| 0.8740 \| {'accuracy': 0.8806460443471119} \| {'recall': 0.8669158455091177} \| {'precision': 0.8839357893521191} \| {'f1': 0.8753430924062213} \| \| 0.0099 \| 10.0 \| 22840 \| 0.9716 \| {'accuracy': 0.8788940596769779} \| {'recall': 0.8694076339336279} \| {'precision': 0.8787635947338294} \| {'f1': 0.8740605784559327} \| \| 0.0092 \| 11.0 \| 25124 \| 1.0296 \| {'accuracy': 0.8822885299753627} \| {'recall': 0.8669158455091177} \| {'precision': 0.8870089233978444} \| {'f1': 0.876847290640394} \| \| 0.0039 \| 12.0 \| 27408 \| 1.0974 \| {'accuracy': 0.8787845606350945} \| {'recall': 0.8628383735417374} \| {'precision': 0.8836561883772184} \| {'f1': 0.8731232091690544} \| \| 0.0053 \| 13.0 \| 29692 \| 1.0833 \| {'accuracy': 0.8799890500958116} \| {'recall': 0.8503794314191868} \| {'precision': 0.8960496479293472} \| {'f1': 0.8726173872617387} \| \| 0.0032 \| 14.0 \| 31976 \| 1.1731 \| {'accuracy': 0.8813030385984123} \| {'recall': 0.8705402650356778} \| {'precision': 0.8823326828148318} \| {'f1': 0.8763968072976055} \| \| 0.0017 \| 15.0 \| 34260 \| 1.2153 \| {'accuracy': 0.8827265261428963} \| {'recall': 0.8641975308641975} \| {'precision': 0.8900034993584509} \| {'f1': 0.8769106999195494} \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
reachrkr/Cartpole-v1	reachrkr	2022-08-01T02:16:58Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2022-08-01T02:16:50Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Cartpole-v1 results: - metrics: - type: mean_reward value: 40.00 +/- 18.57 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 --- # 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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
notmaineyy/bert-base-multilingual-cased-finetuned-ner	notmaineyy	2022-08-01T01:37:57Z	5	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "token-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-07-21T01:33:49Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: notmaineyy/bert-base-multilingual-cased-finetuned-ner 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. --> # notmaineyy/bert-base-multilingual-cased-finetuned-ner This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0248 - Validation Loss: 0.0568 - Train Precision: 0.9424 - Train Recall: 0.9471 - Train F1: 0.9448 - Train Accuracy: 0.9863 - 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 10530, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Precision \| Train Recall \| Train F1 \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:---------------:\|:------------:\|:--------:\|:--------------:\|:-----:\| \| 0.1335 \| 0.0705 \| 0.9152 \| 0.9204 \| 0.9178 \| 0.9806 \| 0 \| \| 0.0497 \| 0.0562 \| 0.9335 \| 0.9472 \| 0.9403 \| 0.9851 \| 1 \| \| 0.0248 \| 0.0568 \| 0.9424 \| 0.9471 \| 0.9448 \| 0.9863 \| 2 \| ### Framework versions - Transformers 4.21.0 - TensorFlow 2.8.2 - Datasets 2.4.0 - Tokenizers 0.12.1
RedPandaAINLP/opus-mt-en-ro-finetuned-en-to-ro	RedPandaAINLP	2022-08-01T00:11:22Z	15	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "generated_from_trainer", "dataset:wmt16", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-07-31T22:39:13Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: opus-mt-en-ro-finetuned-en-to-ro results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16 type: wmt16 config: ro-en split: train args: ro-en metrics: - name: Bleu type: bleu value: 28.1505 --- <!-- 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. --> # opus-mt-en-ro-finetuned-en-to-ro This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.2886 - Bleu: 28.1505 - Gen Len: 34.1036 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 0.7437 \| 1.0 \| 38145 \| 1.2886 \| 28.1505 \| 34.1036 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
elopezlopez/distilbert-base-uncased_fold_4_ternary	elopezlopez	2022-08-01T00:10:08Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-31T23:52:47Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_4_ternary results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_fold_4_ternary This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2981 - F1: 0.7565 ## Model description More information needed ## 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: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 289 \| 0.5588 \| 0.6984 \| \| 0.5547 \| 2.0 \| 578 \| 0.5283 \| 0.7336 \| \| 0.5547 \| 3.0 \| 867 \| 0.7038 \| 0.7202 \| \| 0.2479 \| 4.0 \| 1156 \| 0.8949 \| 0.7284 \| \| 0.2479 \| 5.0 \| 1445 \| 0.9959 \| 0.7286 \| \| 0.1181 \| 6.0 \| 1734 \| 1.0663 \| 0.7311 \| \| 0.0508 \| 7.0 \| 2023 \| 1.2377 \| 0.7054 \| \| 0.0508 \| 8.0 \| 2312 \| 1.2981 \| 0.7565 \| \| 0.0185 \| 9.0 \| 2601 \| 1.3532 \| 0.7407 \| \| 0.0185 \| 10.0 \| 2890 \| 1.5365 \| 0.7333 \| \| 0.0103 \| 11.0 \| 3179 \| 1.5184 \| 0.7423 \| \| 0.0103 \| 12.0 \| 3468 \| 1.6009 \| 0.7420 \| \| 0.0123 \| 13.0 \| 3757 \| 1.6395 \| 0.7402 \| \| 0.008 \| 14.0 \| 4046 \| 1.6838 \| 0.7429 \| \| 0.008 \| 15.0 \| 4335 \| 1.6176 \| 0.7490 \| \| 0.0012 \| 16.0 \| 4624 \| 1.7873 \| 0.7345 \| \| 0.0012 \| 17.0 \| 4913 \| 1.6761 \| 0.7412 \| \| 0.0044 \| 18.0 \| 5202 \| 1.7356 \| 0.7417 \| \| 0.0044 \| 19.0 \| 5491 \| 1.7686 \| 0.7502 \| \| 0.0045 \| 20.0 \| 5780 \| 1.7668 \| 0.7406 \| \| 0.0017 \| 21.0 \| 6069 \| 1.8411 \| 0.7381 \| \| 0.0017 \| 22.0 \| 6358 \| 1.8147 \| 0.7469 \| \| 0.0012 \| 23.0 \| 6647 \| 1.8028 \| 0.7489 \| \| 0.0012 \| 24.0 \| 6936 \| 1.8147 \| 0.7453 \| \| 0.0026 \| 25.0 \| 7225 \| 1.8257 \| 0.7475 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
keithanpai/tiny-random-vit-finetuned-eurosat	keithanpai	2022-08-01T00:08:25Z	73	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-08-01T00:06:15Z	--- tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: tiny-random-vit-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.6646706586826348 --- <!-- 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. --> # tiny-random-vit-finetuned-eurosat This model is a fine-tuned version of [hf-internal-testing/tiny-random-vit](https://huggingface.co/hf-internal-testing/tiny-random-vit) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0488 - Accuracy: 0.6647 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.1192 \| 0.99 \| 70 \| 1.0867 \| 0.6627 \| \| 1.067 \| 1.99 \| 140 \| 1.0563 \| 0.6657 \| \| 0.9719 \| 2.99 \| 210 \| 1.0488 \| 0.6647 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
elopezlopez/xlnet-base-cased_fold_3_binary	elopezlopez	2022-07-31T23:37:52Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlnet", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-31T23:14:01Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlnet-base-cased_fold_3_binary 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. --> # xlnet-base-cased_fold_3_binary This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3616 - F1: 0.7758 ## Model description More information needed ## 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: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 289 \| 0.4668 \| 0.7666 \| \| 0.4142 \| 2.0 \| 578 \| 0.4259 \| 0.7631 \| \| 0.4142 \| 3.0 \| 867 \| 0.6744 \| 0.7492 \| \| 0.235 \| 4.0 \| 1156 \| 0.8879 \| 0.7678 \| \| 0.235 \| 5.0 \| 1445 \| 1.0036 \| 0.7639 \| \| 0.1297 \| 6.0 \| 1734 \| 1.1427 \| 0.7616 \| \| 0.0894 \| 7.0 \| 2023 \| 1.2126 \| 0.7626 \| \| 0.0894 \| 8.0 \| 2312 \| 1.5098 \| 0.7433 \| \| 0.0473 \| 9.0 \| 2601 \| 1.3616 \| 0.7758 \| \| 0.0473 \| 10.0 \| 2890 \| 1.5966 \| 0.7579 \| \| 0.0325 \| 11.0 \| 3179 \| 1.6669 \| 0.7508 \| \| 0.0325 \| 12.0 \| 3468 \| 1.7401 \| 0.7437 \| \| 0.0227 \| 13.0 \| 3757 \| 1.7797 \| 0.7515 \| \| 0.0224 \| 14.0 \| 4046 \| 1.7349 \| 0.7418 \| \| 0.0224 \| 15.0 \| 4335 \| 1.7527 \| 0.7595 \| \| 0.0152 \| 16.0 \| 4624 \| 1.7492 \| 0.7634 \| \| 0.0152 \| 17.0 \| 4913 \| 1.8178 \| 0.7628 \| \| 0.0117 \| 18.0 \| 5202 \| 1.7736 \| 0.7688 \| \| 0.0117 \| 19.0 \| 5491 \| 1.8449 \| 0.7704 \| \| 0.0055 \| 20.0 \| 5780 \| 1.8687 \| 0.7652 \| \| 0.0065 \| 21.0 \| 6069 \| 1.8083 \| 0.7669 \| \| 0.0065 \| 22.0 \| 6358 \| 1.8568 \| 0.7559 \| \| 0.0054 \| 23.0 \| 6647 \| 1.8760 \| 0.7678 \| \| 0.0054 \| 24.0 \| 6936 \| 1.8948 \| 0.7697 \| \| 0.0048 \| 25.0 \| 7225 \| 1.9109 \| 0.7680 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
elopezlopez/distilbert-base-uncased_fold_2_ternary	elopezlopez	2022-07-31T23:35:04Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-31T23:17:46Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_2_ternary results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_fold_2_ternary This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5810 - F1: 0.7620 ## Model description More information needed ## 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: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 294 \| 0.5886 \| 0.7239 \| \| 0.557 \| 2.0 \| 588 \| 0.5085 \| 0.7524 \| \| 0.557 \| 3.0 \| 882 \| 0.6332 \| 0.7530 \| \| 0.2456 \| 4.0 \| 1176 \| 0.8749 \| 0.7161 \| \| 0.2456 \| 5.0 \| 1470 \| 1.0601 \| 0.7371 \| \| 0.1112 \| 6.0 \| 1764 \| 1.1885 \| 0.7451 \| \| 0.0484 \| 7.0 \| 2058 \| 1.3027 \| 0.7240 \| \| 0.0484 \| 8.0 \| 2352 \| 1.4647 \| 0.7259 \| \| 0.0259 \| 9.0 \| 2646 \| 1.4476 \| 0.7322 \| \| 0.0259 \| 10.0 \| 2940 \| 1.4826 \| 0.7388 \| \| 0.0164 \| 11.0 \| 3234 \| 1.5869 \| 0.7333 \| \| 0.0109 \| 12.0 \| 3528 \| 1.5954 \| 0.7539 \| \| 0.0109 \| 13.0 \| 3822 \| 1.5810 \| 0.7620 \| \| 0.0082 \| 14.0 \| 4116 \| 1.7165 \| 0.7335 \| \| 0.0082 \| 15.0 \| 4410 \| 1.8152 \| 0.7414 \| \| 0.004 \| 16.0 \| 4704 \| 1.7411 \| 0.7474 \| \| 0.004 \| 17.0 \| 4998 \| 1.8692 \| 0.7355 \| \| 0.0034 \| 18.0 \| 5292 \| 1.8727 \| 0.7303 \| \| 0.0009 \| 19.0 \| 5586 \| 1.9813 \| 0.7305 \| \| 0.0009 \| 20.0 \| 5880 \| 1.9764 \| 0.7391 \| \| 0.0012 \| 21.0 \| 6174 \| 2.0170 \| 0.7291 \| \| 0.0012 \| 22.0 \| 6468 \| 2.0240 \| 0.7391 \| \| 0.0004 \| 23.0 \| 6762 \| 2.0311 \| 0.7352 \| \| 0.0014 \| 24.0 \| 7056 \| 2.0174 \| 0.7334 \| \| 0.0014 \| 25.0 \| 7350 \| 2.0282 \| 0.7381 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
keithanpai/vit-base-patch32-384-finetuned-eurosat	keithanpai	2022-07-31T22:51:54Z	54	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-07-31T19:46:59Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch32-384-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8423153692614771 --- <!-- 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-patch32-384-finetuned-eurosat This model is a fine-tuned version of [google/vit-base-patch32-384](https://huggingface.co/google/vit-base-patch32-384) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4381 - Accuracy: 0.8423 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.607 \| 0.99 \| 70 \| 0.5609 \| 0.8014 \| \| 0.5047 \| 1.99 \| 140 \| 0.4634 \| 0.8373 \| \| 0.4089 \| 2.99 \| 210 \| 0.4381 \| 0.8423 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
wenkai-li/distilroberta-base-finetuned-wikitextepoch_150	wenkai-li	2022-07-31T22:09:24Z	5	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-07-31T18:31:03Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-wikitextepoch_150 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilroberta-base-finetuned-wikitextepoch_150 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8929 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 2.2428 \| 1.0 \| 1121 \| 2.0500 \| \| 2.1209 \| 2.0 \| 2242 \| 1.9996 \| \| 2.0665 \| 3.0 \| 3363 \| 1.9501 \| \| 2.0179 \| 4.0 \| 4484 \| 1.9311 \| \| 1.9759 \| 5.0 \| 5605 \| 1.9255 \| \| 1.9089 \| 6.0 \| 6726 \| 1.8805 \| \| 1.9143 \| 7.0 \| 7847 \| 1.8715 \| \| 1.8744 \| 8.0 \| 8968 \| 1.8671 \| \| 1.858 \| 9.0 \| 10089 \| 1.8592 \| \| 1.8141 \| 10.0 \| 11210 \| 1.8578 \| \| 1.7917 \| 11.0 \| 12331 \| 1.8574 \| \| 1.7752 \| 12.0 \| 13452 \| 1.8423 \| \| 1.7722 \| 13.0 \| 14573 \| 1.8287 \| \| 1.7354 \| 14.0 \| 15694 \| 1.8396 \| \| 1.7217 \| 15.0 \| 16815 \| 1.8244 \| \| 1.6968 \| 16.0 \| 17936 \| 1.8278 \| \| 1.659 \| 17.0 \| 19057 \| 1.8412 \| \| 1.6442 \| 18.0 \| 20178 \| 1.8328 \| \| 1.6441 \| 19.0 \| 21299 \| 1.8460 \| \| 1.6267 \| 20.0 \| 22420 \| 1.8343 \| \| 1.612 \| 21.0 \| 23541 \| 1.8249 \| \| 1.5963 \| 22.0 \| 24662 \| 1.8253 \| \| 1.6101 \| 23.0 \| 25783 \| 1.7843 \| \| 1.5747 \| 24.0 \| 26904 \| 1.8047 \| \| 1.5559 \| 25.0 \| 28025 \| 1.8618 \| \| 1.5484 \| 26.0 \| 29146 \| 1.8660 \| \| 1.5411 \| 27.0 \| 30267 \| 1.8318 \| \| 1.5247 \| 28.0 \| 31388 \| 1.8216 \| \| 1.5278 \| 29.0 \| 32509 \| 1.8075 \| \| 1.4954 \| 30.0 \| 33630 \| 1.8073 \| \| 1.4863 \| 31.0 \| 34751 \| 1.7958 \| \| 1.4821 \| 32.0 \| 35872 \| 1.8080 \| \| 1.4357 \| 33.0 \| 36993 \| 1.8373 \| \| 1.4602 \| 34.0 \| 38114 \| 1.8199 \| \| 1.447 \| 35.0 \| 39235 \| 1.8325 \| \| 1.4292 \| 36.0 \| 40356 \| 1.8075 \| \| 1.4174 \| 37.0 \| 41477 \| 1.8168 \| \| 1.4103 \| 38.0 \| 42598 \| 1.8095 \| \| 1.4168 \| 39.0 \| 43719 \| 1.8233 \| \| 1.4005 \| 40.0 \| 44840 \| 1.8388 \| \| 1.3799 \| 41.0 \| 45961 \| 1.8235 \| \| 1.3657 \| 42.0 \| 47082 \| 1.8298 \| \| 1.3559 \| 43.0 \| 48203 \| 1.8165 \| \| 1.3723 \| 44.0 \| 49324 \| 1.8059 \| \| 1.3535 \| 45.0 \| 50445 \| 1.8451 \| \| 1.3533 \| 46.0 \| 51566 \| 1.8458 \| \| 1.3469 \| 47.0 \| 52687 \| 1.8237 \| \| 1.3247 \| 48.0 \| 53808 \| 1.8264 \| \| 1.3142 \| 49.0 \| 54929 \| 1.8209 \| \| 1.2958 \| 50.0 \| 56050 \| 1.8244 \| \| 1.293 \| 51.0 \| 57171 \| 1.8311 \| \| 1.2784 \| 52.0 \| 58292 \| 1.8287 \| \| 1.2731 \| 53.0 \| 59413 \| 1.8600 \| \| 1.2961 \| 54.0 \| 60534 \| 1.8086 \| \| 1.2739 \| 55.0 \| 61655 \| 1.8303 \| \| 1.2716 \| 56.0 \| 62776 \| 1.8214 \| \| 1.2459 \| 57.0 \| 63897 \| 1.8440 \| \| 1.2492 \| 58.0 \| 65018 \| 1.8503 \| \| 1.2393 \| 59.0 \| 66139 \| 1.8316 \| \| 1.2077 \| 60.0 \| 67260 \| 1.8283 \| \| 1.2426 \| 61.0 \| 68381 \| 1.8413 \| \| 1.2032 \| 62.0 \| 69502 \| 1.8461 \| \| 1.2123 \| 63.0 \| 70623 \| 1.8469 \| \| 1.2069 \| 64.0 \| 71744 \| 1.8478 \| \| 1.198 \| 65.0 \| 72865 \| 1.8479 \| \| 1.1972 \| 66.0 \| 73986 \| 1.8516 \| \| 1.1885 \| 67.0 \| 75107 \| 1.8341 \| \| 1.1784 \| 68.0 \| 76228 \| 1.8322 \| \| 1.1866 \| 69.0 \| 77349 \| 1.8559 \| \| 1.1648 \| 70.0 \| 78470 \| 1.8758 \| \| 1.1595 \| 71.0 \| 79591 \| 1.8684 \| \| 1.1661 \| 72.0 \| 80712 \| 1.8553 \| \| 1.1478 \| 73.0 \| 81833 \| 1.8658 \| \| 1.1488 \| 74.0 \| 82954 \| 1.8452 \| \| 1.1538 \| 75.0 \| 84075 \| 1.8505 \| \| 1.1267 \| 76.0 \| 85196 \| 1.8430 \| \| 1.1339 \| 77.0 \| 86317 \| 1.8333 \| \| 1.118 \| 78.0 \| 87438 \| 1.8419 \| \| 1.12 \| 79.0 \| 88559 \| 1.8669 \| \| 1.1144 \| 80.0 \| 89680 \| 1.8647 \| \| 1.104 \| 81.0 \| 90801 \| 1.8643 \| \| 1.0864 \| 82.0 \| 91922 \| 1.8528 \| \| 1.0863 \| 83.0 \| 93043 \| 1.8456 \| \| 1.0912 \| 84.0 \| 94164 \| 1.8509 \| \| 1.0873 \| 85.0 \| 95285 \| 1.8690 \| \| 1.0862 \| 86.0 \| 96406 \| 1.8577 \| \| 1.0879 \| 87.0 \| 97527 \| 1.8612 \| \| 1.0783 \| 88.0 \| 98648 \| 1.8410 \| \| 1.0618 \| 89.0 \| 99769 \| 1.8517 \| \| 1.0552 \| 90.0 \| 100890 \| 1.8459 \| \| 1.0516 \| 91.0 \| 102011 \| 1.8723 \| \| 1.0424 \| 92.0 \| 103132 \| 1.8832 \| \| 1.0478 \| 93.0 \| 104253 \| 1.8922 \| \| 1.0523 \| 94.0 \| 105374 \| 1.8753 \| \| 1.027 \| 95.0 \| 106495 \| 1.8625 \| \| 1.0364 \| 96.0 \| 107616 \| 1.8673 \| \| 1.0203 \| 97.0 \| 108737 \| 1.8806 \| \| 1.0309 \| 98.0 \| 109858 \| 1.8644 \| \| 1.0174 \| 99.0 \| 110979 \| 1.8659 \| \| 1.0184 \| 100.0 \| 112100 \| 1.8590 \| \| 1.0234 \| 101.0 \| 113221 \| 1.8614 \| \| 1.013 \| 102.0 \| 114342 \| 1.8866 \| \| 1.0092 \| 103.0 \| 115463 \| 1.8770 \| \| 1.0051 \| 104.0 \| 116584 \| 1.8445 \| \| 1.0105 \| 105.0 \| 117705 \| 1.8512 \| \| 1.0233 \| 106.0 \| 118826 \| 1.8896 \| \| 0.9967 \| 107.0 \| 119947 \| 1.8687 \| \| 0.9795 \| 108.0 \| 121068 \| 1.8618 \| \| 0.9846 \| 109.0 \| 122189 \| 1.8877 \| \| 0.9958 \| 110.0 \| 123310 \| 1.8522 \| \| 0.9689 \| 111.0 \| 124431 \| 1.8765 \| \| 0.9879 \| 112.0 \| 125552 \| 1.8692 \| \| 0.99 \| 113.0 \| 126673 \| 1.8689 \| \| 0.9798 \| 114.0 \| 127794 \| 1.8898 \| \| 0.9676 \| 115.0 \| 128915 \| 1.8782 \| \| 0.9759 \| 116.0 \| 130036 \| 1.8840 \| \| 0.9576 \| 117.0 \| 131157 \| 1.8662 \| \| 0.9637 \| 118.0 \| 132278 \| 1.8984 \| \| 0.9645 \| 119.0 \| 133399 \| 1.8872 \| \| 0.9793 \| 120.0 \| 134520 \| 1.8705 \| \| 0.9643 \| 121.0 \| 135641 \| 1.9036 \| \| 0.961 \| 122.0 \| 136762 \| 1.8683 \| \| 0.9496 \| 123.0 \| 137883 \| 1.8785 \| \| 0.946 \| 124.0 \| 139004 \| 1.8912 \| \| 0.9681 \| 125.0 \| 140125 \| 1.8837 \| \| 0.9403 \| 126.0 \| 141246 \| 1.8824 \| \| 0.9452 \| 127.0 \| 142367 \| 1.8824 \| \| 0.9437 \| 128.0 \| 143488 \| 1.8665 \| \| 0.945 \| 129.0 \| 144609 \| 1.8655 \| \| 0.9453 \| 130.0 \| 145730 \| 1.8695 \| \| 0.9238 \| 131.0 \| 146851 \| 1.8697 \| \| 0.9176 \| 132.0 \| 147972 \| 1.8618 \| \| 0.9405 \| 133.0 \| 149093 \| 1.8679 \| \| 0.9184 \| 134.0 \| 150214 \| 1.9025 \| \| 0.9298 \| 135.0 \| 151335 \| 1.9045 \| \| 0.9215 \| 136.0 \| 152456 \| 1.9014 \| \| 0.9249 \| 137.0 \| 153577 \| 1.8505 \| \| 0.9246 \| 138.0 \| 154698 \| 1.8542 \| \| 0.9205 \| 139.0 \| 155819 \| 1.8731 \| \| 0.9368 \| 140.0 \| 156940 \| 1.8673 \| \| 0.9251 \| 141.0 \| 158061 \| 1.8835 \| \| 0.9224 \| 142.0 \| 159182 \| 1.8727 \| \| 0.9326 \| 143.0 \| 160303 \| 1.8380 \| \| 0.916 \| 144.0 \| 161424 \| 1.8857 \| \| 0.9361 \| 145.0 \| 162545 \| 1.8547 \| \| 0.9121 \| 146.0 \| 163666 \| 1.8587 \| \| 0.9156 \| 147.0 \| 164787 \| 1.8863 \| \| 0.9131 \| 148.0 \| 165908 \| 1.8809 \| \| 0.9185 \| 149.0 \| 167029 \| 1.8734 \| \| 0.9183 \| 150.0 \| 168150 \| 1.8929 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.5.0 - Datasets 2.4.0 - Tokenizers 0.12.1
elopezlopez/distilbert-base-uncased_fold_4_binary	elopezlopez	2022-07-31T22:04:12Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-31T21:54:02Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_4_binary results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased_fold_4_binary This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2977 - F1: 0.8083 ## Model description More information needed ## 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: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 289 \| 0.3701 \| 0.7903 \| \| 0.4005 \| 2.0 \| 578 \| 0.3669 \| 0.7994 \| \| 0.4005 \| 3.0 \| 867 \| 0.5038 \| 0.7955 \| \| 0.1945 \| 4.0 \| 1156 \| 0.6353 \| 0.8006 \| \| 0.1945 \| 5.0 \| 1445 \| 0.8974 \| 0.7826 \| \| 0.0909 \| 6.0 \| 1734 \| 0.8533 \| 0.7764 \| \| 0.0389 \| 7.0 \| 2023 \| 0.9969 \| 0.7957 \| \| 0.0389 \| 8.0 \| 2312 \| 1.0356 \| 0.7952 \| \| 0.0231 \| 9.0 \| 2601 \| 1.1538 \| 0.7963 \| \| 0.0231 \| 10.0 \| 2890 \| 1.2011 \| 0.7968 \| \| 0.0051 \| 11.0 \| 3179 \| 1.2329 \| 0.7935 \| \| 0.0051 \| 12.0 \| 3468 \| 1.2829 \| 0.8056 \| \| 0.0066 \| 13.0 \| 3757 \| 1.2946 \| 0.7956 \| \| 0.004 \| 14.0 \| 4046 \| 1.2977 \| 0.8083 \| \| 0.004 \| 15.0 \| 4335 \| 1.3970 \| 0.7957 \| \| 0.0007 \| 16.0 \| 4624 \| 1.3361 \| 0.7917 \| \| 0.0007 \| 17.0 \| 4913 \| 1.5782 \| 0.7954 \| \| 0.0107 \| 18.0 \| 5202 \| 1.4641 \| 0.7900 \| \| 0.0107 \| 19.0 \| 5491 \| 1.4490 \| 0.7957 \| \| 0.0058 \| 20.0 \| 5780 \| 1.4607 \| 0.7932 \| \| 0.0016 \| 21.0 \| 6069 \| 1.5048 \| 0.7939 \| \| 0.0016 \| 22.0 \| 6358 \| 1.5219 \| 0.7945 \| \| 0.0027 \| 23.0 \| 6647 \| 1.4783 \| 0.7937 \| \| 0.0027 \| 24.0 \| 6936 \| 1.4715 \| 0.7981 \| \| 0.0004 \| 25.0 \| 7225 \| 1.4989 \| 0.7900 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
oMateos2020/pegasus-newsroom-cnn_full-adam8bit	oMateos2020	2022-07-31T21:02:17Z	10	0	transformers	[ "transformers", "pytorch", "pegasus", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-07-29T07:55:23Z	--- tags: - generated_from_trainer model-index: - name: pegasus-newsroom-cnn_full-adam8bit results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pegasus-newsroom-cnn_full-adam8bit This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-newsroom) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 2.9826 - eval_rouge1: 38.2456 - eval_rouge2: 17.3966 - eval_rougeL: 26.9273 - eval_rougeLsum: 35.3265 - eval_gen_len: 69.658 - eval_runtime: 13626.7467 - eval_samples_per_second: 0.183 - eval_steps_per_second: 0.012 - epoch: 0.22 - step: 250 ## Model description More information needed ## 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: 0.0016 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.1 ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
DS-20202/DoubleHardDebias	DS-20202	2022-07-31T20:32:45Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-07-31T12:08:09Z	--- title: Double Hard Debiasing emoji: 👁 colorFrom: blue colorTo: pink sdk: gradio sdk_version: 3.1.1 app_file: app.py pinned: false license: mit --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
neuralmagic/oBERT-teacher-squadv1	neuralmagic	2022-07-31T19:52:34Z	396	0	transformers	[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]	null	2022-05-25T13:47:26Z	--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # SQuADv1 teacher This model is used as a teacher for all runs on the SQuADv1 downstream task in the paper [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). SQuADv1 dev-set: ``` EM = 81.41 F1 = 88.54 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```
neuralmagic/oBERT-teacher-qqp	neuralmagic	2022-07-31T19:52:34Z	8	0	transformers	[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:qqp", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]	null	2022-05-25T13:55:22Z	--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: qqp --- # QQP teacher This model is used as a teacher for all runs on the QQP downstream task in the paper [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). QQP dev-set: ``` accuracy = 91.06 F1 = 88.00 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```
neuralmagic/oBERT-6-downstream-pruned-block4-90-squadv1	neuralmagic	2022-07-31T19:52:34Z	2	0	transformers	[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]	null	2022-05-25T14:00:31Z	--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-6-downstream-pruned-block4-90-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 6 Layers - Sparsity 90% - 4-block`. ``` Pruning method: oBERT downstream block-4 Paper: https://arxiv.org/abs/2203.07259 Dataset: SQuADv1 Sparsity: 90% Number of layers: 6 ``` The dev-set performance of this model: ``` EM = 77.65 F1 = 85.34 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```
neuralmagic/oBERT-6-downstream-pruned-block4-80-squadv1	neuralmagic	2022-07-31T19:52:34Z	6	0	transformers	[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]	null	2022-05-25T14:00:18Z	--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-6-downstream-pruned-block4-80-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 6 Layers - Sparsity 80% - 4-block`. ``` Pruning method: oBERT downstream block-4 Paper: https://arxiv.org/abs/2203.07259 Dataset: SQuADv1 Sparsity: 80% Number of layers: 6 ``` The dev-set performance of this model: ``` EM = 79.55 F1 = 87.00 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```
neuralmagic/oBERT-6-downstream-pruned-block4-80-QAT-squadv1	neuralmagic	2022-07-31T19:52:34Z	8	0	transformers	[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]	null	2022-05-25T19:20:49Z	--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-6-downstream-pruned-block4-80-QAT-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 6 Layers - Sparsity 80% - 4-block + QAT`. ``` Pruning method: oBERT downstream block-4 + QAT Paper: https://arxiv.org/abs/2203.07259 Dataset: SQuADv1 Sparsity: 80% Number of layers: 6 ``` The dev-set performance of this model: ``` EM = 78.28 F1 = 86.10 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```
neuralmagic/oBERT-3-downstream-pruned-block4-80-squadv1	neuralmagic	2022-07-31T19:52:33Z	6	0	transformers	[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]	null	2022-05-25T14:01:27Z	--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-3-downstream-pruned-block4-80-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 80% - 4-block`. ``` Pruning method: oBERT downstream block-4 Paper: https://arxiv.org/abs/2203.07259 Dataset: SQuADv1 Sparsity: 80% Number of layers: 3 ``` The dev-set performance of this model: ``` EM = 74.07 F1 = 82.79 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```
neuralmagic/oBERT-6-downstream-dense-QAT-squadv1	neuralmagic	2022-07-31T19:52:33Z	2	0	transformers	[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]	null	2022-05-25T19:20:36Z	--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-6-downstream-dense-QAT-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 6 Layers - 0% Sparsity - QAT`, and it represents an upper bound for performance of the corresponding pruned and quantized models: - 80% unstructured QAT: `neuralmagic/oBERT-6-downstream-pruned-unstructured-80-QAT-squadv1` - 80% block-4 QAT: `neuralmagic/oBERT-6-downstream-pruned-block4-80-QAT-squadv1` - 90% unstructured QAT: `neuralmagic/oBERT-6-downstream-pruned-unstructured-90-QAT-squadv1` - 90% block-4 QAT: `neuralmagic/oBERT-6-downstream-pruned-block4-90-QAT-squadv1` SQuADv1 dev-set: ``` EM = 80.85 F1 = 87.94 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```
neuralmagic/oBERT-3-downstream-pruned-block4-90-QAT-squadv1	neuralmagic	2022-07-31T19:52:33Z	14	0	transformers	[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]	null	2022-05-25T19:21:41Z	--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-3-downstream-pruned-block4-90-QAT-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 90% - 4-block + QAT`. ``` Pruning method: oBERT downstream block-4 + QAT Paper: https://arxiv.org/abs/2203.07259 Dataset: SQuADv1 Sparsity: 90% Number of layers: 3 ``` The dev-set performance of this model: ``` EM = 70.00 F1 = 79.66 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```
neuralmagic/oBERT-3-downstream-pruned-block4-90-squadv1	neuralmagic	2022-07-31T19:52:33Z	5	0	transformers	[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]	null	2022-05-25T14:01:41Z	--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-3-downstream-pruned-block4-90-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 90% - 4-block`. ``` Pruning method: oBERT downstream block-4 Paper: https://arxiv.org/abs/2203.07259 Dataset: SQuADv1 Sparsity: 90% Number of layers: 3 ``` The dev-set performance of this model: ``` EM = 71.36 F1 = 80.69 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```
neuralmagic/oBERT-3-downstream-pruned-block4-80-QAT-squadv1	neuralmagic	2022-07-31T19:52:33Z	5	0	transformers	[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]	null	2022-05-25T19:21:28Z	--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-3-downstream-pruned-block4-80-QAT-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 80% - 4-block + QAT`. ``` Pruning method: oBERT downstream block-4 + QAT Paper: https://arxiv.org/abs/2203.07259 Dataset: SQuADv1 Sparsity: 80% Number of layers: 3 ``` The dev-set performance of this model: ``` EM = 72.70 F1 = 82.04 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```
neuralmagic/oBERT-3-downstream-pruned-unstructured-80-squadv1	neuralmagic	2022-07-31T19:52:33Z	6	0	transformers	[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]	null	2022-05-25T14:01:00Z	--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-3-downstream-pruned-unstructured-80-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 80% - unstructured`. ``` Pruning method: oBERT downstream unstructured Paper: https://arxiv.org/abs/2203.07259 Dataset: SQuADv1 Sparsity: 80% Number of layers: 3 ``` The dev-set performance of this model: ``` EM = 75.62 F1 = 84.08 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```

elopezlopez/distilbert-base-uncased_fold_5_binary_v1

elopezlopez

2022-08-02T23:02:16Z

3

0

transformers

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

text-classification

2022-08-02T22:48:50Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_5_binary_v1 results: [] ---  # distilbert-base-uncased_fold_5_binary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6980 - F1: 0.8110 ## Model description More information needed ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 288 | 0.4412 | 0.7981 | | 0.396 | 2.0 | 576 | 0.4419 | 0.8078 | | 0.396 | 3.0 | 864 | 0.4955 | 0.8166 | | 0.2019 | 4.0 | 1152 | 0.6341 | 0.8075 | | 0.2019 | 5.0 | 1440 | 1.0351 | 0.7979 | | 0.0808 | 6.0 | 1728 | 1.1818 | 0.7844 | | 0.0315 | 7.0 | 2016 | 1.2530 | 0.8051 | | 0.0315 | 8.0 | 2304 | 1.3568 | 0.7937 | | 0.0143 | 9.0 | 2592 | 1.4009 | 0.8045 | | 0.0143 | 10.0 | 2880 | 1.5333 | 0.7941 | | 0.0066 | 11.0 | 3168 | 1.5242 | 0.7982 | | 0.0066 | 12.0 | 3456 | 1.5752 | 0.8050 | | 0.0091 | 13.0 | 3744 | 1.5199 | 0.8046 | | 0.0111 | 14.0 | 4032 | 1.5319 | 0.8117 | | 0.0111 | 15.0 | 4320 | 1.5333 | 0.8156 | | 0.0072 | 16.0 | 4608 | 1.5461 | 0.8192 | | 0.0072 | 17.0 | 4896 | 1.5288 | 0.8252 | | 0.0048 | 18.0 | 5184 | 1.5725 | 0.8078 | | 0.0048 | 19.0 | 5472 | 1.5896 | 0.8138 | | 0.0032 | 20.0 | 5760 | 1.6917 | 0.8071 | | 0.0028 | 21.0 | 6048 | 1.6608 | 0.8109 | | 0.0028 | 22.0 | 6336 | 1.7013 | 0.8122 | | 0.0029 | 23.0 | 6624 | 1.6769 | 0.8148 | | 0.0029 | 24.0 | 6912 | 1.6906 | 0.8100 | | 0.0006 | 25.0 | 7200 | 1.6980 | 0.8110 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

elopezlopez/distilbert-base-uncased_fold_4_binary_v1

elopezlopez

2022-08-02T22:47:30Z

3

0

transformers

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

text-classification

2022-08-02T22:34:06Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_4_binary_v1 results: [] ---  # distilbert-base-uncased_fold_4_binary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5144 - F1: 0.8245 ## Model description More information needed ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.3756 | 0.8175 | | 0.3977 | 2.0 | 578 | 0.3672 | 0.8336 | | 0.3977 | 3.0 | 867 | 0.4997 | 0.8276 | | 0.1972 | 4.0 | 1156 | 0.6597 | 0.8244 | | 0.1972 | 5.0 | 1445 | 0.8501 | 0.8195 | | 0.0824 | 6.0 | 1734 | 1.0074 | 0.8097 | | 0.037 | 7.0 | 2023 | 1.1122 | 0.8131 | | 0.037 | 8.0 | 2312 | 1.0963 | 0.8189 | | 0.0182 | 9.0 | 2601 | 1.2511 | 0.8125 | | 0.0182 | 10.0 | 2890 | 1.2255 | 0.8141 | | 0.0121 | 11.0 | 3179 | 1.3120 | 0.8187 | | 0.0121 | 12.0 | 3468 | 1.4182 | 0.8165 | | 0.0079 | 13.0 | 3757 | 1.4142 | 0.8218 | | 0.0081 | 14.0 | 4046 | 1.4765 | 0.8150 | | 0.0081 | 15.0 | 4335 | 1.3510 | 0.8187 | | 0.0109 | 16.0 | 4624 | 1.3455 | 0.8255 | | 0.0109 | 17.0 | 4913 | 1.4157 | 0.8234 | | 0.0022 | 18.0 | 5202 | 1.4651 | 0.8197 | | 0.0022 | 19.0 | 5491 | 1.4388 | 0.8267 | | 0.0017 | 20.0 | 5780 | 1.4552 | 0.8304 | | 0.0005 | 21.0 | 6069 | 1.5357 | 0.8248 | | 0.0005 | 22.0 | 6358 | 1.4924 | 0.8241 | | 0.0009 | 23.0 | 6647 | 1.4865 | 0.8248 | | 0.0009 | 24.0 | 6936 | 1.4697 | 0.8275 | | 0.0013 | 25.0 | 7225 | 1.5144 | 0.8245 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

elopezlopez/distilbert-base-uncased_fold_3_binary_v1

elopezlopez

2022-08-02T22:32:39Z

3

0

transformers

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

text-classification

2022-08-02T22:19:07Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_3_binary_v1 results: [] ---  # distilbert-base-uncased_fold_3_binary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9405 - F1: 0.7878 ## Model description More information needed ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.4630 | 0.7897 | | 0.3954 | 2.0 | 578 | 0.4549 | 0.7936 | | 0.3954 | 3.0 | 867 | 0.6527 | 0.7868 | | 0.1991 | 4.0 | 1156 | 0.7510 | 0.7951 | | 0.1991 | 5.0 | 1445 | 0.9327 | 0.8000 | | 0.095 | 6.0 | 1734 | 1.0974 | 0.7859 | | 0.0347 | 7.0 | 2023 | 1.2692 | 0.7919 | | 0.0347 | 8.0 | 2312 | 1.3718 | 0.7921 | | 0.0105 | 9.0 | 2601 | 1.4679 | 0.7999 | | 0.0105 | 10.0 | 2890 | 1.5033 | 0.8070 | | 0.0079 | 11.0 | 3179 | 1.6074 | 0.8008 | | 0.0079 | 12.0 | 3468 | 1.6921 | 0.7904 | | 0.0053 | 13.0 | 3757 | 1.7079 | 0.7945 | | 0.0054 | 14.0 | 4046 | 1.8361 | 0.7887 | | 0.0054 | 15.0 | 4335 | 1.7695 | 0.7873 | | 0.0046 | 16.0 | 4624 | 1.7934 | 0.7917 | | 0.0046 | 17.0 | 4913 | 1.8036 | 0.8008 | | 0.0064 | 18.0 | 5202 | 1.8780 | 0.7888 | | 0.0064 | 19.0 | 5491 | 1.8943 | 0.7923 | | 0.0032 | 20.0 | 5780 | 1.8694 | 0.7905 | | 0.002 | 21.0 | 6069 | 1.9348 | 0.7869 | | 0.002 | 22.0 | 6358 | 1.9578 | 0.7804 | | 0.0036 | 23.0 | 6647 | 1.9438 | 0.7827 | | 0.0036 | 24.0 | 6936 | 1.9386 | 0.7878 | | 0.0011 | 25.0 | 7225 | 1.9405 | 0.7878 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

elopezlopez/distilbert-base-uncased_fold_2_binary_v1

elopezlopez

2022-08-02T22:17:49Z

5

0

transformers

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

text-classification

2022-08-02T22:03:59Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_2_binary_v1 results: [] ---  # distilbert-base-uncased_fold_2_binary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8833 - F1: 0.7841 ## Model description More information needed ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 290 | 0.4060 | 0.8070 | | 0.3981 | 2.0 | 580 | 0.4534 | 0.8072 | | 0.3981 | 3.0 | 870 | 0.5460 | 0.7961 | | 0.1985 | 4.0 | 1160 | 0.8684 | 0.7818 | | 0.1985 | 5.0 | 1450 | 0.9009 | 0.7873 | | 0.0844 | 6.0 | 1740 | 1.1529 | 0.7825 | | 0.0329 | 7.0 | 2030 | 1.3185 | 0.7850 | | 0.0329 | 8.0 | 2320 | 1.4110 | 0.7862 | | 0.0109 | 9.0 | 2610 | 1.4751 | 0.7784 | | 0.0109 | 10.0 | 2900 | 1.6276 | 0.7723 | | 0.0071 | 11.0 | 3190 | 1.6779 | 0.7861 | | 0.0071 | 12.0 | 3480 | 1.6258 | 0.7850 | | 0.0041 | 13.0 | 3770 | 1.6324 | 0.7903 | | 0.0109 | 14.0 | 4060 | 1.7563 | 0.7932 | | 0.0109 | 15.0 | 4350 | 1.6740 | 0.7906 | | 0.0079 | 16.0 | 4640 | 1.7468 | 0.7944 | | 0.0079 | 17.0 | 4930 | 1.7095 | 0.7879 | | 0.0067 | 18.0 | 5220 | 1.7293 | 0.7912 | | 0.0021 | 19.0 | 5510 | 1.7875 | 0.7848 | | 0.0021 | 20.0 | 5800 | 1.7462 | 0.7906 | | 0.0026 | 21.0 | 6090 | 1.8549 | 0.7815 | | 0.0026 | 22.0 | 6380 | 1.8314 | 0.7860 | | 0.0021 | 23.0 | 6670 | 1.8577 | 0.7839 | | 0.0021 | 24.0 | 6960 | 1.8548 | 0.7883 | | 0.0001 | 25.0 | 7250 | 1.8833 | 0.7841 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

sgraf202/finetuning-sentiment-model-3000-samples

sgraf202

2022-08-02T21:32:52Z

5

0

transformers

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

text-classification

2022-06-18T10:41:11Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: [] ---  # finetuning-sentiment-model-3000-samples 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: 0.7404 - Accuracy: 0.4688 - F1: 0.5526 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

aujer/autotrain-not_interested_2-1213045881

aujer

2022-08-02T21:15:40Z

4

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "en", "dataset:aujer/autotrain-data-not_interested_2", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-08-02T21:14:05Z

--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - aujer/autotrain-data-not_interested_2 co2_eq_emissions: emissions: 1.695519133475222 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1213045881 - CO2 Emissions (in grams): 1.6955 ## Validation Metrics - Loss: 1.607 - Accuracy: 0.535 - Macro F1: 0.306 - Micro F1: 0.535 - Weighted F1: 0.440 - Macro Precision: 0.346 - Micro Precision: 0.535 - Weighted Precision: 0.435 - Macro Recall: 0.345 - Micro Recall: 0.535 - Weighted Recall: 0.535 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/aujer/autotrain-not_interested_2-1213045881 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("aujer/autotrain-not_interested_2-1213045881", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("aujer/autotrain-not_interested_2-1213045881", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

srcocotero/tiny-bert-qa

srcocotero

2022-08-02T19:58:09Z

6

2

transformers

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

question-answering

2022-07-27T19:12:14Z

--- tags: - generated_from_trainer datasets: - squad model-index: - name: mini_model results: [] ---  # mini_model This model is a fine-tuned version of [nreimers/BERT-Tiny_L-2_H-128_A-2](https://huggingface.co/nreimers/BERT-Tiny_L-2_H-128_A-2) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 5 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

Rifky/indobert-hoax-classification

Rifky

2022-08-02T19:32:31Z

18

0

transformers

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

text-classification

2022-08-02T16:42:51Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: indobert-hoax-classification results: [] ---  # indobert-hoax-classification This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6230 - Accuracy: 0.8059 ## Model description More information needed ## 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: 4.2173070213315e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 30 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 85 | 0.5540 | 0.7029 | | No log | 2.0 | 170 | 0.5432 | 0.7029 | | No log | 3.0 | 255 | 0.4963 | 0.7441 | | No log | 4.0 | 340 | 0.5791 | 0.7971 | | No log | 5.0 | 425 | 0.6230 | 0.8059 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

elopezlopez/distilbert-base-uncased_fold_10_ternary_v1

elopezlopez

2022-08-02T18:22:45Z

4

0

transformers

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

text-classification

2022-08-02T18:09:19Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_10_ternary_v1 results: [] ---  # distilbert-base-uncased_fold_10_ternary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9887 - F1: 0.7797 ## Model description More information needed ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 290 | 0.5701 | 0.7463 | | 0.5651 | 2.0 | 580 | 0.5359 | 0.7748 | | 0.5651 | 3.0 | 870 | 0.6043 | 0.7847 | | 0.2605 | 4.0 | 1160 | 1.0124 | 0.7587 | | 0.2605 | 5.0 | 1450 | 1.1140 | 0.7599 | | 0.1223 | 6.0 | 1740 | 1.2713 | 0.7859 | | 0.0469 | 7.0 | 2030 | 1.3188 | 0.7822 | | 0.0469 | 8.0 | 2320 | 1.3819 | 0.7946 | | 0.0279 | 9.0 | 2610 | 1.5444 | 0.7847 | | 0.0279 | 10.0 | 2900 | 1.5851 | 0.7908 | | 0.0084 | 11.0 | 3190 | 1.7003 | 0.7822 | | 0.0084 | 12.0 | 3480 | 1.8148 | 0.7748 | | 0.007 | 13.0 | 3770 | 1.7651 | 0.7748 | | 0.008 | 14.0 | 4060 | 1.8423 | 0.7748 | | 0.008 | 15.0 | 4350 | 1.7871 | 0.7809 | | 0.0054 | 16.0 | 4640 | 1.9324 | 0.7748 | | 0.0054 | 17.0 | 4930 | 1.8685 | 0.7809 | | 0.0048 | 18.0 | 5220 | 1.9901 | 0.7797 | | 0.002 | 19.0 | 5510 | 1.9273 | 0.7785 | | 0.002 | 20.0 | 5800 | 1.9945 | 0.7809 | | 0.0018 | 21.0 | 6090 | 1.9250 | 0.7785 | | 0.0018 | 22.0 | 6380 | 1.9929 | 0.7822 | | 0.0032 | 23.0 | 6670 | 1.9306 | 0.7859 | | 0.0032 | 24.0 | 6960 | 1.9603 | 0.7847 | | 0.0029 | 25.0 | 7250 | 1.9887 | 0.7797 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

elopezlopez/distilbert-base-uncased_fold_9_ternary_v1

elopezlopez

2022-08-02T18:08:13Z

4

0

transformers

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

text-classification

2022-08-02T17:54:55Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_9_ternary_v1 results: [] ---  # distilbert-base-uncased_fold_9_ternary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9406 - F1: 0.7841 ## Model description More information needed ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 292 | 0.5684 | 0.7635 | | 0.5656 | 2.0 | 584 | 0.5753 | 0.7725 | | 0.5656 | 3.0 | 876 | 0.6159 | 0.7866 | | 0.2499 | 4.0 | 1168 | 0.7743 | 0.7828 | | 0.2499 | 5.0 | 1460 | 0.9820 | 0.7674 | | 0.1153 | 6.0 | 1752 | 1.2383 | 0.7738 | | 0.0547 | 7.0 | 2044 | 1.2468 | 0.7815 | | 0.0547 | 8.0 | 2336 | 1.3480 | 0.7622 | | 0.0233 | 9.0 | 2628 | 1.3791 | 0.7892 | | 0.0233 | 10.0 | 2920 | 1.4344 | 0.7841 | | 0.0142 | 11.0 | 3212 | 1.4958 | 0.7802 | | 0.0087 | 12.0 | 3504 | 1.5714 | 0.7674 | | 0.0087 | 13.0 | 3796 | 1.6129 | 0.7956 | | 0.0111 | 14.0 | 4088 | 1.7799 | 0.7751 | | 0.0111 | 15.0 | 4380 | 1.7272 | 0.7789 | | 0.0055 | 16.0 | 4672 | 1.7696 | 0.7866 | | 0.0055 | 17.0 | 4964 | 1.8622 | 0.7789 | | 0.003 | 18.0 | 5256 | 1.8563 | 0.7802 | | 0.0004 | 19.0 | 5548 | 1.8993 | 0.7815 | | 0.0004 | 20.0 | 5840 | 1.9199 | 0.7853 | | 0.0005 | 21.0 | 6132 | 1.9003 | 0.7879 | | 0.0005 | 22.0 | 6424 | 1.9161 | 0.7828 | | 0.0011 | 23.0 | 6716 | 1.9691 | 0.7815 | | 0.0017 | 24.0 | 7008 | 1.9492 | 0.7841 | | 0.0017 | 25.0 | 7300 | 1.9406 | 0.7841 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

ArunkumarCH/DeepLearning

ArunkumarCH

2022-08-02T17:54:25Z

0

null

[ "region:us" ]

null

2022-08-02T17:53:42Z

About this DeepLearning Model: We will build an front end application to upload the image and get the deeplearning model predicts the name of the object with acccuracy. Steps for building the Image classification model: 1. Image classification model using pretrained DL model 1.1 Define deeplearning model 2.2 Preprocess the data 3.3 Get prediction 1.1 Define deep learning model # import required modules import json import numpy as np from PIL import Image import matplotlib.pyplot as plt # import pytorch related modules import torch from torchvision import transforms from torchvision.models import densenet121 # define pretrained DL model model = densenet121(pretrained=True) model.eval(); 1.2 Preprocess data # load image using PIL input_image = Image.open(filename) # preprocess image according to the pretrained model preprocess = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) input_tensor = preprocess(input_image) # create a mini-batch as expected by the model input_batch = input_tensor.unsqueeze(0) # pass input batch to the model with torch.no_grad(): output = model(input_batch) 1.3 Get prediction pred = torch.nn.functional.softmax(output[0], dim=0).cpu().numpy() np.argmax(pred) # download classes on which the model was trained on !wget https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json # get the prediction accuracy print(classes[str(np.argmax(pred))][1], round(max(pred)*100, 2)) 2. Deploying Image Classification model 1.1 Install required libraries 1.2 Setup DL model using streamlit 1.3 Deploy DL model on AWS/Colab/HF spaces 1.1 Install required libraries !pip install -q streamlit !pip install -q pyngrok 1.2 Setup DL model using streamlit %%writefile app.py ## create streamlit app # import required libraries and modules import json import numpy as np import matplotlib.pyplot as plt import torch from PIL import Image from torchvision import transforms from torchvision.models import densenet121 import streamlit as st # define prediction function def predict(image): # load DL model model = densenet121(pretrained=True) model.eval() # load classes with open('imagenet_class_index.json', 'r') as f: classes = json.load(f) # preprocess image preprocess = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) input_tensor = preprocess(input_image) input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model # get prediction with torch.no_grad(): output = model(input_batch) pred = torch.nn.functional.softmax(output[0], dim=0).cpu().numpy() # return confidence and label confidence = round(max(pred)*100, 2) label = classes[str(np.argmax(pred))][1] return confidence, label # define image file uploader image = st.file_uploader("Upload image here") # define button for getting prediction if image is not None and st.button("Get prediction"): # load image using PIL input_image = Image.open(image) # show image st.image(input_image, use_column_width=True) # get prediction confidence, label = predict(input_image) # print results "Model is", confidence, "% confident that this image is of a", label 1.3 Deploy DL model # run streamlit app !streamlit run app.py &>/dev/null& # make streamlit app available publicly from pyngrok import ngrok public_url = ngrok.connect('8501'); public_url Model can be deployed on AWS/Colab/Flask/Hugging Spaces Hugging spaces model https://huggingface.co/spaces/ArunkumarCH/BirdClassification

elopezlopez/distilbert-base-uncased_fold_8_ternary_v1

elopezlopez

2022-08-02T17:53:47Z

3

0

transformers

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

text-classification

2022-08-02T17:40:31Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_8_ternary_v1 results: [] ---  # distilbert-base-uncased_fold_8_ternary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8474 - F1: 0.8022 ## Model description More information needed ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.5398 | 0.7838 | | 0.5509 | 2.0 | 578 | 0.6062 | 0.7703 | | 0.5509 | 3.0 | 867 | 0.6563 | 0.7666 | | 0.2366 | 4.0 | 1156 | 0.7688 | 0.7961 | | 0.2366 | 5.0 | 1445 | 1.0968 | 0.7690 | | 0.1247 | 6.0 | 1734 | 1.1414 | 0.7924 | | 0.0482 | 7.0 | 2023 | 1.2159 | 0.7875 | | 0.0482 | 8.0 | 2312 | 1.2703 | 0.7887 | | 0.0245 | 9.0 | 2601 | 1.3401 | 0.7985 | | 0.0245 | 10.0 | 2890 | 1.4645 | 0.7961 | | 0.0149 | 11.0 | 3179 | 1.5632 | 0.7801 | | 0.0149 | 12.0 | 3468 | 1.5249 | 0.7875 | | 0.0124 | 13.0 | 3757 | 1.6263 | 0.7948 | | 0.0038 | 14.0 | 4046 | 1.8059 | 0.7764 | | 0.0038 | 15.0 | 4335 | 1.7649 | 0.7776 | | 0.0061 | 16.0 | 4624 | 1.8293 | 0.7850 | | 0.0061 | 17.0 | 4913 | 1.8316 | 0.7887 | | 0.0022 | 18.0 | 5202 | 1.7628 | 0.7973 | | 0.0022 | 19.0 | 5491 | 1.8763 | 0.7862 | | 0.002 | 20.0 | 5780 | 1.8409 | 0.7899 | | 0.0026 | 21.0 | 6069 | 1.8146 | 0.8022 | | 0.0026 | 22.0 | 6358 | 1.8420 | 0.7973 | | 0.0008 | 23.0 | 6647 | 1.8683 | 0.8010 | | 0.0008 | 24.0 | 6936 | 1.8571 | 0.8010 | | 0.0015 | 25.0 | 7225 | 1.8474 | 0.8022 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

elopezlopez/distilbert-base-uncased_fold_5_ternary_v1

elopezlopez

2022-08-02T17:10:39Z

3

0

transformers

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

text-classification

2022-08-02T16:56:52Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_5_ternary_v1 results: [] ---  # distilbert-base-uncased_fold_5_ternary_v1 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: 2.1368 - F1: 0.7682 ## Model description More information needed ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 291 | 0.6423 | 0.7465 | | 0.5563 | 2.0 | 582 | 0.6001 | 0.7631 | | 0.5563 | 3.0 | 873 | 0.6884 | 0.7785 | | 0.2595 | 4.0 | 1164 | 0.9920 | 0.7439 | | 0.2595 | 5.0 | 1455 | 1.1434 | 0.7631 | | 0.1159 | 6.0 | 1746 | 1.3289 | 0.7606 | | 0.0473 | 7.0 | 2037 | 1.3966 | 0.7708 | | 0.0473 | 8.0 | 2328 | 1.4761 | 0.7606 | | 0.0282 | 9.0 | 2619 | 1.6144 | 0.7542 | | 0.0282 | 10.0 | 2910 | 1.5642 | 0.7695 | | 0.0134 | 11.0 | 3201 | 1.7206 | 0.7593 | | 0.0134 | 12.0 | 3492 | 1.8008 | 0.7542 | | 0.0059 | 13.0 | 3783 | 1.8056 | 0.7746 | | 0.002 | 14.0 | 4074 | 1.9160 | 0.7593 | | 0.002 | 15.0 | 4365 | 2.0223 | 0.7606 | | 0.0052 | 16.0 | 4656 | 1.9112 | 0.7810 | | 0.0052 | 17.0 | 4947 | 1.9040 | 0.7772 | | 0.0056 | 18.0 | 5238 | 1.9852 | 0.7734 | | 0.0061 | 19.0 | 5529 | 2.0590 | 0.7644 | | 0.0061 | 20.0 | 5820 | 2.1078 | 0.7631 | | 0.0044 | 21.0 | 6111 | 2.1177 | 0.7631 | | 0.0044 | 22.0 | 6402 | 2.0983 | 0.7644 | | 0.0012 | 23.0 | 6693 | 2.1384 | 0.7670 | | 0.0012 | 24.0 | 6984 | 2.1467 | 0.7657 | | 0.0018 | 25.0 | 7275 | 2.1368 | 0.7682 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

mrm8488/dqn-SpaceInvadersNoFrameskip-v4-2

mrm8488

2022-08-02T17:00:07Z

6

1

stable-baselines3

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

reinforcement-learning

2022-08-02T16:59:39Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 181.00 +/- 111.42 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mrm8488 -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mrm8488 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 1024), ('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', 800000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

elopezlopez/distilbert-base-uncased_fold_4_ternary_v1

elopezlopez

2022-08-02T16:55:47Z

5

0

transformers

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

text-classification

2022-08-02T16:42:24Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_4_ternary_v1 results: [] ---  # distilbert-base-uncased_fold_4_ternary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9355 - F1: 0.7891 ## Model description More information needed ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.5637 | 0.7485 | | 0.5729 | 2.0 | 578 | 0.5305 | 0.7805 | | 0.5729 | 3.0 | 867 | 0.6948 | 0.7670 | | 0.2548 | 4.0 | 1156 | 0.8351 | 0.7744 | | 0.2548 | 5.0 | 1445 | 1.0005 | 0.8027 | | 0.1157 | 6.0 | 1734 | 1.1578 | 0.7978 | | 0.0473 | 7.0 | 2023 | 1.2275 | 0.7953 | | 0.0473 | 8.0 | 2312 | 1.3245 | 0.7916 | | 0.0276 | 9.0 | 2601 | 1.3728 | 0.7953 | | 0.0276 | 10.0 | 2890 | 1.4577 | 0.7867 | | 0.0149 | 11.0 | 3179 | 1.5832 | 0.7731 | | 0.0149 | 12.0 | 3468 | 1.5056 | 0.7818 | | 0.0143 | 13.0 | 3757 | 1.6263 | 0.7904 | | 0.0066 | 14.0 | 4046 | 1.6596 | 0.7793 | | 0.0066 | 15.0 | 4335 | 1.6795 | 0.7941 | | 0.0022 | 16.0 | 4624 | 1.8443 | 0.7744 | | 0.0022 | 17.0 | 4913 | 1.7160 | 0.7953 | | 0.0034 | 18.0 | 5202 | 1.7819 | 0.7781 | | 0.0034 | 19.0 | 5491 | 1.7931 | 0.7904 | | 0.0036 | 20.0 | 5780 | 1.8447 | 0.7818 | | 0.0014 | 21.0 | 6069 | 1.9975 | 0.7707 | | 0.0014 | 22.0 | 6358 | 1.9324 | 0.7830 | | 0.0008 | 23.0 | 6647 | 1.9086 | 0.7842 | | 0.0008 | 24.0 | 6936 | 1.9507 | 0.7867 | | 0.0002 | 25.0 | 7225 | 1.9355 | 0.7891 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

jinghan/deberta-base-finetuned-wnli

jinghan

2022-08-02T15:47:58Z

6

0

transformers

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

text-classification

2022-08-02T14:56:26Z

--- license: mit tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: deberta-base-finetuned-wnli results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: wnli split: train args: wnli metrics: - name: Accuracy type: accuracy value: 0.5633802816901409 ---  # deberta-base-finetuned-wnli This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6926 - Accuracy: 0.5634 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 40 | 0.6926 | 0.5634 | | No log | 2.0 | 80 | 0.6911 | 0.5634 | | No log | 3.0 | 120 | 0.6903 | 0.5634 | | No log | 4.0 | 160 | 0.6905 | 0.5634 | | No log | 5.0 | 200 | 0.6904 | 0.5634 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

ligerre/xlm-roberta-base-finetuned-panx-fr

ligerre

2022-08-02T15:32:07Z

3

0

transformers

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

token-classification

2022-08-02T15:15:14Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.8299296953465015 ---  # xlm-roberta-base-finetuned-panx-fr 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: 0.2848 - F1: 0.8299 ## Model description More information needed ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5989 | 1.0 | 191 | 0.3383 | 0.7928 | | 0.2617 | 2.0 | 382 | 0.2966 | 0.8318 | | 0.1672 | 3.0 | 573 | 0.2848 | 0.8299 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

huggingtweets/iamsamirarora-naval-vivek_investor

huggingtweets

2022-08-02T15:16:48Z

3

0

transformers

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

text-generation

2022-08-02T15:15:22Z

--- language: en thumbnail: http://www.huggingtweets.com/iamsamirarora-naval-vivek_investor/1659453403535/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1256841238298292232/ycqwaMI2_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/853146176295759872/YiAPXQ0s_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1479277051802574853/qs6u-imt_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Naval & Samir Arora & Vivek</div> <div style="text-align: center; font-size: 14px;">@iamsamirarora-naval-vivek_investor</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Naval & Samir Arora & Vivek. | Data | Naval | Samir Arora | Vivek | | --- | --- | --- | --- | | Tweets downloaded | 3211 | 3250 | 3250 | | Retweets | 195 | 76 | 96 | | Short tweets | 612 | 973 | 601 | | Tweets kept | 2404 | 2201 | 2553 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1oa4j8zi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @iamsamirarora-naval-vivek_investor's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/21s56oiv) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/21s56oiv/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/iamsamirarora-naval-vivek_investor') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

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

ligerre

2022-08-02T15:09:38Z

3

0

transformers

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

token-classification

2022-08-02T14:48:44Z

--- license: mit 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: 0.1654 - F1: 0.8590 ## Model description More information needed ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2845 | 1.0 | 715 | 0.1831 | 0.8249 | | 0.1449 | 2.0 | 1430 | 0.1643 | 0.8479 | | 0.0929 | 3.0 | 2145 | 0.1654 | 0.8590 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

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

ligerre

2022-08-02T14:39:34Z

3

0

transformers

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

token-classification

2022-08-02T14:16:11Z

--- license: mit 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 args: PAN-X.de metrics: - name: F1 type: f1 value: 0.863677639046538 ---  # 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: 0.1343 - F1: 0.8637 ## Model description More information needed ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2578 | 1.0 | 525 | 0.1562 | 0.8273 | | 0.1297 | 2.0 | 1050 | 0.1330 | 0.8474 | | 0.0809 | 3.0 | 1575 | 0.1343 | 0.8637 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

aliromagnoli/distilbert-base-uncased-finetuned-emotion

aliromagnoli

2022-08-02T14:26:32Z

10

0

transformers

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

text-classification

2022-08-02T13:13:25Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.924 - name: F1 type: f1 value: 0.9238827602069696 ---  # 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: 0.2145 - Accuracy: 0.924 - F1: 0.9239 ## Model description More information needed ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8235 | 1.0 | 250 | 0.3050 | 0.9085 | 0.9063 | | 0.2456 | 2.0 | 500 | 0.2145 | 0.924 | 0.9239 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

anjleeg/roberta-base-finetuned-cola

anjleeg

2022-08-02T14:02:49Z

4

0

transformers

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

text-classification

2022-08-02T12:51:41Z

--- license: mit tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: roberta-base-finetuned-cola results: [] ---  # roberta-base-finetuned-cola This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4497 - Matthews Correlation: 0.6272 ## Model description More information needed ## 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: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - total_eval_batch_size: 20 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - training precision: Mixed Precision ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4453 | 1.0 | 133 | 0.4348 | 0.5391 | | 0.3121 | 2.0 | 266 | 0.3938 | 0.5827 | | 0.1149 | 3.0 | 399 | 0.4497 | 0.6272 | | 0.1194 | 4.0 | 532 | 0.5005 | 0.6076 | | 0.1639 | 5.0 | 665 | 0.5645 | 0.5943 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cpu - Datasets 2.4.0 - Tokenizers 0.12.1

s-nlp/GenChal_2022_nigula

s-nlp

2022-08-02T13:43:11Z

11

2

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "feedback comment generation for writing learning", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-07-08T15:17:59Z

--- language: - en tags: - feedback comment generation for writing learning licenses: - cc-by-nc-sa --- ## Model overview This model was trained in terms of [GenChal 2022: Feedback Comment Generation for Writing Learning](https://fcg.sharedtask.org/) shared task In this task, the model gets the string with text with the error and the exact span of the error and should return the comment in natural language, which explains the nature of the error. ## How to use ```python !pip install feedback_generation_nigula from feedback_generation_nigula.generator import FeedbackGenerator fg = FeedbackGenerator(cuda_index = 0) text_with_error = "The smoke flow my face ." error_span = (10,17) fg.get_feedback([text_with_error ], [error_span ]) # expected output ["When the <verb> <<flow>> is used as an <intransitive verb> to express'' to move in a stream'', a <preposition> needs to be placed to indicate the direction"] ``` ## Model training details #### Data The data was provided in the following way ``` input sentence [\t] offset range [\t] feedback comment ``` Here are some examples ``` The smoke flow my face . 10:17 When the <verb> <<flow>> is used as an <intransitive verb> to express ''to move in a stream'', a <preposition> needs to be placed to indicate the direction. 'To' and 'towards' are <prepositions> that indicate direction. I want to stop smoking during driving bicycle . 23:29 A <gerund> does not normally follow the <preposition> <<during>>. Think of an expression using the <conjunction> 'while' instead of a <preposition>. ``` Grammar termins are highlighted with '< ... >' marks and word examples - with '<< ... >>' #### Data preprocessing We lowercased the text, split it from any punctuation, including task specific marks (<< >>) and explicitly pointed out the error in the original text using << >>. ``` the smoke < < flow > > < < my > > face . 10:17 When the < verb > < < flow > > is used as an < intransitive verb > to express '' to move in a stream '', a < preposition > needs to be placed to indicate the direction. ' to ' and ' towards ' are < prepositions > that indicate direction . i want to stop smoking < < during > > driving bicycle . 23:29 a < gerund > does not normally follow the < preposition > < < during > > . think of an expression using the < conjunction > ' while ' instead of a < preposition > . ``` #### Data augmentation The main feature of our training pipeline was data augmentation. The idea of the augmentation is as follows: we cut the existing text with error after the last word which was syntactically connected to the words inside the error span (syntactic dependencies were automatically parsed with spacy) and this cut version of the text with error was used as a prompt for language model (we used [GPT-Neo 1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B)). Using both initial and augmented data we fine-tuned [t5-large](https://huggingface.co/t5-large). ## Licensing Information [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. [![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] [cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ [cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png

dandelin/vilt-b32-finetuned-vqa

dandelin

2022-08-02T13:03:04Z

105,438

400

transformers

[ "transformers", "pytorch", "vilt", "visual-question-answering", "arxiv:2102.03334", "license:apache-2.0", "endpoints_compatible", "region:us" ]

visual-question-answering

2022-03-02T23:29:05Z

--- tags: - visual-question-answering license: apache-2.0 widget: - text: "What's the animal doing?" src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg" - text: "What is on top of the building?" src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg" --- # Vision-and-Language Transformer (ViLT), fine-tuned on VQAv2 Vision-and-Language Transformer (ViLT) model fine-tuned on [VQAv2](https://visualqa.org/). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT). Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Intended uses & limitations You can use the raw model for visual question answering. ### How to use Here is how to use this model in PyTorch: ```python from transformers import ViltProcessor, ViltForQuestionAnswering import requests from PIL import Image # prepare image + question url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) text = "How many cats are there?" processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa") model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa") # prepare inputs encoding = processor(image, text, return_tensors="pt") # forward pass outputs = model(**encoding) logits = outputs.logits idx = logits.argmax(-1).item() print("Predicted answer:", model.config.id2label[idx]) ``` ## Training data (to do) ## Training procedure ### Preprocessing (to do) ### Pretraining (to do) ## Evaluation results (to do) ### BibTeX entry and citation info ```bibtex @misc{kim2021vilt, title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision}, author={Wonjae Kim and Bokyung Son and Ildoo Kim}, year={2021}, eprint={2102.03334}, archivePrefix={arXiv}, primaryClass={stat.ML} } ```

sepidmnorozy/finetuned-sentiment-withGPU

sepidmnorozy

2022-08-02T12:33:09Z

7

1

transformers

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

text-classification

2022-07-04T13:26:21Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: finetuning-sentiment-model_withGPU results: [] ---  # finetuning-sentiment-model-10-samples_withGPU This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3893 - Accuracy: 0.8744 - F1: 0.8684 - Precision: 0.9126 - Recall: 0.8283 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.3631 | 1.0 | 7088 | 0.3622 | 0.8638 | 0.8519 | 0.9334 | 0.7835 | | 0.35 | 2.0 | 14176 | 0.3875 | 0.8714 | 0.8622 | 0.9289 | 0.8044 | | 0.3262 | 3.0 | 21264 | 0.3893 | 0.8744 | 0.8684 | 0.9126 | 0.8283 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0 - Datasets 2.0.0 - Tokenizers 0.11.6

pannaga/wav2vec2-base-timit-demo-google-colab-testing

pannaga

2022-08-02T12:18:36Z

6

0

transformers

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

automatic-speech-recognition

2022-07-21T10:06:12Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-google-colab-testing results: [] ---  # wav2vec2-base-timit-demo-google-colab-testing This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.12.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

wenkai-li/distilbert-base-uncased-finetuned-wikiandmark_epoch50

wenkai-li

2022-08-02T12:11:19Z

6

0

transformers

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

text-classification

2022-08-02T11:02:56Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-wikiandmark_epoch50 results: [] ---  # distilbert-base-uncased-finetuned-wikiandmark_epoch50 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: - eval_loss: 0.0049 - eval_accuracy: 0.9995 - eval_runtime: 29.1585 - eval_samples_per_second: 127.613 - eval_steps_per_second: 4.013 - epoch: 6.0 - step: 4656 ## Model description More information needed ## 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: 50 ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

dfsj/distilbert-base-uncased-distilled-clinc

dfsj

2022-08-02T11:38:29Z

4

0

transformers

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

text-classification

2022-08-01T00:46:22Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9448387096774193 ---  # distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.3163 - Accuracy: 0.9448 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 9 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 318 | 2.3518 | 0.7510 | | 2.7559 | 2.0 | 636 | 1.2235 | 0.8506 | | 2.7559 | 3.0 | 954 | 0.6786 | 0.9168 | | 1.0767 | 4.0 | 1272 | 0.4668 | 0.9368 | | 0.4584 | 5.0 | 1590 | 0.3810 | 0.9410 | | 0.4584 | 6.0 | 1908 | 0.3479 | 0.9435 | | 0.2876 | 7.0 | 2226 | 0.3282 | 0.9455 | | 0.2285 | 8.0 | 2544 | 0.3201 | 0.9452 | | 0.2285 | 9.0 | 2862 | 0.3163 | 0.9448 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu102 - Datasets 2.0.0 - Tokenizers 0.12.1

spacestar1705/Reinforce-CartPole-v1

spacestar1705

2022-08-02T10:58:23Z

0

null

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

reinforcement-learning

2022-08-02T10:50:12Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - metrics: - type: mean_reward value: 92.70 +/- 7.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 --- # **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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5

Tarkan/cikolata-finetuned-hastalik

Tarkan

2022-08-02T10:47:46Z

4

0

transformers

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

token-classification

2022-08-02T10:07:32Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Tarkan/cikolata-finetuned-hastalik results: [] ---  # Tarkan/cikolata-finetuned-hastalik 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: 0.1853 - Validation Loss: 0.0921 - Train Precision: 0.6410 - Train Recall: 0.7388 - Train F1: 0.6864 - Train Accuracy: 0.9686 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 339, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.1853 | 0.0921 | 0.6410 | 0.7388 | 0.6864 | 0.9686 | 0 | ### Framework versions - Transformers 4.21.0 - TensorFlow 2.8.2 - Datasets 2.4.0 - Tokenizers 0.12.1

JmPaunlagui/Improve

JmPaunlagui

2022-08-02T10:17:55Z

0

keras

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

null

2022-08-02T09:42:09Z

--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: | name | learning_rate | decay | beta_1 | beta_2 | epsilon | amsgrad | training_precision | |----|-------------|-----|------|------|-------|-------|------------------| |Adam|0.001|0.0|0.9|0.999|1e-07|False|float32|

DrY/bert-finetuned-squad

DrY

2022-08-02T10:16:43Z

4

0

transformers

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

question-answering

2022-08-02T07:52:45Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bert-finetuned-squad results: [] ---  # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

gustavhartz/roberta-base-cuad-finetuned

gustavhartz

2022-08-02T09:11:38Z

320

1

transformers

[ "transformers", "pytorch", "roberta", "question-answering", "en", "dataset:cuad", "endpoints_compatible", "region:us" ]

question-answering

2022-06-26T13:51:35Z

--- language: en datasets: - cuad --- # Finetuned legal contract review QA model based 👩‍⚖️ 📑 Best model presented in the master thesis [*Exploring CUAD using RoBERTa span-selection QA models for legal contract review*](https://github.com/gustavhartz/transformers-legal-tasks) for QA on the Contract Understanding Atticus Dataset. Full training logic and associated thesis available through link. Outperform the most popular HF cuad model [Rakib/roberta-base-on-cuad](https://huggingface.co/Rakib/roberta-base-on-cuad) and is the best model for CUAD on Hugging Face 26/06/2022 | **Model name** | **Top 1 Has Ans F1** | **Top 3 Has Ans F1** | |-----------------------------------------|----------------------|----------------------| | gustavhartz/roberta-base-cuad-finetuned | 85.68 | 94.06 | | Rakib/roberta-base-on-cuad | 81.26 | 92.48 | For questions etc. go through the Github repo :) ### Citation If you found the code of thesis helpful you can please cite it :) ``` @thesis{ha2022, author = {Hartz, Gustav Selfort}, title = {Exploring CUAD using RoBERTa span-selection QA models for legal contract review}, language = {English}, format = {thesis}, year = {2022}, publisher = {DTU Department of Applied Mathematics and Computer Science} } ```

ysnow9876/alephbert-base-finetuned-for-shut

ysnow9876

2022-08-02T09:11:19Z

4

0

transformers

[ "transformers", "pytorch", "bert", "fill-mask", "language model", "he", "dataset:responsa", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-30T11:28:30Z

--- language: - he tags: - language model datasets: - responsa --- **AlephBERT-base-finetuned-for-shut** **Hebrew Language Model** Based on alephbert-base: https://huggingface.co/onlplab/alephbert-base#alephbert **How to use:** from transformers import AutoModelForMaskedLM, AutoTokenizer checkpoint = 'ysnow9876/alephbert-base-finetuned-for-shut' tokenizer = AutoTokenizer.from_pretrained(checkpoint) model= AutoModelForMaskedLM.from_pretrained(checkpoint) #if not finetuning - disable dropout model.eval() **Training Data** about 26,000 different responsa from different rabbis from the past few hundred years

th1s1s1t/Reinforce-cartbole-v1

th1s1s1t

2022-08-02T09:07:16Z

0

null

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

reinforcement-learning

2022-08-02T09:02:44Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartbole-v1 results: - metrics: - type: mean_reward value: 255.40 +/- 10.01 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 --- # **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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5

jinghan/roberta-base-finetuned-wnli

jinghan

2022-08-02T09:04:37Z

4

0

transformers

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

text-classification

2022-08-02T08:49:05Z

--- license: mit tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: roberta-base-finetuned-wnli results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: wnli split: train args: wnli metrics: - name: Accuracy type: accuracy value: 0.5633802816901409 ---  # roberta-base-finetuned-wnli This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6880 - Accuracy: 0.5634 ## Model description More information needed ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 40 | 0.6880 | 0.5634 | | No log | 2.0 | 80 | 0.6851 | 0.5634 | | No log | 3.0 | 120 | 0.6961 | 0.4366 | | No log | 4.0 | 160 | 0.6906 | 0.5634 | | No log | 5.0 | 200 | 0.6891 | 0.5634 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

commanderstrife/PV-Bio_clinicalBERT-superset

commanderstrife

2022-08-02T08:58:17Z

7

3

transformers

[ "transformers", "pytorch", "tensorboard", "electra", "token-classification", "generated_from_trainer", "dataset:pv_dataset", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-08-02T05:36:04Z

--- tags: - generated_from_trainer datasets: - pv_dataset metrics: - precision - recall - f1 - accuracy model-index: - name: PV-Bio_clinicalBERT-superset results: - task: name: Token Classification type: token-classification dataset: name: pv_dataset type: pv_dataset config: PVDatasetCorpus split: train args: PVDatasetCorpus metrics: - name: Precision type: precision value: 0.7055946686730801 - name: Recall type: recall value: 0.7473672226333467 - name: F1 type: f1 value: 0.7258804666334938 - name: Accuracy type: accuracy value: 0.9656573815513143 ---  # PV-Bio_clinicalBERT-superset This model is a fine-tuned version of [giacomomiolo/electramed_base_scivocab_1M](https://huggingface.co/giacomomiolo/electramed_base_scivocab_1M) on the pv_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.2082 - Precision: 0.7056 - Recall: 0.7474 - F1: 0.7259 - Accuracy: 0.9657 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.063 | 1.0 | 1813 | 0.1061 | 0.6453 | 0.7306 | 0.6853 | 0.9623 | | 0.0086 | 2.0 | 3626 | 0.1068 | 0.6620 | 0.7516 | 0.7040 | 0.9647 | | 0.0089 | 3.0 | 5439 | 0.1265 | 0.7026 | 0.7300 | 0.7160 | 0.9657 | | 0.004 | 4.0 | 7252 | 0.1369 | 0.6820 | 0.7601 | 0.7189 | 0.9638 | | 0.0004 | 5.0 | 9065 | 0.1573 | 0.6937 | 0.7602 | 0.7254 | 0.9656 | | 0.0184 | 6.0 | 10878 | 0.1707 | 0.7078 | 0.7475 | 0.7271 | 0.9662 | | 0.0009 | 7.0 | 12691 | 0.1787 | 0.7116 | 0.7398 | 0.7254 | 0.9662 | | 0.0006 | 8.0 | 14504 | 0.1874 | 0.6979 | 0.7576 | 0.7265 | 0.9655 | | 0.0008 | 9.0 | 16317 | 0.1970 | 0.7083 | 0.7475 | 0.7273 | 0.9660 | | 0.0003 | 10.0 | 18130 | 0.2082 | 0.7056 | 0.7474 | 0.7259 | 0.9657 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

silviacamplani/distilbert-base-uncased-finetuned-ner-conll2003_100train

silviacamplani

2022-08-02T08:55:52Z

4

0

transformers

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

token-classification

2022-08-02T08:54:21Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: silviacamplani/distilbert-base-uncased-finetuned-ner-conll2003_100train results: [] ---  # silviacamplani/distilbert-base-uncased-finetuned-ner-conll2003_100train 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: 1.4072 - Validation Loss: 1.4582 - Train Precision: 0.0 - Train Recall: 0.0 - Train F1: 0.0 - Train Accuracy: 0.7920 - 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 2.0837 | 1.8526 | 0.0013 | 0.0015 | 0.0014 | 0.7006 | 0 | | 1.6450 | 1.5672 | 0.0 | 0.0 | 0.0 | 0.7916 | 1 | | 1.4072 | 1.4582 | 0.0 | 0.0 | 0.0 | 0.7920 | 2 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1

ajders/nl_electra

ajders

2022-08-02T08:43:24Z

6

0

transformers

[ "transformers", "pytorch", "electra", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-07-07T13:48:39Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: nl_electra results: [] ---  # nl_electra This model is a pretrained version of [ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra) on the Dutch subset of the [CC100](https://huggingface.co/datasets/cc100) dataset. It achieves the following results on the evaluation set: - Loss: 2.4650 - Accuracy: 0.5392 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 703 - gradient_accumulation_steps: 32 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 8000 - num_epochs: 400.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:------:|:---------------:|:--------:| | No log | 0.67 | 500 | 9.9977 | 0.0486 | | No log | 1.35 | 1000 | 9.5620 | 0.0543 | | No log | 2.02 | 1500 | 8.9306 | 0.0741 | | No log | 2.69 | 2000 | 8.2617 | 0.0826 | | No log | 3.36 | 2500 | 7.6880 | 0.0792 | | No log | 4.04 | 3000 | 7.3316 | 0.0757 | | No log | 4.71 | 3500 | 7.1944 | 0.0747 | | No log | 5.38 | 4000 | 7.1349 | 0.0802 | | No log | 6.06 | 4500 | 7.0752 | 0.0887 | | 8.201 | 6.73 | 5000 | 7.0046 | 0.1021 | | 8.201 | 7.4 | 5500 | 6.9295 | 0.1090 | | 8.201 | 8.08 | 6000 | 6.8483 | 0.1132 | | 8.201 | 8.75 | 6500 | 6.7750 | 0.1171 | | 8.201 | 9.42 | 7000 | 6.7116 | 0.1187 | | 8.201 | 10.09 | 7500 | 6.6560 | 0.1218 | | 8.201 | 10.77 | 8000 | 6.6178 | 0.1239 | | 8.201 | 11.44 | 8500 | 6.5824 | 0.1255 | | 8.201 | 12.11 | 9000 | 6.5521 | 0.1273 | | 8.201 | 12.79 | 9500 | 6.5203 | 0.1292 | | 6.7257 | 13.46 | 10000 | 6.5027 | 0.1303 | | 6.7257 | 14.13 | 10500 | 6.4809 | 0.1314 | | 6.7257 | 14.8 | 11000 | 6.4631 | 0.1322 | | 6.7257 | 15.48 | 11500 | 6.4483 | 0.1329 | | 6.7257 | 16.15 | 12000 | 6.4320 | 0.1338 | | 6.7257 | 16.82 | 12500 | 6.4169 | 0.1348 | | 6.7257 | 17.5 | 13000 | 6.4067 | 0.1359 | | 6.7257 | 18.17 | 13500 | 6.3994 | 0.1359 | | 6.7257 | 18.84 | 14000 | 6.3823 | 0.1368 | | 6.7257 | 19.52 | 14500 | 6.3759 | 0.1373 | | 6.4502 | 20.19 | 15000 | 6.3629 | 0.1374 | | 6.4502 | 20.86 | 15500 | 6.3638 | 0.1373 | | 6.4502 | 21.53 | 16000 | 6.3505 | 0.1382 | | 6.4502 | 22.21 | 16500 | 6.3416 | 0.1387 | | 6.4502 | 22.88 | 17000 | 6.3420 | 0.1383 | | 6.4502 | 23.55 | 17500 | 6.3330 | 0.1389 | | 6.4502 | 24.23 | 18000 | 6.3289 | 0.1388 | | 6.4502 | 24.9 | 18500 | 6.3184 | 0.1389 | | 6.4502 | 25.57 | 19000 | 6.3099 | 0.1396 | | 6.4502 | 26.24 | 19500 | 6.2789 | 0.1405 | | 6.3474 | 26.92 | 20000 | 6.2398 | 0.1404 | | 6.3474 | 27.59 | 20500 | 6.2012 | 0.1412 | | 6.3474 | 28.26 | 21000 | 6.1803 | 0.1414 | | 6.3474 | 28.94 | 21500 | 6.1579 | 0.1414 | | 6.3474 | 29.61 | 22000 | 6.1403 | 0.1431 | | 6.3474 | 30.28 | 22500 | 6.1316 | 0.1423 | | 6.3474 | 30.96 | 23000 | 6.1102 | 0.1435 | | 6.3474 | 31.63 | 23500 | 6.0998 | 0.1439 | | 6.3474 | 32.3 | 24000 | 6.0867 | 0.1446 | | 6.3474 | 32.97 | 24500 | 6.0700 | 0.1451 | | 6.1758 | 33.65 | 25000 | 6.0554 | 0.1457 | | 6.1758 | 34.32 | 25500 | 6.0487 | 0.1457 | | 6.1758 | 34.99 | 26000 | 6.0328 | 0.1469 | | 6.1758 | 35.67 | 26500 | 6.0265 | 0.1469 | | 6.1758 | 36.34 | 27000 | 5.9992 | 0.1486 | | 6.1758 | 37.01 | 27500 | 5.9934 | 0.1485 | | 6.1758 | 37.68 | 28000 | 5.9702 | 0.1501 | | 6.1758 | 38.36 | 28500 | 5.9661 | 0.1503 | | 6.1758 | 39.03 | 29000 | 5.9558 | 0.1512 | | 6.1758 | 39.7 | 29500 | 5.9321 | 0.1528 | | 6.052 | 40.38 | 30000 | 5.9147 | 0.1532 | | 6.052 | 41.05 | 30500 | 5.8975 | 0.1545 | | 6.052 | 41.72 | 31000 | 5.8784 | 0.1566 | | 6.052 | 42.4 | 31500 | 5.8584 | 0.1586 | | 6.052 | 43.07 | 32000 | 5.8325 | 0.1603 | | 6.052 | 43.74 | 32500 | 5.7583 | 0.1664 | | 6.052 | 44.41 | 33000 | 5.6158 | 0.1787 | | 6.052 | 45.09 | 33500 | 5.4580 | 0.1917 | | 6.052 | 45.76 | 34000 | 5.3396 | 0.2010 | | 6.052 | 46.43 | 34500 | 5.2568 | 0.2082 | | 5.7995 | 47.11 | 35000 | 5.1775 | 0.2146 | | 5.7995 | 47.78 | 35500 | 5.1076 | 0.2204 | | 5.7995 | 48.45 | 36000 | 5.0457 | 0.2258 | | 5.7995 | 49.13 | 36500 | 4.9932 | 0.2313 | | 5.7995 | 49.8 | 37000 | 4.9164 | 0.2384 | | 5.7995 | 50.47 | 37500 | 4.7844 | 0.2521 | | 5.7995 | 51.14 | 38000 | 4.6598 | 0.2642 | | 5.7995 | 51.82 | 38500 | 4.5472 | 0.2757 | | 5.7995 | 52.49 | 39000 | 4.4374 | 0.2871 | | 5.7995 | 53.16 | 39500 | 4.3399 | 0.2982 | | 5.0341 | 53.84 | 40000 | 4.2549 | 0.3083 | | 5.0341 | 54.51 | 40500 | 4.1795 | 0.3170 | | 5.0341 | 55.18 | 41000 | 4.1017 | 0.3274 | | 5.0341 | 55.85 | 41500 | 4.0308 | 0.3375 | | 5.0341 | 56.53 | 42000 | 3.9673 | 0.3462 | | 5.0341 | 57.2 | 42500 | 3.9130 | 0.3538 | | 5.0341 | 57.87 | 43000 | 3.8672 | 0.3599 | | 5.0341 | 58.55 | 43500 | 3.8249 | 0.3656 | | 5.0341 | 59.22 | 44000 | 3.7748 | 0.3728 | | 5.0341 | 59.89 | 44500 | 3.7459 | 0.3768 | | 4.2119 | 60.57 | 45000 | 3.7089 | 0.3808 | | 4.2119 | 61.24 | 45500 | 3.6732 | 0.3857 | | 4.2119 | 61.91 | 46000 | 3.6546 | 0.3881 | | 4.2119 | 62.58 | 46500 | 3.6205 | 0.3921 | | 4.2119 | 63.26 | 47000 | 3.5908 | 0.3960 | | 4.2119 | 63.93 | 47500 | 3.5627 | 0.3991 | | 4.2119 | 64.6 | 48000 | 3.5466 | 0.4019 | | 4.2119 | 65.28 | 48500 | 3.5262 | 0.4039 | | 4.2119 | 65.95 | 49000 | 3.4987 | 0.4074 | | 4.2119 | 66.62 | 49500 | 3.4817 | 0.4093 | | 3.8182 | 67.29 | 50000 | 3.4608 | 0.4119 | | 3.8182 | 67.97 | 50500 | 3.4467 | 0.4142 | | 3.8182 | 68.64 | 51000 | 3.4280 | 0.4163 | | 3.8182 | 69.31 | 51500 | 3.4165 | 0.4175 | | 3.8182 | 69.99 | 52000 | 3.3970 | 0.4199 | | 3.8182 | 70.66 | 52500 | 3.3738 | 0.4227 | | 3.8182 | 71.33 | 53000 | 3.3640 | 0.4242 | | 3.8182 | 72.01 | 53500 | 3.3583 | 0.4252 | | 3.8182 | 72.68 | 54000 | 3.3319 | 0.4279 | | 3.8182 | 73.35 | 54500 | 3.3153 | 0.4303 | | 3.5946 | 74.02 | 55000 | 3.3098 | 0.4304 | | 3.5946 | 74.7 | 55500 | 3.2949 | 0.4328 | | 3.5946 | 75.37 | 56000 | 3.2820 | 0.4335 | | 3.5946 | 76.04 | 56500 | 3.2686 | 0.4355 | | 3.5946 | 76.72 | 57000 | 3.2663 | 0.4359 | | 3.5946 | 77.39 | 57500 | 3.2482 | 0.4379 | | 3.5946 | 78.06 | 58000 | 3.2344 | 0.4393 | | 3.5946 | 78.73 | 58500 | 3.2281 | 0.4407 | | 3.5946 | 79.41 | 59000 | 3.2172 | 0.4412 | | 3.5946 | 80.08 | 59500 | 3.2110 | 0.4420 | | 3.4435 | 80.75 | 60000 | 3.1940 | 0.4443 | | 3.4435 | 81.43 | 60500 | 3.1837 | 0.4455 | | 3.4435 | 82.1 | 61000 | 3.1744 | 0.4469 | | 3.4435 | 82.77 | 61500 | 3.1611 | 0.4483 | | 3.4435 | 83.45 | 62000 | 3.1531 | 0.4496 | | 3.4435 | 84.12 | 62500 | 3.1524 | 0.4499 | | 3.4435 | 84.79 | 63000 | 3.1431 | 0.4501 | | 3.4435 | 85.46 | 63500 | 3.1287 | 0.4527 | | 3.4435 | 86.14 | 64000 | 3.1192 | 0.4533 | | 3.4435 | 86.81 | 64500 | 3.1107 | 0.4547 | | 3.3301 | 87.48 | 65000 | 3.1041 | 0.4553 | | 3.3301 | 88.16 | 65500 | 3.0999 | 0.4562 | | 3.3301 | 88.83 | 66000 | 3.0882 | 0.4576 | | 3.3301 | 89.5 | 66500 | 3.0777 | 0.4589 | | 3.3301 | 90.17 | 67000 | 3.0726 | 0.4588 | | 3.3301 | 90.85 | 67500 | 3.0676 | 0.4601 | | 3.3301 | 91.52 | 68000 | 3.0616 | 0.4602 | | 3.3301 | 92.19 | 68500 | 3.0523 | 0.4621 | | 3.3301 | 92.87 | 69000 | 3.0464 | 0.4624 | | 3.3301 | 93.54 | 69500 | 3.0405 | 0.4635 | | 3.2418 | 94.21 | 70000 | 3.0312 | 0.4649 | | 3.2418 | 94.89 | 70500 | 3.0209 | 0.4653 | | 3.2418 | 95.56 | 71000 | 3.0202 | 0.4657 | | 3.2418 | 96.23 | 71500 | 3.0101 | 0.4676 | | 3.2418 | 96.9 | 72000 | 3.0105 | 0.4666 | | 3.2418 | 97.58 | 72500 | 3.0023 | 0.4685 | | 3.2418 | 98.25 | 73000 | 3.0008 | 0.4680 | | 3.2418 | 98.92 | 73500 | 2.9882 | 0.4691 | | 3.2418 | 99.6 | 74000 | 2.9855 | 0.4702 | | 3.2418 | 100.27 | 74500 | 2.9790 | 0.4709 | | 3.1698 | 100.94 | 75000 | 2.9680 | 0.4716 | | 3.1698 | 101.61 | 75500 | 2.9667 | 0.4724 | | 3.1698 | 102.29 | 76000 | 2.9657 | 0.4726 | | 3.1698 | 102.96 | 76500 | 2.9623 | 0.4731 | | 3.1698 | 103.63 | 77000 | 2.9515 | 0.4745 | | 3.1698 | 104.31 | 77500 | 2.9471 | 0.4753 | | 3.1698 | 104.98 | 78000 | 2.9407 | 0.4756 | | 3.1698 | 105.65 | 78500 | 2.9388 | 0.4761 | | 3.1698 | 106.33 | 79000 | 2.9369 | 0.4766 | | 3.1698 | 107.0 | 79500 | 2.9297 | 0.4762 | | 3.1101 | 107.67 | 80000 | 2.9291 | 0.4776 | | 3.1101 | 108.34 | 80500 | 2.9139 | 0.4788 | | 3.1101 | 109.02 | 81000 | 2.9113 | 0.4790 | | 3.1101 | 109.69 | 81500 | 2.9057 | 0.4798 | | 3.1101 | 110.36 | 82000 | 2.9058 | 0.4804 | | 3.1101 | 111.04 | 82500 | 2.9019 | 0.4807 | | 3.1101 | 111.71 | 83000 | 2.8934 | 0.4818 | | 3.1101 | 112.38 | 83500 | 2.8864 | 0.4825 | | 3.1101 | 113.06 | 84000 | 2.8926 | 0.4815 | | 3.1101 | 113.73 | 84500 | 2.8812 | 0.4830 | | 3.058 | 114.4 | 85000 | 2.8740 | 0.4840 | | 3.058 | 115.07 | 85500 | 2.8730 | 0.4840 | | 3.058 | 115.75 | 86000 | 2.8694 | 0.4847 | | 3.058 | 116.42 | 86500 | 2.8694 | 0.4848 | | 3.058 | 117.09 | 87000 | 2.8601 | 0.4862 | | 3.058 | 117.77 | 87500 | 2.8547 | 0.4862 | | 3.058 | 118.44 | 88000 | 2.8538 | 0.4861 | | 3.058 | 119.11 | 88500 | 2.8494 | 0.4876 | | 3.058 | 119.78 | 89000 | 2.8430 | 0.4882 | | 3.058 | 120.46 | 89500 | 2.8436 | 0.4875 | | 3.0129 | 121.13 | 90000 | 2.8402 | 0.4884 | | 3.0129 | 121.8 | 90500 | 2.8353 | 0.4888 | | 3.0129 | 122.48 | 91000 | 2.8271 | 0.4896 | | 3.0129 | 123.15 | 91500 | 2.8236 | 0.4900 | | 3.0129 | 123.82 | 92000 | 2.8199 | 0.4913 | | 3.0129 | 124.5 | 92500 | 2.8119 | 0.4916 | | 3.0129 | 125.17 | 93000 | 2.8138 | 0.4916 | | 3.0129 | 125.84 | 93500 | 2.8089 | 0.4925 | | 3.0129 | 126.51 | 94000 | 2.8067 | 0.4928 | | 3.0129 | 127.19 | 94500 | 2.8010 | 0.4939 | | 2.9701 | 127.86 | 95000 | 2.7992 | 0.4938 | | 2.9701 | 128.53 | 95500 | 2.7953 | 0.4948 | | 2.9701 | 129.21 | 96000 | 2.7964 | 0.4942 | | 2.9701 | 129.88 | 96500 | 2.7838 | 0.4955 | | 2.9701 | 130.55 | 97000 | 2.7770 | 0.4968 | | 2.9701 | 131.22 | 97500 | 2.7800 | 0.4962 | | 2.9701 | 131.9 | 98000 | 2.7743 | 0.4972 | | 2.9701 | 132.57 | 98500 | 2.7696 | 0.4973 | | 2.9701 | 133.24 | 99000 | 2.7691 | 0.4980 | | 2.9701 | 133.92 | 99500 | 2.7612 | 0.4989 | | 2.9289 | 134.59 | 100000 | 2.7606 | 0.4987 | | 2.9289 | 135.26 | 100500 | 2.7545 | 0.4993 | | 2.9289 | 135.94 | 101000 | 2.7544 | 0.4999 | | 2.9289 | 136.61 | 101500 | 2.7550 | 0.4999 | | 2.9289 | 137.28 | 102000 | 2.7510 | 0.5001 | | 2.9289 | 137.95 | 102500 | 2.7480 | 0.5002 | | 2.9289 | 138.63 | 103000 | 2.7422 | 0.5012 | | 2.9289 | 139.3 | 103500 | 2.7419 | 0.5014 | | 2.9289 | 139.97 | 104000 | 2.7416 | 0.5009 | | 2.9289 | 140.65 | 104500 | 2.7412 | 0.5017 | | 2.8968 | 141.32 | 105000 | 2.7356 | 0.5023 | | 2.8968 | 141.99 | 105500 | 2.7303 | 0.5027 | | 2.8968 | 142.66 | 106000 | 2.7359 | 0.5029 | | 2.8968 | 143.34 | 106500 | 2.7283 | 0.5032 | | 2.8968 | 144.01 | 107000 | 2.7226 | 0.5033 | | 2.8968 | 144.68 | 107500 | 2.7247 | 0.5039 | | 2.8968 | 145.36 | 108000 | 2.7209 | 0.5044 | | 2.8968 | 146.03 | 108500 | 2.7210 | 0.5039 | | 2.8968 | 146.7 | 109000 | 2.7135 | 0.5046 | | 2.8968 | 147.38 | 109500 | 2.7139 | 0.5048 | | 2.8697 | 148.05 | 110000 | 2.7167 | 0.5050 | | 2.8697 | 148.72 | 110500 | 2.7125 | 0.5058 | | 2.8697 | 149.39 | 111000 | 2.7064 | 0.5060 | | 2.8697 | 150.07 | 111500 | 2.7024 | 0.5067 | | 2.8697 | 150.74 | 112000 | 2.7035 | 0.5067 | | 2.8697 | 151.41 | 112500 | 2.7034 | 0.5067 | | 2.8697 | 152.09 | 113000 | 2.6967 | 0.5073 | | 2.8697 | 152.76 | 113500 | 2.6982 | 0.5070 | | 2.8697 | 153.43 | 114000 | 2.6948 | 0.5079 | | 2.8697 | 154.1 | 114500 | 2.6946 | 0.5076 | | 2.8457 | 154.78 | 115000 | 2.6918 | 0.5078 | | 2.8457 | 155.45 | 115500 | 2.6917 | 0.5078 | | 2.8457 | 156.12 | 116000 | 2.6868 | 0.5086 | | 2.8457 | 156.8 | 116500 | 2.6870 | 0.5084 | | 2.8457 | 157.47 | 117000 | 2.6830 | 0.5091 | | 2.8457 | 158.14 | 117500 | 2.6824 | 0.5090 | | 2.8457 | 158.82 | 118000 | 2.6812 | 0.5092 | | 2.8457 | 159.49 | 118500 | 2.6747 | 0.5098 | | 2.8457 | 160.16 | 119000 | 2.6747 | 0.5105 | | 2.8457 | 160.83 | 119500 | 2.6750 | 0.5102 | | 2.825 | 161.51 | 120000 | 2.6761 | 0.5102 | | 2.825 | 162.18 | 120500 | 2.6670 | 0.5115 | | 2.825 | 162.85 | 121000 | 2.6740 | 0.5104 | | 2.825 | 163.53 | 121500 | 2.6700 | 0.5106 | | 2.825 | 164.2 | 122000 | 2.6629 | 0.5119 | | 2.825 | 164.87 | 122500 | 2.6642 | 0.5117 | | 2.825 | 165.54 | 123000 | 2.6664 | 0.5109 | | 2.825 | 166.22 | 123500 | 2.6626 | 0.5117 | | 2.825 | 166.89 | 124000 | 2.6561 | 0.5130 | | 2.825 | 167.56 | 124500 | 2.6612 | 0.5125 | | 2.8059 | 168.24 | 125000 | 2.6594 | 0.5123 | | 2.8059 | 168.91 | 125500 | 2.6508 | 0.5132 | | 2.8059 | 169.58 | 126000 | 2.6477 | 0.5134 | | 2.8059 | 170.26 | 126500 | 2.6527 | 0.5133 | | 2.8059 | 170.93 | 127000 | 2.6519 | 0.5136 | | 2.8059 | 171.6 | 127500 | 2.6456 | 0.5141 | | 2.8059 | 172.27 | 128000 | 2.6473 | 0.5139 | | 2.8059 | 172.95 | 128500 | 2.6426 | 0.5144 | | 2.8059 | 173.62 | 129000 | 2.6454 | 0.5137 | | 2.8059 | 174.29 | 129500 | 2.6427 | 0.5147 | | 2.788 | 174.97 | 130000 | 2.6376 | 0.5150 | | 2.788 | 175.64 | 130500 | 2.6366 | 0.5154 | | 2.788 | 176.31 | 131000 | 2.6327 | 0.5156 | | 2.788 | 176.98 | 131500 | 2.6328 | 0.5157 | | 2.788 | 177.66 | 132000 | 2.6335 | 0.5156 | | 2.788 | 178.33 | 132500 | 2.6302 | 0.5166 | | 2.788 | 179.0 | 133000 | 2.6333 | 0.5160 | | 2.788 | 179.68 | 133500 | 2.6253 | 0.5171 | | 2.788 | 180.35 | 134000 | 2.6237 | 0.5167 | | 2.788 | 181.02 | 134500 | 2.6269 | 0.5165 | | 2.7723 | 181.7 | 135000 | 2.6283 | 0.5164 | | 2.7723 | 182.37 | 135500 | 2.6255 | 0.5174 | | 2.7723 | 183.04 | 136000 | 2.6254 | 0.5175 | | 2.7723 | 183.71 | 136500 | 2.6231 | 0.5172 | | 2.7723 | 184.39 | 137000 | 2.6181 | 0.5173 | | 2.7723 | 185.06 | 137500 | 2.6260 | 0.5168 | | 2.7723 | 185.73 | 138000 | 2.6183 | 0.5176 | | 2.7723 | 186.41 | 138500 | 2.6174 | 0.5182 | | 2.7723 | 187.08 | 139000 | 2.6144 | 0.5182 | | 2.7723 | 187.75 | 139500 | 2.6152 | 0.5186 | | 2.7575 | 188.43 | 140000 | 2.6150 | 0.5183 | | 2.7575 | 189.1 | 140500 | 2.6110 | 0.5190 | | 2.7575 | 189.77 | 141000 | 2.6044 | 0.5194 | | 2.7575 | 190.44 | 141500 | 2.6083 | 0.5186 | | 2.7575 | 191.12 | 142000 | 2.6102 | 0.5189 | | 2.7575 | 191.79 | 142500 | 2.6063 | 0.5195 | | 2.7575 | 192.46 | 143000 | 2.6071 | 0.5198 | | 2.7575 | 193.14 | 143500 | 2.6090 | 0.5191 | | 2.7575 | 193.81 | 144000 | 2.6068 | 0.5200 | | 2.7575 | 194.48 | 144500 | 2.6032 | 0.5200 | | 2.7445 | 195.15 | 145000 | 2.6031 | 0.5200 | | 2.7445 | 195.83 | 145500 | 2.6007 | 0.5201 | | 2.7445 | 196.5 | 146000 | 2.5998 | 0.5203 | | 2.7445 | 197.17 | 146500 | 2.5980 | 0.5208 | | 2.7445 | 197.85 | 147000 | 2.5952 | 0.5211 | | 2.7445 | 198.52 | 147500 | 2.5977 | 0.5210 | | 2.7445 | 199.19 | 148000 | 2.5922 | 0.5212 | | 2.7445 | 199.87 | 148500 | 2.5936 | 0.5211 | | 2.7445 | 200.54 | 149000 | 2.5933 | 0.5219 | | 2.7445 | 201.21 | 149500 | 2.5887 | 0.5219 | | 2.7324 | 201.88 | 150000 | 2.5911 | 0.5215 | | 2.7324 | 202.56 | 150500 | 2.5892 | 0.5219 | | 2.7324 | 203.23 | 151000 | 2.5875 | 0.5218 | | 2.7324 | 203.9 | 151500 | 2.5898 | 0.5220 | | 2.7324 | 204.58 | 152000 | 2.5872 | 0.5223 | | 2.7324 | 205.25 | 152500 | 2.5805 | 0.5226 | | 2.7324 | 205.92 | 153000 | 2.5861 | 0.5225 | | 2.7324 | 206.59 | 153500 | 2.5839 | 0.5223 | | 2.7324 | 207.27 | 154000 | 2.5804 | 0.5232 | | 2.7324 | 207.94 | 154500 | 2.5766 | 0.5235 | | 2.7212 | 208.61 | 155000 | 2.5764 | 0.5233 | | 2.7212 | 209.29 | 155500 | 2.5801 | 0.5233 | | 2.7212 | 209.96 | 156000 | 2.5737 | 0.5241 | | 2.7212 | 210.63 | 156500 | 2.5769 | 0.5236 | | 2.7212 | 211.31 | 157000 | 2.5769 | 0.5237 | | 2.7212 | 211.98 | 157500 | 2.5748 | 0.5239 | | 2.7212 | 212.65 | 158000 | 2.5745 | 0.5230 | | 2.7212 | 213.32 | 158500 | 2.5725 | 0.5240 | | 2.7212 | 214.0 | 159000 | 2.5736 | 0.5239 | | 2.7212 | 214.67 | 159500 | 2.5675 | 0.5252 | | 2.7103 | 215.34 | 160000 | 2.5678 | 0.5245 | | 2.7103 | 216.02 | 160500 | 2.5691 | 0.5250 | | 2.7103 | 216.69 | 161000 | 2.5688 | 0.5245 | | 2.7103 | 217.36 | 161500 | 2.5681 | 0.5251 | | 2.7103 | 218.03 | 162000 | 2.5582 | 0.5255 | | 2.7103 | 218.71 | 162500 | 2.5675 | 0.5247 | | 2.7103 | 219.38 | 163000 | 2.5609 | 0.5259 | | 2.7103 | 220.05 | 163500 | 2.5625 | 0.5254 | | 2.7103 | 220.73 | 164000 | 2.5630 | 0.5254 | | 2.7103 | 221.4 | 164500 | 2.5607 | 0.5265 | | 2.7003 | 222.07 | 165000 | 2.5615 | 0.5260 | | 2.7003 | 222.75 | 165500 | 2.5660 | 0.5248 | | 2.7003 | 223.42 | 166000 | 2.5569 | 0.5263 | | 2.7003 | 224.09 | 166500 | 2.5610 | 0.5255 | | 2.7003 | 224.76 | 167000 | 2.5569 | 0.5263 | | 2.7003 | 225.44 | 167500 | 2.5534 | 0.5265 | | 2.7003 | 226.11 | 168000 | 2.5573 | 0.5259 | | 2.7003 | 226.78 | 168500 | 2.5559 | 0.5264 | | 2.7003 | 227.46 | 169000 | 2.5508 | 0.5277 | | 2.7003 | 228.13 | 169500 | 2.5500 | 0.5276 | | 2.6915 | 228.8 | 170000 | 2.5501 | 0.5270 | | 2.6915 | 229.47 | 170500 | 2.5508 | 0.5273 | | 2.6915 | 230.15 | 171000 | 2.5523 | 0.5267 | | 2.6915 | 230.82 | 171500 | 2.5464 | 0.5276 | | 2.6915 | 231.49 | 172000 | 2.5482 | 0.5271 | | 2.6915 | 232.17 | 172500 | 2.5486 | 0.5270 | | 2.6915 | 232.84 | 173000 | 2.5474 | 0.5275 | | 2.6915 | 233.51 | 173500 | 2.5483 | 0.5270 | | 2.6915 | 234.19 | 174000 | 2.5480 | 0.5276 | | 2.6915 | 234.86 | 174500 | 2.5486 | 0.5278 | | 2.6833 | 235.53 | 175000 | 2.5484 | 0.5273 | | 2.6833 | 236.2 | 175500 | 2.5436 | 0.5277 | | 2.6833 | 236.88 | 176000 | 2.5448 | 0.5278 | | 2.6833 | 237.55 | 176500 | 2.5430 | 0.5284 | | 2.6833 | 238.22 | 177000 | 2.5433 | 0.5279 | | 2.6833 | 238.9 | 177500 | 2.5398 | 0.5288 | | 2.6833 | 239.57 | 178000 | 2.5424 | 0.5282 | | 2.6833 | 240.24 | 178500 | 2.5371 | 0.5291 | | 2.6833 | 240.91 | 179000 | 2.5372 | 0.5294 | | 2.6833 | 241.59 | 179500 | 2.5368 | 0.5290 | | 2.6753 | 242.26 | 180000 | 2.5383 | 0.5289 | | 2.6753 | 242.93 | 180500 | 2.5387 | 0.5289 | | 2.6753 | 243.61 | 181000 | 2.5351 | 0.5295 | | 2.6753 | 244.28 | 181500 | 2.5340 | 0.5296 | | 2.6753 | 244.95 | 182000 | 2.5349 | 0.5289 | | 2.6753 | 245.63 | 182500 | 2.5358 | 0.5295 | | 2.6753 | 246.3 | 183000 | 2.5333 | 0.5299 | | 2.6753 | 246.97 | 183500 | 2.5363 | 0.5292 | | 2.6753 | 247.64 | 184000 | 2.5323 | 0.5298 | | 2.6753 | 248.32 | 184500 | 2.5286 | 0.5299 | | 2.6679 | 248.99 | 185000 | 2.5276 | 0.5306 | | 2.6679 | 249.66 | 185500 | 2.5249 | 0.5308 | | 2.6679 | 250.34 | 186000 | 2.5308 | 0.5302 | | 2.6679 | 251.01 | 186500 | 2.5307 | 0.5297 | | 2.6679 | 251.68 | 187000 | 2.5293 | 0.5305 | | 2.6679 | 252.36 | 187500 | 2.5255 | 0.5306 | | 2.6679 | 253.03 | 188000 | 2.5244 | 0.5312 | | 2.6679 | 253.7 | 188500 | 2.5278 | 0.5305 | | 2.6679 | 254.37 | 189000 | 2.5212 | 0.5317 | | 2.6679 | 255.05 | 189500 | 2.5256 | 0.5307 | | 2.6611 | 255.72 | 190000 | 2.5273 | 0.5303 | | 2.6611 | 256.39 | 190500 | 2.5222 | 0.5310 | | 2.6611 | 257.07 | 191000 | 2.5237 | 0.5311 | | 2.6611 | 257.74 | 191500 | 2.5258 | 0.5309 | | 2.6611 | 258.41 | 192000 | 2.5219 | 0.5313 | | 2.6611 | 259.08 | 192500 | 2.5243 | 0.5314 | | 2.6611 | 259.76 | 193000 | 2.5203 | 0.5319 | | 2.6611 | 260.43 | 193500 | 2.5205 | 0.5313 | | 2.6611 | 261.1 | 194000 | 2.5205 | 0.5322 | | 2.6611 | 261.78 | 194500 | 2.5196 | 0.5317 | | 2.655 | 262.45 | 195000 | 2.5199 | 0.5315 | | 2.655 | 263.12 | 195500 | 2.5226 | 0.5315 | | 2.655 | 263.8 | 196000 | 2.5175 | 0.5316 | | 2.655 | 264.47 | 196500 | 2.5160 | 0.5322 | | 2.655 | 265.14 | 197000 | 2.5185 | 0.5316 | | 2.655 | 265.81 | 197500 | 2.5133 | 0.5322 | | 2.655 | 266.49 | 198000 | 2.5163 | 0.5318 | | 2.655 | 267.16 | 198500 | 2.5135 | 0.5325 | | 2.655 | 267.83 | 199000 | 2.5132 | 0.5326 | | 2.655 | 268.51 | 199500 | 2.5148 | 0.5323 | | 2.6486 | 269.18 | 200000 | 2.5194 | 0.5317 | | 2.6486 | 269.85 | 200500 | 2.5162 | 0.5321 | | 2.6486 | 270.52 | 201000 | 2.5090 | 0.5332 | | 2.6486 | 271.2 | 201500 | 2.5126 | 0.5325 | | 2.6486 | 271.87 | 202000 | 2.5155 | 0.5320 | | 2.6486 | 272.54 | 202500 | 2.5099 | 0.5329 | | 2.6486 | 273.22 | 203000 | 2.5130 | 0.5325 | | 2.6486 | 273.89 | 203500 | 2.5064 | 0.5329 | | 2.6486 | 274.56 | 204000 | 2.5154 | 0.5319 | | 2.6486 | 275.24 | 204500 | 2.5097 | 0.5329 | | 2.6433 | 275.91 | 205000 | 2.5075 | 0.5334 | | 2.6433 | 276.58 | 205500 | 2.5120 | 0.5325 | | 2.6433 | 277.25 | 206000 | 2.5100 | 0.5329 | | 2.6433 | 277.93 | 206500 | 2.5115 | 0.5332 | | 2.6433 | 278.6 | 207000 | 2.5071 | 0.5332 | | 2.6433 | 279.27 | 207500 | 2.5075 | 0.5335 | | 2.6433 | 279.95 | 208000 | 2.5020 | 0.5338 | | 2.6433 | 280.62 | 208500 | 2.5025 | 0.5340 | | 2.6433 | 281.29 | 209000 | 2.5064 | 0.5333 | | 2.6433 | 281.96 | 209500 | 2.5038 | 0.5336 | | 2.6383 | 282.64 | 210000 | 2.5041 | 0.5340 | | 2.6383 | 283.31 | 210500 | 2.5075 | 0.5336 | | 2.6383 | 283.98 | 211000 | 2.5028 | 0.5333 | | 2.6383 | 284.66 | 211500 | 2.5008 | 0.5340 | | 2.6383 | 285.33 | 212000 | 2.5005 | 0.5345 | | 2.6383 | 286.0 | 212500 | 2.5020 | 0.5334 | | 2.6383 | 286.68 | 213000 | 2.5011 | 0.5344 | | 2.6383 | 287.35 | 213500 | 2.5028 | 0.5338 | | 2.6383 | 288.02 | 214000 | 2.4970 | 0.5340 | | 2.6383 | 288.69 | 214500 | 2.4995 | 0.5343 | | 2.6336 | 289.37 | 215000 | 2.5010 | 0.5343 | | 2.6336 | 290.04 | 215500 | 2.5060 | 0.5336 | | 2.6336 | 290.71 | 216000 | 2.4955 | 0.5347 | | 2.6336 | 291.39 | 216500 | 2.4972 | 0.5349 | | 2.6336 | 292.06 | 217000 | 2.4977 | 0.5349 | | 2.6336 | 292.73 | 217500 | 2.4973 | 0.5346 | | 2.6336 | 293.4 | 218000 | 2.4981 | 0.5346 | | 2.6336 | 294.08 | 218500 | 2.4941 | 0.5346 | | 2.6336 | 294.75 | 219000 | 2.4978 | 0.5350 | | 2.6336 | 295.42 | 219500 | 2.4990 | 0.5343 | | 2.6288 | 296.1 | 220000 | 2.4929 | 0.5347 | | 2.6288 | 296.77 | 220500 | 2.4937 | 0.5349 | | 2.6288 | 297.44 | 221000 | 2.4938 | 0.5349 | | 2.6288 | 298.12 | 221500 | 2.4938 | 0.5343 | | 2.6288 | 298.79 | 222000 | 2.4924 | 0.5354 | | 2.6288 | 299.46 | 222500 | 2.4953 | 0.5348 | | 2.6288 | 300.13 | 223000 | 2.4930 | 0.5347 | | 2.6288 | 300.81 | 223500 | 2.4934 | 0.5353 | | 2.6288 | 301.48 | 224000 | 2.4942 | 0.5348 | | 2.6288 | 302.15 | 224500 | 2.4960 | 0.5344 | | 2.6246 | 302.83 | 225000 | 2.4875 | 0.5357 | | 2.6246 | 303.5 | 225500 | 2.4898 | 0.5355 | | 2.6246 | 304.17 | 226000 | 2.4847 | 0.5366 | | 2.6246 | 304.84 | 226500 | 2.4970 | 0.5348 | | 2.6246 | 305.52 | 227000 | 2.4905 | 0.5356 | | 2.6246 | 306.19 | 227500 | 2.4873 | 0.5361 | | 2.6246 | 306.86 | 228000 | 2.4939 | 0.5350 | | 2.6246 | 307.54 | 228500 | 2.4910 | 0.5360 | | 2.6246 | 308.21 | 229000 | 2.4886 | 0.5355 | | 2.6246 | 308.88 | 229500 | 2.4890 | 0.5369 | | 2.6207 | 309.56 | 230000 | 2.4900 | 0.5360 | | 2.6207 | 310.23 | 230500 | 2.4885 | 0.5354 | | 2.6207 | 310.9 | 231000 | 2.4895 | 0.5358 | | 2.6207 | 311.57 | 231500 | 2.4871 | 0.5358 | | 2.6207 | 312.25 | 232000 | 2.4914 | 0.5352 | | 2.6207 | 312.92 | 232500 | 2.4843 | 0.5366 | | 2.6207 | 313.59 | 233000 | 2.4837 | 0.5365 | | 2.6207 | 314.27 | 233500 | 2.4883 | 0.5360 | | 2.6207 | 314.94 | 234000 | 2.4839 | 0.5366 | | 2.6207 | 315.61 | 234500 | 2.4854 | 0.5366 | | 2.6171 | 316.29 | 235000 | 2.4833 | 0.5367 | | 2.6171 | 316.96 | 235500 | 2.4783 | 0.5374 | | 2.6171 | 317.63 | 236000 | 2.4807 | 0.5370 | | 2.6171 | 318.3 | 236500 | 2.4824 | 0.5366 | | 2.6171 | 318.98 | 237000 | 2.4857 | 0.5361 | | 2.6171 | 319.65 | 237500 | 2.4817 | 0.5366 | | 2.6171 | 320.32 | 238000 | 2.4855 | 0.5364 | | 2.6171 | 321.0 | 238500 | 2.4834 | 0.5367 | | 2.6171 | 321.67 | 239000 | 2.4831 | 0.5363 | | 2.6171 | 322.34 | 239500 | 2.4806 | 0.5370 | | 2.6134 | 323.01 | 240000 | 2.4842 | 0.5365 | | 2.6134 | 323.69 | 240500 | 2.4830 | 0.5364 | | 2.6134 | 324.36 | 241000 | 2.4822 | 0.5367 | | 2.6134 | 325.03 | 241500 | 2.4805 | 0.5373 | | 2.6134 | 325.71 | 242000 | 2.4838 | 0.5365 | | 2.6134 | 326.38 | 242500 | 2.4776 | 0.5371 | | 2.6134 | 327.05 | 243000 | 2.4786 | 0.5376 | | 2.6134 | 327.73 | 243500 | 2.4824 | 0.5371 | | 2.6134 | 328.4 | 244000 | 2.4842 | 0.5363 | | 2.6134 | 329.07 | 244500 | 2.4790 | 0.5375 | | 2.6107 | 329.74 | 245000 | 2.4770 | 0.5372 | | 2.6107 | 330.42 | 245500 | 2.4805 | 0.5375 | | 2.6107 | 331.09 | 246000 | 2.4839 | 0.5370 | | 2.6107 | 331.76 | 246500 | 2.4802 | 0.5367 | | 2.6107 | 332.44 | 247000 | 2.4796 | 0.5373 | | 2.6107 | 333.11 | 247500 | 2.4736 | 0.5377 | | 2.6107 | 333.78 | 248000 | 2.4789 | 0.5374 | | 2.6107 | 334.45 | 248500 | 2.4761 | 0.5375 | | 2.6107 | 335.13 | 249000 | 2.4728 | 0.5379 | | 2.6107 | 335.8 | 249500 | 2.4702 | 0.5386 | | 2.608 | 336.47 | 250000 | 2.4764 | 0.5377 | | 2.608 | 337.15 | 250500 | 2.4738 | 0.5380 | | 2.608 | 337.82 | 251000 | 2.4795 | 0.5371 | | 2.608 | 338.49 | 251500 | 2.4702 | 0.5387 | | 2.608 | 339.17 | 252000 | 2.4823 | 0.5369 | | 2.608 | 339.84 | 252500 | 2.4741 | 0.5382 | | 2.608 | 340.51 | 253000 | 2.4718 | 0.5382 | | 2.608 | 341.18 | 253500 | 2.4731 | 0.5378 | | 2.608 | 341.86 | 254000 | 2.4780 | 0.5373 | | 2.608 | 342.53 | 254500 | 2.4706 | 0.5388 | | 2.6058 | 343.2 | 255000 | 2.4707 | 0.5386 | | 2.6058 | 343.88 | 255500 | 2.4725 | 0.5380 | | 2.6058 | 344.55 | 256000 | 2.4744 | 0.5382 | | 2.6058 | 345.22 | 256500 | 2.4766 | 0.5374 | | 2.6058 | 345.89 | 257000 | 2.4736 | 0.5378 | | 2.6058 | 346.57 | 257500 | 2.4731 | 0.5383 | | 2.6058 | 347.24 | 258000 | 2.4754 | 0.5377 | | 2.6058 | 347.91 | 258500 | 2.4749 | 0.5382 | | 2.6058 | 348.59 | 259000 | 2.4735 | 0.5378 | | 2.6058 | 349.26 | 259500 | 2.4716 | 0.5384 | | 2.6027 | 349.93 | 260000 | 2.4726 | 0.5378 | | 2.6027 | 350.61 | 260500 | 2.4733 | 0.5378 | | 2.6027 | 351.28 | 261000 | 2.4698 | 0.5386 | | 2.6027 | 351.95 | 261500 | 2.4702 | 0.5388 | | 2.6027 | 352.62 | 262000 | 2.4673 | 0.5390 | | 2.6027 | 353.3 | 262500 | 2.4683 | 0.5390 | | 2.6027 | 353.97 | 263000 | 2.4739 | 0.5379 | | 2.6027 | 354.64 | 263500 | 2.4743 | 0.5382 | | 2.6027 | 355.32 | 264000 | 2.4694 | 0.5388 | | 2.6027 | 355.99 | 264500 | 2.4671 | 0.5391 | | 2.6009 | 356.66 | 265000 | 2.4747 | 0.5383 | | 2.6009 | 357.34 | 265500 | 2.4703 | 0.5382 | | 2.6009 | 358.01 | 266000 | 2.4695 | 0.5388 | | 2.6009 | 358.68 | 266500 | 2.4725 | 0.5380 | | 2.6009 | 359.35 | 267000 | 2.4639 | 0.5397 | | 2.6009 | 360.03 | 267500 | 2.4686 | 0.5385 | | 2.6009 | 360.7 | 268000 | 2.4698 | 0.5386 | | 2.6009 | 361.37 | 268500 | 2.4694 | 0.5386 | | 2.6009 | 362.05 | 269000 | 2.4680 | 0.5390 | | 2.6009 | 362.72 | 269500 | 2.4728 | 0.5383 | | 2.5989 | 363.39 | 270000 | 2.4697 | 0.5385 | | 2.5989 | 364.06 | 270500 | 2.4701 | 0.5387 | | 2.5989 | 364.74 | 271000 | 2.4702 | 0.5387 | | 2.5989 | 365.41 | 271500 | 2.4687 | 0.5390 | | 2.5989 | 366.08 | 272000 | 2.4725 | 0.5382 | | 2.5989 | 366.76 | 272500 | 2.4673 | 0.5384 | | 2.5989 | 367.43 | 273000 | 2.4659 | 0.5390 | | 2.5989 | 368.1 | 273500 | 2.4686 | 0.5389 | | 2.5989 | 368.78 | 274000 | 2.4677 | 0.5382 | | 2.5989 | 369.45 | 274500 | 2.4632 | 0.5389 | | 2.5977 | 370.12 | 275000 | 2.4676 | 0.5385 | | 2.5977 | 370.79 | 275500 | 2.4699 | 0.5388 | | 2.5977 | 371.47 | 276000 | 2.4629 | 0.5394 | | 2.5977 | 372.14 | 276500 | 2.4720 | 0.5380 | | 2.5977 | 372.81 | 277000 | 2.4678 | 0.5391 | | 2.5977 | 373.49 | 277500 | 2.4643 | 0.5396 | | 2.5977 | 374.16 | 278000 | 2.4654 | 0.5395 | | 2.5977 | 374.83 | 278500 | 2.4645 | 0.5395 | | 2.5977 | 375.5 | 279000 | 2.4649 | 0.5391 | | 2.5977 | 376.18 | 279500 | 2.4639 | 0.5392 | | 2.5961 | 376.85 | 280000 | 2.4659 | 0.5389 | | 2.5961 | 377.52 | 280500 | 2.4681 | 0.5385 | | 2.5961 | 378.2 | 281000 | 2.4641 | 0.5390 | | 2.5961 | 378.87 | 281500 | 2.4658 | 0.5393 | | 2.5961 | 379.54 | 282000 | 2.4687 | 0.5388 | | 2.5961 | 380.22 | 282500 | 2.4690 | 0.5385 | | 2.5961 | 380.89 | 283000 | 2.4679 | 0.5391 | | 2.5961 | 381.56 | 283500 | 2.4612 | 0.5395 | | 2.5961 | 382.23 | 284000 | 2.4624 | 0.5395 | | 2.5961 | 382.91 | 284500 | 2.4668 | 0.5390 | | 2.5947 | 383.58 | 285000 | 2.4663 | 0.5389 | | 2.5947 | 384.25 | 285500 | 2.4654 | 0.5387 | | 2.5947 | 384.93 | 286000 | 2.4708 | 0.5385 | | 2.5947 | 385.6 | 286500 | 2.4669 | 0.5388 | | 2.5947 | 386.27 | 287000 | 2.4612 | 0.5396 | | 2.5947 | 386.94 | 287500 | 2.4666 | 0.5392 | | 2.5947 | 387.62 | 288000 | 2.4653 | 0.5393 | | 2.5947 | 388.29 | 288500 | 2.4666 | 0.5390 | | 2.5947 | 388.96 | 289000 | 2.4684 | 0.5388 | | 2.5947 | 389.64 | 289500 | 2.4660 | 0.5394 | | 2.5936 | 390.31 | 290000 | 2.4642 | 0.5395 | | 2.5936 | 390.98 | 290500 | 2.4627 | 0.5403 | | 2.5936 | 391.66 | 291000 | 2.4683 | 0.5389 | | 2.5936 | 392.33 | 291500 | 2.4667 | 0.5387 | | 2.5936 | 393.0 | 292000 | 2.4660 | 0.5389 | | 2.5936 | 393.67 | 292500 | 2.4673 | 0.5390 | | 2.5936 | 394.35 | 293000 | 2.4645 | 0.5391 | | 2.5936 | 395.02 | 293500 | 2.4693 | 0.5389 | | 2.5936 | 395.69 | 294000 | 2.4692 | 0.5385 | | 2.5936 | 396.37 | 294500 | 2.4653 | 0.5385 | | 2.5934 | 397.04 | 295000 | 2.4661 | 0.5390 | | 2.5934 | 397.71 | 295500 | 2.4630 | 0.5394 | | 2.5934 | 398.38 | 296000 | 2.4641 | 0.5390 | | 2.5934 | 399.06 | 296500 | 2.4636 | 0.5392 | | 2.5934 | 399.73 | 297000 | 2.4650 | 0.5392 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1 ### Additional configurations ``` data: dataset_name: cc100 lang: nl overwrite_cache: False validation_split_percentage: 5 max_seq_length: 512 preprocessing_num_workers: 8 mlm_probability: 0.15 line_by_line: False pad_to_max_length: False max_train_samples: -1 max_eval_samples: -1 training: do_train: True do_eval: True do_predict: True resume_from_checkpoint: False evaluation_strategy: steps eval_steps: 500 per_device_train_batch_size: 16 per_device_eval_batch_size: 16 gradient_accumulation_steps: 32 eval_accumulation_steps: 1 learning_rate: 5e-5 weight_decay: 0.0 adam_beta1: 0.9 adam_beta2: 0.999 adam_epsilon: 1e-8 max_grad_norm: 1.0 num_train_epochs: 400.0 lr_scheduler_type: linear fp16: False warmup_steps: 8000 seed: 703 ```

kyoumiaoi/wav2vec2-base-timit-demo-google-colab

kyoumiaoi

2022-08-02T08:28:06Z

3

0

transformers

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

automatic-speech-recognition

2022-08-02T06:15:34Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-google-colab results: [] ---  # wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5499 - Wer: 0.3435 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.599 | 1.0 | 500 | 2.1267 | 0.9976 | | 1.016 | 2.01 | 1000 | 0.6193 | 0.5443 | | 0.5299 | 3.01 | 1500 | 0.5324 | 0.4889 | | 0.3626 | 4.02 | 2000 | 0.4525 | 0.4402 | | 0.2854 | 5.02 | 2500 | 0.4266 | 0.4233 | | 0.2373 | 6.02 | 3000 | 0.4713 | 0.4082 | | 0.1979 | 7.03 | 3500 | 0.4778 | 0.4018 | | 0.1761 | 8.03 | 4000 | 0.4585 | 0.3947 | | 0.1537 | 9.04 | 4500 | 0.5297 | 0.3946 | | 0.1379 | 10.04 | 5000 | 0.4988 | 0.3856 | | 0.124 | 11.04 | 5500 | 0.5262 | 0.3852 | | 0.11 | 12.05 | 6000 | 0.5545 | 0.3854 | | 0.106 | 13.05 | 6500 | 0.5196 | 0.3805 | | 0.0918 | 14.06 | 7000 | 0.4515 | 0.3655 | | 0.0829 | 15.06 | 7500 | 0.5087 | 0.3722 | | 0.0775 | 16.06 | 8000 | 0.4980 | 0.3781 | | 0.0685 | 17.07 | 8500 | 0.5564 | 0.3650 | | 0.0655 | 18.07 | 9000 | 0.5323 | 0.3672 | | 0.0578 | 19.08 | 9500 | 0.5675 | 0.3637 | | 0.052 | 20.08 | 10000 | 0.5604 | 0.3664 | | 0.0512 | 21.08 | 10500 | 0.5922 | 0.3804 | | 0.0431 | 22.09 | 11000 | 0.6379 | 0.3754 | | 0.0428 | 23.09 | 11500 | 0.5905 | 0.3764 | | 0.0393 | 24.1 | 12000 | 0.5667 | 0.3542 | | 0.0326 | 25.1 | 12500 | 0.5612 | 0.3537 | | 0.0289 | 26.1 | 13000 | 0.5618 | 0.3475 | | 0.0298 | 27.11 | 13500 | 0.5578 | 0.3439 | | 0.0264 | 28.11 | 14000 | 0.5547 | 0.3433 | | 0.026 | 29.12 | 14500 | 0.5499 | 0.3435 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.12.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

psroy/wav2vec2-base-timit-demo-google-colab

psroy

2022-08-02T07:12:02Z

4

0

transformers

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

automatic-speech-recognition

2022-07-29T04:40:18Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-google-colab results: [] ---  # wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5366 - Wer: 0.3452 ## Model description More information needed ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.5499 | 2.01 | 500 | 1.9780 | 0.9933 | | 0.7517 | 4.02 | 1000 | 0.4654 | 0.4720 | | 0.2953 | 6.02 | 1500 | 0.4202 | 0.4049 | | 0.1809 | 8.03 | 2000 | 0.4276 | 0.3759 | | 0.1335 | 10.04 | 2500 | 0.4458 | 0.3774 | | 0.107 | 12.05 | 3000 | 0.4559 | 0.3707 | | 0.0923 | 14.06 | 3500 | 0.4607 | 0.3659 | | 0.0753 | 16.06 | 4000 | 0.4699 | 0.3531 | | 0.0658 | 18.07 | 4500 | 0.4507 | 0.3588 | | 0.0569 | 20.08 | 5000 | 0.5089 | 0.3532 | | 0.0493 | 22.09 | 5500 | 0.5481 | 0.3515 | | 0.043 | 24.1 | 6000 | 0.5066 | 0.3528 | | 0.0388 | 26.1 | 6500 | 0.5418 | 0.3534 | | 0.034 | 28.11 | 7000 | 0.5566 | 0.3524 | | 0.03 | 30.12 | 7500 | 0.4994 | 0.3437 | | 0.0274 | 32.13 | 8000 | 0.5588 | 0.3520 | | 0.0239 | 34.14 | 8500 | 0.5328 | 0.3458 | | 0.0212 | 36.14 | 9000 | 0.5221 | 0.3467 | | 0.0186 | 38.15 | 9500 | 0.5366 | 0.3452 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.12.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

JAlexis/bert001

JAlexis

2022-08-02T02:59:29Z

5

0

transformers

[ "transformers", "pytorch", "bert", "question-answering", "en", "endpoints_compatible", "region:us" ]

question-answering

2022-08-02T01:35:56Z

--- language: en #epoch 7 #batch size 14 #lr 5e-5 widget: - text: "How can I protect myself against covid-19?" context: "Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model and measures of Liao et al. " - text: "How can I protect myself against covid-19?" context: " " --- ## Model description This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset. ## How to use ```python from transformers.pipelines import pipeline model_name = "JAlexis/PruebaBert" nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) inputs = { 'question': 'How can I protect myself against covid-19?', 'context': 'Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model and measures of Liao et al. [6] to the current context of the COVID-19 pandemic and the culture of the USA. Applying this model in a different time and context provides an opportunity to make comparisons of reactions to information sources across a decade of evolving attitudes toward media and government, between two cultures (Hong Kong vs. the USA), and between two considerably different global pandemics (H1N1 vs. COVID-19). ', } nlp(inputs) ```

JAlexis/PruebaBert

JAlexis

2022-08-02T01:46:49Z

27

0

transformers

[ "transformers", "pytorch", "bert", "question-answering", "en", "dataset:squad2", "dataset:cord19", "endpoints_compatible", "region:us" ]

question-answering

2022-03-02T23:29:04Z

--- language: en tags: - pytorch - question-answering datasets: - squad2 - cord19 metrics: - EM (exact match) widget: - text: "How can I protect myself against covid-19?" context: "Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model and measures of Liao et al. [6] to the current context of the COVID-19 pandemic and the culture of the USA. Applying this model in a different time and context provides an opportunity to make comparisons of reactions to information sources across a decade of evolving attitudes toward media and government, between two cultures (Hong Kong vs. the USA), and between two considerably different global pandemics (H1N1 vs. COVID-19)." - text: "How can I protect myself against covid-19?" context: " " --- ## Model description This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset. ## How to use ```python from transformers.pipelines import pipeline model_name = "JAlexis/PruebaBert" nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) inputs = { 'question': 'How can I protect myself against covid-19?', 'context': 'Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model and measures of Liao et al. [6] to the current context of the COVID-19 pandemic and the culture of the USA. Applying this model in a different time and context provides an opportunity to make comparisons of reactions to information sources across a decade of evolving attitudes toward media and government, between two cultures (Hong Kong vs. the USA), and between two considerably different global pandemics (H1N1 vs. COVID-19). ', 'question': 'How can I protect myself against covid-19?', 'context': ' ', } nlp(inputs) ``` ## Overview ``` Language model: deepset/bert-base-cased-squad2 Language: English Downstream-task: Q&A Datasets: CORD-19 from 31rd January 2022 Code: Haystack and FARM Infrastructure: Tesla T4 ``` ## Hyperparameters ``` batch_size = 8 n_epochs = 9 max_seq_len = max_length learning_rate = AdamW: 1e-5 ```

rdruce/ddpm-cheese-32

rdruce

2022-08-02T00:34:19Z

0

diffusers

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

null

2022-08-02T00:05:54Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] ---  # ddpm-cheese-32 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/rdruce/ddpm-cheese-32/tensorboard?#scalars)

muhtasham/bert-tiny-finetuned-xglue-ner

muhtasham

2022-08-01T23:20:07Z

5

1

transformers

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

token-classification

2022-08-01T23:13:29Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - xglue metrics: - precision - recall - f1 - accuracy model-index: - name: bert-tiny-finetuned-xglue-ner results: - task: name: Token Classification type: token-classification dataset: name: xglue type: xglue config: ner split: train args: ner metrics: - name: Precision type: precision value: 0.630759453447728 - name: Recall type: recall value: 0.6681252103668799 - name: F1 type: f1 value: 0.6489048708728343 - name: Accuracy type: accuracy value: 0.9274310133922189 ---  # bert-tiny-finetuned-xglue-ner This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the xglue dataset. It achieves the following results on the evaluation set: - Loss: 0.2489 - Precision: 0.6308 - Recall: 0.6681 - F1: 0.6489 - Accuracy: 0.9274 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.4082 | 1.0 | 1756 | 0.3326 | 0.5600 | 0.5798 | 0.5697 | 0.9118 | | 0.2974 | 2.0 | 3512 | 0.2635 | 0.6143 | 0.6562 | 0.6346 | 0.9248 | | 0.2741 | 3.0 | 5268 | 0.2489 | 0.6308 | 0.6681 | 0.6489 | 0.9274 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

SharpAI/mal-tls-mobilebert

SharpAI

2022-08-01T22:53:41Z

4

0

transformers

[ "transformers", "pytorch", "tf", "mobilebert", "text-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-08-01T22:45:11Z

--- tags: - generated_from_keras_callback model-index: - name: mal_tls-mobilebert results: [] ---  # mal_tls-mobilebert This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1

cansen88/turkishReviews_5_topic

cansen88

2022-08-01T22:13:12Z

4

0

transformers

[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-08-01T21:21:12Z

--- license: mit tags: - generated_from_keras_callback model-index: - name: turkishReviews_5_topic results: [] ---  # turkishReviews_5_topic This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 6.8939 - Validation Loss: 6.8949 - 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 756, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 8.0049 | 6.8949 | 0 | | 6.8943 | 6.8949 | 1 | | 6.8939 | 6.8949 | 2 | ### Framework versions - Transformers 4.21.0 - TensorFlow 2.8.2 - Datasets 2.4.0 - Tokenizers 0.12.1

Intel/bert-large-uncased-squadv1.1-sparse-80-1x4-block-pruneofa

Intel

2022-08-01T21:04:22Z

75

1

transformers

[ "transformers", "pytorch", "onnx", "bert", "question-answering", "en", "arxiv:2111.05754", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-03-27T20:17:27Z

--- language: en license: apache-2.0 --- # 80% 1x4 Block Sparse BERT-Large (uncased) Fine Tuned on SQuADv1.1 This model is a result of fine-tuning a Prune OFA 80% 1x4 block sparse pre-trained BERT-Large combined with knowledge distillation. This model yields the following results on SQuADv1.1 development set:<br> `{"exact_match": 84.673, "f1": 91.174}` For further details see our paper, [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754), and our open source implementation available [here](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all).

mrm8488/pyramidsrnd

mrm8488

2022-08-01T20:36:43Z

9

1

ml-agents

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

reinforcement-learning

2022-08-01T20:36:37Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # **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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: mrm8488/pyramidsrnd 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

muhtasham/bert-tiny-finetuned-pile-of-law-tos

muhtasham

2022-08-01T20:24:25Z

6

1

transformers

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

fill-mask

2022-08-01T18:22:18Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-tiny-finetuned-pile-of-law-tos results: [] ---  # bert-tiny-finetuned-pile-of-law-tos This model is a MLM fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the [pile-of-law/tos](https://huggingface.co/datasets/pile-of-law/pile-of-law) dataset. It achieves the following results on the evaluation set: - Loss: 3.3545 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 264 | 3.5896 | | 3.8119 | 2.0 | 528 | 3.5598 | | 3.8119 | 3.0 | 792 | 3.5263 | | 3.7028 | 4.0 | 1056 | 3.4982 | | 3.7028 | 5.0 | 1320 | 3.5170 | | 3.6286 | 6.0 | 1584 | 3.5143 | | 3.6286 | 7.0 | 1848 | 3.4477 | | 3.553 | 8.0 | 2112 | 3.4044 | | 3.553 | 9.0 | 2376 | 3.4670 | | 3.5179 | 10.0 | 2640 | 3.3991 | | 3.5179 | 11.0 | 2904 | 3.4330 | | 3.4784 | 12.0 | 3168 | 3.4671 | | 3.4784 | 13.0 | 3432 | 3.3489 | | 3.4535 | 14.0 | 3696 | 3.4354 | | 3.4535 | 15.0 | 3960 | 3.4023 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

SharpAI/mal-tls-bert-base-relu-w8a8

SharpAI

2022-08-01T20:23:16Z

4

0

transformers

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

text-classification

2022-08-01T20:22:51Z

--- tags: - generated_from_keras_callback model-index: - name: mal_tls-bert-base-relu-w8a8 results: [] ---  # mal_tls-bert-base-relu-w8a8 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.15.0 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.10.3

arize-ai/resnet-50-fashion-mnist-quality-drift

arize-ai

2022-08-01T19:55:57Z

182

4

transformers

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

image-classification

2022-08-01T19:32:07Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - fashion_mnist_quality_drift metrics: - accuracy - f1 model-index: - name: resnet-50-fashion-mnist-quality-drift results: - task: name: Image Classification type: image-classification dataset: name: fashion_mnist_quality_drift type: fashion_mnist_quality_drift config: default split: training args: default metrics: - name: Accuracy type: accuracy value: 0.73 - name: F1 type: f1 value: 0.7289360255705818 ---  # resnet-50-fashion-mnist-quality-drift This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the fashion_mnist_quality_drift dataset. It achieves the following results on the evaluation set: - Loss: 0.7473 - Accuracy: 0.73 - F1: 0.7289 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.5138 | 1.0 | 750 | 0.9237 | 0.684 | 0.6826 | | 0.9377 | 2.0 | 1500 | 0.7861 | 0.722 | 0.7253 | | 0.8276 | 3.0 | 2250 | 0.7473 | 0.73 | 0.7289 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

SharpAI/mal-tls-bert-base-relu

SharpAI

2022-08-01T19:54:00Z

5

0

transformers

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

text-classification

2022-08-01T19:53:07Z

--- tags: - generated_from_keras_callback model-index: - name: mal_tls-bert-base-relu results: [] ---  # mal_tls-bert-base-relu This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1

mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es

mrm8488

2022-08-01T19:41:40Z

1,199

3

sentence-transformers

[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "es", "dataset:stsb_multi_mt", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-03-02T23:29:05Z

--- language: es thumbnail: https://imgur.com/a/G77ZqQN pipeline_tag: sentence-similarity datasets: - stsb_multi_mt tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # Distiluse-m-v2 fine-tuned on stsb_multi_mt for Spanish Semantic Textual Similarity This is a [sentence-transformers](https://www.SBERT.net) model (distiluse-base-multilingual-cased-v2): 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 = ["Nerea va a comprar un cuadro usando bitcoins", "Se puede comprar arte con bitcoins"] model = SentenceTransformer('mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es') 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 = ["Nerea va a comprar un cuadro usando bitcoins", "Se puede comprar arte con bitcoins"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es') model = AutoModel.from_pretrained('mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es') # 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) ``` ## How to evaluate ```py from datasets import load_dataset from sentence_transformers import SentenceTransformer, InputExample from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator test_data = load_dataset('stsb_multi_mt', 'es', split='test') test_data = test_data.rename_columns({'similarity_score': 'label'}) test_data = test_data.map(lambda x: {'label': x['label'] / 5.0}) samples = [] for sample in test_data: samples.append(InputExample( texts=[sample['sentence1'], sample['sentence2']], label=sample['label'] )) evaluator = EmbeddingSimilarityEvaluator.from_input_examples( samples, write_csv=False ) model = SentenceTransformer('mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es') evaluator(model) # It outputs: 0.7604056195656299 ``` ## Evaluation Results **Spearman’s rank correlation: 0.7604056195656299** 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**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 906 with parameters: ``` {'batch_size': 16} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 271, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (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

vidyavenkappa/pegasus-samsum

vidyavenkappa

2022-08-01T18:30:17Z

4

0

transformers

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

text2text-generation

2022-07-30T12:10:24Z

--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum results: [] ---  # pegasus-samsum This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.3086 ## Model description More information needed ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.6151 | 0.54 | 500 | 1.4238 | | 1.3357 | 1.09 | 1000 | 1.3629 | | 1.4423 | 1.63 | 1500 | 1.3380 | | 1.3747 | 2.17 | 2000 | 1.3218 | | 1.3397 | 2.72 | 2500 | 1.3124 | | 1.2706 | 3.26 | 3000 | 1.3149 | | 1.1849 | 3.8 | 3500 | 1.3120 | | 1.2222 | 4.35 | 4000 | 1.3120 | | 1.2339 | 4.89 | 4500 | 1.3086 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

turhancan97/Reinforce-2

turhancan97

2022-08-01T16:45:20Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2022-08-01T16:44:23Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-2 results: - metrics: - type: mean_reward value: 9.40 +/- 13.66 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5

silviacamplani/twitter-roberta-base-finetuned-ner-wnut

silviacamplani

2022-08-01T16:26:39Z

5

0

transformers

[ "transformers", "tf", "tensorboard", "roberta", "token-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-08-01T15:50:19Z

--- tags: - generated_from_keras_callback model-index: - name: silviacamplani/twitter-roberta-base-finetuned-ner-wnut results: [] ---  # silviacamplani/twitter-roberta-base-finetuned-ner-wnut This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0812 - Validation Loss: 0.2553 - Train Precision: 0.6263 - Train Recall: 0.5191 - Train F1: 0.5677 - Train Accuracy: 0.9398 - 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: {'inner_optimizer': {'class_name': 'Adam', 'config': {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.0813 | 0.2553 | 0.6263 | 0.5191 | 0.5677 | 0.9398 | 0 | | 0.0815 | 0.2553 | 0.6263 | 0.5191 | 0.5677 | 0.9398 | 1 | | 0.0812 | 0.2553 | 0.6263 | 0.5191 | 0.5677 | 0.9398 | 2 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1

thocheat/v2-fine-tune-wav2vec2-Vietnamese-ARS-demo

thocheat

2022-08-01T16:01:15Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-08-01T14:23:01Z

--- license: cc-by-nc-4.0 tags: - generated_from_trainer model-index: - name: v2-fine-tune-wav2vec2-Vietnamese-ARS-demo results: [] ---  # v2-fine-tune-wav2vec2-Vietnamese-ARS-demo This model is a fine-tuned version of [nguyenvulebinh/wav2vec2-base-vietnamese-250h](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2515 - Wer: 0.2235 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 8.8651 | 0.34 | 500 | 3.6919 | 0.9999 | | 3.54 | 0.69 | 1000 | 3.3584 | 1.0 | | 2.9478 | 1.03 | 1500 | 2.2535 | 0.9885 | | 1.9147 | 1.37 | 2000 | 0.9977 | 0.7260 | | 1.1667 | 1.71 | 2500 | 0.5577 | 0.4746 | | 0.844 | 2.06 | 3000 | 0.4129 | 0.3581 | | 0.6968 | 2.4 | 3500 | 0.3566 | 0.3090 | | 0.6273 | 2.74 | 4000 | 0.3243 | 0.2813 | | 0.5434 | 3.09 | 4500 | 0.3076 | 0.2631 | | 0.5069 | 3.43 | 5000 | 0.2902 | 0.2539 | | 0.4842 | 3.77 | 5500 | 0.2752 | 0.2432 | | 0.4318 | 4.12 | 6000 | 0.2854 | 0.2384 | | 0.3951 | 4.46 | 6500 | 0.2674 | 0.2350 | | 0.3954 | 4.8 | 7000 | 0.2628 | 0.2322 | | 0.3763 | 5.14 | 7500 | 0.2609 | 0.2284 | | 0.3652 | 5.49 | 8000 | 0.2508 | 0.2249 | | 0.3703 | 5.83 | 8500 | 0.2515 | 0.2235 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.12.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

silviacamplani/distilbert-base-uncased-finetuned-ner-wnut

silviacamplani

2022-08-01T14:53:43Z

4

0

transformers

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

token-classification

2022-08-01T10:37:08Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: silviacamplani/distilbert-base-uncased-finetuned-ner-wnut results: [] ---  # silviacamplani/distilbert-base-uncased-finetuned-ner-wnut 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: 0.1241 - Validation Loss: 0.3433 - Train Precision: 0.5677 - Train Recall: 0.3660 - Train F1: 0.4451 - Train Accuracy: 0.9215 - 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: {'inner_optimizer': {'class_name': 'Adam', 'config': {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.3454 | 0.4475 | 0.0 | 0.0 | 0.0 | 0.8961 | 0 | | 0.1637 | 0.3637 | 0.6297 | 0.2990 | 0.4055 | 0.9154 | 1 | | 0.1241 | 0.3433 | 0.5677 | 0.3660 | 0.4451 | 0.9215 | 2 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1

dminiotas05/distilbert-base-uncased-finetuned-ft750_reg5

dminiotas05

2022-08-01T14:18:11Z

5

0

transformers

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

text-classification

2022-08-01T13:57:20Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-ft750_reg5 results: [] ---  # distilbert-base-uncased-finetuned-ft750_reg5 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: - Loss: 0.6298 - Mse: 0.6298 - Mae: 0.6087 - R2: 0.4072 - Accuracy: 0.4973 ## Model description More information needed ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:| | 1.8617 | 1.0 | 188 | 0.7482 | 0.7482 | 0.6639 | 0.2957 | 0.4707 | | 0.5667 | 2.0 | 376 | 0.6017 | 0.6017 | 0.5978 | 0.4336 | 0.5127 | | 0.5038 | 3.0 | 564 | 0.6298 | 0.6298 | 0.6087 | 0.4072 | 0.4973 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

muhtasham/bert-tiny-finetuned-finer-tf

muhtasham

2022-08-01T13:41:59Z

4

0

transformers

[ "transformers", "tf", "bert", "token-classification", "generated_from_keras_callback", "dataset:nlpaueb/finer-139", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-07-29T22:13:44Z

--- license: apache-2.0 datasets: - nlpaueb/finer-139 tags: - generated_from_keras_callback model-index: - name: muhtasham/bert-tiny-finetuned-finer-tf results: [] ---  # muhtasham/bert-tiny-finetuned-finer-tf This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0372 - Validation Loss: 0.0296 - 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 168822, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1188 | 0.0420 | 0 | | 0.0438 | 0.0313 | 1 | | 0.0372 | 0.0296 | 2 | ### Framework versions - Transformers 4.21.0 - TensorFlow 2.8.2 - Datasets 2.4.0 - Tokenizers 0.12.1

rdruce/ddpm-butterflies-128

rdruce

2022-08-01T12:46:38Z

0

diffusers

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

null

2022-08-01T11:33:05Z

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

sumba/covid-twitter-bert-v2-no_description-stance-loss-hyp

sumba

2022-08-01T12:16:28Z

6

0

transformers

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

text-classification

2022-07-31T12:21:31Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: covid-twitter-bert-v2-no_description-stance-loss-hyp results: [] ---  # covid-twitter-bert-v2-no_description-stance-loss-hyp This model is a fine-tuned version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6202 - Accuracy: 0.0829 ## Model description More information needed ## 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: 1.4275469935864394e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8211 | 1.0 | 632 | 0.6258 | 0.1153 | | 0.5742 | 2.0 | 1264 | 0.6202 | 0.0829 | | 0.4456 | 3.0 | 1896 | 0.6340 | 0.0627 | | 0.2163 | 4.0 | 2528 | 0.7645 | 0.0470 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu102 - Datasets 2.2.1 - Tokenizers 0.12.1

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

BekirTaha

2022-08-01T12:12:18Z

0

null

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

reinforcement-learning

2022-08-01T12:01:10Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="Beyko7/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

turhancan97/testpyramidsrnd

turhancan97

2022-08-01T12:08:01Z

6

0

ml-agents

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

reinforcement-learning

2022-08-01T12:07:56Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # **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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: turhancan97/testpyramidsrnd 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

dminiotas05/distilbert-base-uncased-finetuned-ft750_reg3

dminiotas05

2022-08-01T11:51:26Z

6

0

transformers

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

text-classification

2022-08-01T11:22:10Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-ft750_reg3 results: [] ---  # distilbert-base-uncased-finetuned-ft750_reg3 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: - Loss: 0.6143 - Mse: 0.6143 - Mae: 0.6022 - R2: 0.4218 - Accuracy: 0.52 ## Model description More information needed ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:| | 0.5241 | 1.0 | 188 | 0.6143 | 0.6143 | 0.6022 | 0.4218 | 0.52 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

lewiswu1209/Winnie

lewiswu1209

2022-08-01T10:52:48Z

5

0

transformers

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

text-generation

2022-07-24T03:09:47Z

--- license: mit --- # Winnie Winnie是基于[cambridgeltl/simctg_lccc_dialogue](https://huggingface.co/cambridgeltl/simctg_lccc_dialogue)训练的我修改了vocab.txt, 新增了`[NAME][NICK][GENDER][YEAROFBIRTH][MONTHOFBIRTH][DAYOFBIRTH][ZODIAC][AGE]`几个special_token，然后搞了些类似 ``` 你是谁？我是[NAME]。你叫什么？我叫[NAME]。你多大啦？我[AGE]岁了。 ``` 的语料。第一次训练的时候起名叫Vicky，然后把Vicky的脑子训瓦特了，只能摸索新的办法了。后来利用了[50W闲聊语料](https://github.com/yangjianxin1/GPT2-chitchat#%E9%97%B2%E8%81%8A%E8%AF%AD%E6%96%99%E5%88%86%E4%BA%AB)搭配新增的语料按照大约19:1的比例进行训练，感觉效果还可以。

dminiotas05/camembert-base-finetuned-ft750_reg2

dminiotas05

2022-08-01T10:10:20Z

4

0

transformers

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

text-classification

2022-07-28T11:03:55Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: camembert-base-finetuned-ft750_reg2 results: [] ---  # camembert-base-finetuned-ft750_reg2 This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6449 - Mse: 0.6449 - Mae: 0.6171 - R2: 0.3929 - Accuracy: 0.504 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:| | 0.6283 | 1.0 | 750 | 0.6074 | 0.6074 | 0.6086 | 0.4282 | 0.4887 | | 0.5007 | 2.0 | 1500 | 0.6449 | 0.6449 | 0.6171 | 0.3929 | 0.504 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

lakshaywadhwa1993/ner_hindi_bert

lakshaywadhwa1993

2022-08-01T09:14:58Z

8

1

transformers

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

token-classification

2022-08-01T09:05:27Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - wikiann model-index: - name: ner_hindi_bert results: [] ---  # ner_hindi_bert This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.3713 - Overall Precision: 0.8942 - Overall Recall: 0.8972 - Overall F1: 0.8957 - Overall Accuracy: 0.9367 - Loc F1: 0.8766 - Org F1: 0.8489 - Per F1: 0.9454 ## Model description More information needed ## 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: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Loc F1 | Org F1 | Per F1 | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|:------:| | 0.2993 | 3.19 | 1000 | 0.3230 | 0.8779 | 0.8786 | 0.8782 | 0.9244 | 0.8535 | 0.8270 | 0.9358 | | 0.0641 | 6.39 | 2000 | 0.3713 | 0.8942 | 0.8972 | 0.8957 | 0.9367 | 0.8766 | 0.8489 | 0.9454 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

psroy/wav2vec2-base-timit-demo-colab

psroy

2022-08-01T08:59:16Z

3

0

transformers

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

automatic-speech-recognition

2022-07-29T10:16:59Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] ---  # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4772 - Wer: 0.2821 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.6949 | 0.87 | 500 | 2.4599 | 0.9999 | | 0.9858 | 1.73 | 1000 | 0.5249 | 0.4674 | | 0.4645 | 2.6 | 1500 | 0.4604 | 0.3900 | | 0.3273 | 3.46 | 2000 | 0.3939 | 0.3612 | | 0.2474 | 4.33 | 2500 | 0.4150 | 0.3560 | | 0.2191 | 5.19 | 3000 | 0.3855 | 0.3344 | | 0.1662 | 6.06 | 3500 | 0.3779 | 0.3258 | | 0.1669 | 6.92 | 4000 | 0.4841 | 0.3286 | | 0.151 | 7.79 | 4500 | 0.4182 | 0.3219 | | 0.1175 | 8.65 | 5000 | 0.4194 | 0.3107 | | 0.1103 | 9.52 | 5500 | 0.4256 | 0.3129 | | 0.1 | 10.38 | 6000 | 0.4352 | 0.3089 | | 0.0949 | 11.25 | 6500 | 0.4649 | 0.3160 | | 0.0899 | 12.11 | 7000 | 0.4472 | 0.3065 | | 0.0787 | 12.98 | 7500 | 0.4763 | 0.3128 | | 0.0742 | 13.84 | 8000 | 0.4321 | 0.3034 | | 0.067 | 14.71 | 8500 | 0.4562 | 0.3076 | | 0.063 | 15.57 | 9000 | 0.4541 | 0.3102 | | 0.0624 | 16.44 | 9500 | 0.5113 | 0.3040 | | 0.0519 | 17.3 | 10000 | 0.4925 | 0.3008 | | 0.0525 | 18.17 | 10500 | 0.4710 | 0.2987 | | 0.046 | 19.03 | 11000 | 0.4781 | 0.2977 | | 0.0455 | 19.9 | 11500 | 0.4572 | 0.2969 | | 0.0394 | 20.76 | 12000 | 0.5256 | 0.2966 | | 0.0373 | 21.63 | 12500 | 0.4723 | 0.2921 | | 0.0375 | 22.49 | 13000 | 0.4640 | 0.2847 | | 0.0334 | 23.36 | 13500 | 0.4740 | 0.2917 | | 0.0304 | 24.22 | 14000 | 0.4817 | 0.2874 | | 0.0291 | 25.09 | 14500 | 0.4722 | 0.2896 | | 0.0247 | 25.95 | 15000 | 0.4765 | 0.2870 | | 0.0223 | 26.82 | 15500 | 0.4728 | 0.2821 | | 0.0223 | 27.68 | 16000 | 0.4690 | 0.2834 | | 0.0207 | 28.55 | 16500 | 0.4706 | 0.2825 | | 0.0186 | 29.41 | 17000 | 0.4772 | 0.2821 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.12.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

lakshaywadhwa1993/ner_marathi_bert

lakshaywadhwa1993

2022-08-01T08:39:52Z

3

0

transformers

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

token-classification

2022-06-09T21:00:19Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - wikiann model-index: - name: ner_marathi_bert results: [] ---  # ner_marathi_bert This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.3606 - Overall Precision: 0.8939 - Overall Recall: 0.9030 - Overall F1: 0.8984 - Overall Accuracy: 0.9347 - Loc F1: 0.8823 - Org F1: 0.8555 - Per F1: 0.9435 ## Model description More information needed ## 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: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Loc F1 | Org F1 | Per F1 | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|:------:| | 0.2961 | 3.19 | 1000 | 0.3496 | 0.8720 | 0.8841 | 0.8780 | 0.9229 | 0.8599 | 0.8210 | 0.9343 | | 0.0613 | 6.39 | 2000 | 0.3606 | 0.8939 | 0.9030 | 0.8984 | 0.9347 | 0.8823 | 0.8555 | 0.9435 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

meln1k/a2c-AntBulletEnv-v0

meln1k

2022-08-01T08:04:22Z

5

0

stable-baselines3

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

reinforcement-learning

2022-08-01T08:03:39Z

--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - metrics: - type: mean_reward value: 2061.72 +/- 70.57 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code

BekirTaha/ppo-LunarLander-v2

BekirTaha

2022-08-01T07:53:28Z

4

0

stable-baselines3

[ "stable-baselines3", "deep-reinforcement-learning", "reinforcement-learning", "region:us" ]

reinforcement-learning

2022-08-01T06:40:27Z

--- tags: - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 --- # "Beyko7/ppo-LunarLander-v2" This is a pre-trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library. ### Usage (with Stable-baselines3) Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed: ``` pip install stable-baselines3 pip install huggingface_sb3 ``` Then, you can use the model like this: ```python import gym from huggingface_sb3 import load_from_hub from stable_baselines3 import PPO from stable_baselines3.common.evaluation import evaluate_policy # Retrieve the model from the hub ## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name}) ## filename = name of the model zip file from the repository checkpoint = load_from_hub(repo_id="Beyko7/ppo-LunarLander-v2", filename="LunarLander-v2.zip") model = PPO.load(checkpoint) # Evaluate the agent eval_env = gym.make('LunarLander-v2') mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True) print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") # Watch the agent play obs = env.reset() for i in range(1000): action, _state = model.predict(obs) obs, reward, done, info = env.step(action) env.render() if done: obs = env.reset() env.close() ``` ### Evaluation Results Mean_reward: 248.30 +/- 23.32882124373712 ---

huggingtweets/kantegory

huggingtweets

2022-08-01T07:26:39Z

3

0

transformers

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

text-generation

2022-08-01T07:26:04Z

--- language: en thumbnail: http://www.huggingtweets.com/kantegory/1659338795219/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1122432883036172288/mYZ4acNy_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">David Dobryakov</div> <div style="text-align: center; font-size: 14px;">@kantegory</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from David Dobryakov. | Data | David Dobryakov | | --- | --- | | Tweets downloaded | 3017 | | Retweets | 90 | | Short tweets | 256 | | Tweets kept | 2671 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1g9yc7mp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @kantegory's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2aeg6rk1) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2aeg6rk1/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/kantegory') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

KDHyun08/TAACO_STS

KDHyun08

2022-08-01T05:00:14Z

2,406

2

sentence-transformers

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

sentence-similarity

2022-07-25T08:19:31Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - transformers - TAACO language: ko --- # TAACO_Similarity 본 모델은 [Sentence-transformers](https://www.SBERT.net)를 기반으로 하며 KLUE의 STS(Sentence Textual Similarity) 데이터셋을 통해 훈련을 진행한 모델입니다. 필자가 제작하고 있는 한국어 문장간 결속성 측정 도구인 K-TAACO(가제)의 지표 중 하나인 문장 간 의미적 결속성을 측정하기 위해 본 모델을 제작하였습니다. 또한 모두의 말뭉치의 문장간 유사도 데이터 등 다양한 데이터를 구해 추가 훈련을 진행할 예정입니다. ## Train Data KLUE-sts-v1.1._train.json NLI-sts-train.tsv ## Usage (Sentence-Transformers) 본 모델을 사용하기 위해서는 [Sentence-transformers](https://www.SBERT.net)를 설치하여야 합니다. ``` pip install -U sentence-transformers ``` 모델을 사용하기 위해서는 아래 코드를 참조하시길 바랍니다. ```python from sentence_transformers import SentenceTransformer, models sentences = ["This is an example sentence", "Each sentence is converted"] embedding_model = models.Transformer( model_name_or_path="KDHyun08/TAACO_STS", max_seq_length=256, do_lower_case=True ) pooling_model = models.Pooling( embedding_model.get_word_embedding_dimension(), pooling_mode_mean_tokens=True, pooling_mode_cls_token=False, pooling_mode_max_tokens=False, ) model = SentenceTransformer(modules=[embedding_model, pooling_model]) embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (실제 문장 간 유사도 비교) [Sentence-transformers](https://www.SBERT.net) 를 설치한 후 아래 내용과 같이 문장 간 유사도를 비교할 수 있습니다. query 변수는 비교 기준이 되는 문장(Source Sentence)이고 비교를 진행할 문장은 docs에 list 형식으로 구성하시면 됩니다. ```python from sentence_transformers import SentenceTransformer, models embedding_model = models.Transformer( model_name_or_path="KDHyun08/TAACO_STS", max_seq_length=256, do_lower_case=True ) pooling_model = models.Pooling( embedding_model.get_word_embedding_dimension(), pooling_mode_mean_tokens=True, pooling_mode_cls_token=False, pooling_mode_max_tokens=False, ) model = SentenceTransformer(modules=[embedding_model, pooling_model]) docs = ['어제는 아내의 생일이었다', '생일을 맞이하여 아침을 준비하겠다고 오전 8시 30분부터 음식을 준비하였다. 주된 메뉴는 스테이크와 낙지볶음, 미역국, 잡채, 소야 등이었다', '스테이크는 자주 하는 음식이어서 자신이 준비하려고 했다', '앞뒤도 1분씩 3번 뒤집고 래스팅을 잘 하면 육즙이 가득한 스테이크가 준비되다', '아내도 그런 스테이크를 좋아한다. 그런데 상상도 못한 일이 벌이지고 말았다', '보통 시즈닝이 되지 않은 원육을 사서 스테이크를 했는데, 이번에는 시즈닝이 된 부챗살을 구입해서 했다', '그런데 케이스 안에 방부제가 들어있는 것을 인지하지 못하고 방부제와 동시에 프라이팬에 올려놓을 것이다', '그것도 인지 못한 체... 앞면을 센 불에 1분을 굽고 뒤집는 순간 방부제가 함께 구어진 것을 알았다', '아내의 생일이라 맛있게 구워보고 싶었는데 어처구니없는 상황이 발생한 것이다', '방부제가 센 불에 녹아서 그런지 물처럼 흘러내렸다', ' 고민을 했다. 방부제가 묻은 부문만 제거하고 다시 구울까 했는데 방부제에 절대 먹지 말라는 문구가 있어서 아깝지만 버리는 방향을 했다', '너무나 안타까웠다', '아침 일찍 아내가 좋아하는 스테이크를 준비하고 그것을 맛있게 먹는 아내의 모습을 보고 싶었는데 전혀 생각지도 못한 상황이 발생해서... 하지만 정신을 추스르고 바로 다른 메뉴로 변경했다', '소야, 소시지 야채볶음..', '아내가 좋아하는지 모르겠지만 냉장고 안에 있는 후랑크소세지를 보니 바로 소야를 해야겠다는 생각이 들었다. 음식은 성공적으로 완성이 되었다', '40번째를 맞이하는 아내의 생일은 성공적으로 준비가 되었다', '맛있게 먹어 준 아내에게도 감사했다', '매년 아내의 생일에 맞이하면 아침마다 생일을 차려야겠다. 오늘도 즐거운 하루가 되었으면 좋겠다', '생일이니까~'] #각 문장의 vector값 encoding document_embeddings = model.encode(docs) query = '생일을 맞이하여 아침을 준비하겠다고 오전 8시 30분부터 음식을 준비하였다' query_embedding = model.encode(query) top_k = min(10, len(docs)) # 코사인 유사도 계산 후, cos_scores = util.pytorch_cos_sim(query_embedding, document_embeddings)[0] # 코사인 유사도 순으로 문장 추출 top_results = torch.topk(cos_scores, k=top_k) print(f"입력 문장: {query}") print(f"\n<입력 문장과 유사한 {top_k} 개의 문장>\n") for i, (score, idx) in enumerate(zip(top_results[0], top_results[1])): print(f"{i+1}: {docs[idx]} {'(유사도: {:.4f})'.format(score)}\n") ``` ## Evaluation Results 위 Usage를 실행하게 되면 아래와 같은 결과가 도출됩니다. 1에 가까울수록 유사한 문장입니다. ``` 입력 문장: 생일을 맞이하여 아침을 준비하겠다고 오전 8시 30분부터 음식을 준비하였다 <입력 문장과 유사한 10 개의 문장> 1: 생일을 맞이하여 아침을 준비하겠다고 오전 8시 30분부터 음식을 준비하였다. 주된 메뉴는 스테이크와 낙지볶음, 미역국, 잡채, 소야 등이었다 (유사도: 0.6687) 2: 매년 아내의 생일에 맞이하면 아침마다 생일을 차려야겠다. 오늘도 즐거운 하루가 되었으면 좋겠다 (유사도: 0.6468) 3: 40번째를 맞이하는 아내의 생일은 성공적으로 준비가 되었다 (유사도: 0.4647) 4: 아내의 생일이라 맛있게 구워보고 싶었는데 어처구니없는 상황이 발생한 것이다 (유사도: 0.4469) 5: 생일이니까~ (유사도: 0.4218) 6: 어제는 아내의 생일이었다 (유사도: 0.4192) 7: 아침 일찍 아내가 좋아하는 스테이크를 준비하고 그것을 맛있게 먹는 아내의 모습을 보고 싶었는데 전혀 생각지도 못한 상황이 발생해서... 하지만 정신을 추스르고 바로 다른 메뉴로 변경했다 (유사도: 0.4156) 8: 맛있게 먹어 준 아내에게도 감사했다 (유사도: 0.3093) 9: 아내가 좋아하는지 모르겠지만 냉장고 안에 있는 후랑크소세지를 보니 바로 소야를 해야겠다는 생각이 들었다. 음식은 성공적으로 완성이 되었다 (유사도: 0.2259) 10: 아내도 그런 스테이크를 좋아한다. 그런데 상상도 못한 일이 벌이지고 말았다 (유사도: 0.1967) ``` **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 142 with parameters: ``` {'batch_size': 32, '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": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

wenkai-li/distilroberta-base-finetuned-marktextepoch_n200

wenkai-li

2022-08-01T04:07:13Z

3

0

transformers

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

fill-mask

2022-07-31T18:33:57Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-marktextepoch_n200 results: [] ---  # distilroberta-base-finetuned-marktextepoch_n200 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0531 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 2.2313 | 1.0 | 1500 | 2.1592 | | 2.1731 | 2.0 | 3000 | 2.1277 | | 2.153 | 3.0 | 4500 | 2.1144 | | 2.1469 | 4.0 | 6000 | 2.1141 | | 2.1281 | 5.0 | 7500 | 2.1374 | | 2.1043 | 6.0 | 9000 | 2.1069 | | 2.0834 | 7.0 | 10500 | 2.0993 | | 2.0602 | 8.0 | 12000 | 2.0817 | | 2.024 | 9.0 | 13500 | 2.0918 | | 2.0261 | 10.0 | 15000 | 2.0793 | | 1.9889 | 11.0 | 16500 | 2.0567 | | 1.9915 | 12.0 | 18000 | 2.0700 | | 1.9532 | 13.0 | 19500 | 2.0436 | | 1.9362 | 14.0 | 21000 | 2.0596 | | 1.9024 | 15.0 | 22500 | 2.0189 | | 1.9262 | 16.0 | 24000 | 2.0435 | | 1.8883 | 17.0 | 25500 | 2.0430 | | 1.8867 | 18.0 | 27000 | 2.0416 | | 1.8807 | 19.0 | 28500 | 2.0051 | | 1.8517 | 20.0 | 30000 | 2.0338 | | 1.8357 | 21.0 | 31500 | 2.0166 | | 1.8241 | 22.0 | 33000 | 2.0355 | | 1.7985 | 23.0 | 34500 | 2.0073 | | 1.8061 | 24.0 | 36000 | 2.0473 | | 1.7996 | 25.0 | 37500 | 2.0446 | | 1.7786 | 26.0 | 39000 | 2.0086 | | 1.771 | 27.0 | 40500 | 2.0294 | | 1.7549 | 28.0 | 42000 | 2.0127 | | 1.7726 | 29.0 | 43500 | 2.0191 | | 1.7275 | 30.0 | 45000 | 2.0182 | | 1.708 | 31.0 | 46500 | 2.0130 | | 1.7345 | 32.0 | 48000 | 2.0155 | | 1.7044 | 33.0 | 49500 | 1.9898 | | 1.7126 | 34.0 | 51000 | 2.0166 | | 1.698 | 35.0 | 52500 | 1.9879 | | 1.6637 | 36.0 | 54000 | 2.0311 | | 1.6854 | 37.0 | 55500 | 2.0355 | | 1.6585 | 38.0 | 57000 | 2.0094 | | 1.6418 | 39.0 | 58500 | 2.0042 | | 1.667 | 40.0 | 60000 | 2.0116 | | 1.6507 | 41.0 | 61500 | 2.0095 | | 1.622 | 42.0 | 63000 | 2.0158 | | 1.6381 | 43.0 | 64500 | 2.0339 | | 1.6099 | 44.0 | 66000 | 2.0082 | | 1.6076 | 45.0 | 67500 | 2.0207 | | 1.5805 | 46.0 | 69000 | 2.0172 | | 1.5862 | 47.0 | 70500 | 2.0132 | | 1.5806 | 48.0 | 72000 | 2.0198 | | 1.574 | 49.0 | 73500 | 2.0181 | | 1.5718 | 50.0 | 75000 | 2.0086 | | 1.5591 | 51.0 | 76500 | 1.9832 | | 1.5468 | 52.0 | 78000 | 2.0167 | | 1.5637 | 53.0 | 79500 | 2.0118 | | 1.5117 | 54.0 | 81000 | 2.0290 | | 1.5363 | 55.0 | 82500 | 2.0011 | | 1.4976 | 56.0 | 84000 | 2.0160 | | 1.5129 | 57.0 | 85500 | 2.0224 | | 1.4964 | 58.0 | 87000 | 2.0219 | | 1.4906 | 59.0 | 88500 | 2.0212 | | 1.4941 | 60.0 | 90000 | 2.0255 | | 1.4876 | 61.0 | 91500 | 2.0116 | | 1.4837 | 62.0 | 93000 | 2.0176 | | 1.4661 | 63.0 | 94500 | 2.0388 | | 1.4634 | 64.0 | 96000 | 2.0165 | | 1.4449 | 65.0 | 97500 | 2.0185 | | 1.468 | 66.0 | 99000 | 2.0246 | | 1.4567 | 67.0 | 100500 | 2.0244 | | 1.4367 | 68.0 | 102000 | 2.0093 | | 1.4471 | 69.0 | 103500 | 2.0101 | | 1.4255 | 70.0 | 105000 | 2.0248 | | 1.4203 | 71.0 | 106500 | 2.0224 | | 1.42 | 72.0 | 108000 | 2.0279 | | 1.4239 | 73.0 | 109500 | 2.0295 | | 1.4126 | 74.0 | 111000 | 2.0196 | | 1.4038 | 75.0 | 112500 | 2.0225 | | 1.3874 | 76.0 | 114000 | 2.0456 | | 1.3758 | 77.0 | 115500 | 2.0423 | | 1.3924 | 78.0 | 117000 | 2.0184 | | 1.3744 | 79.0 | 118500 | 2.0555 | | 1.3622 | 80.0 | 120000 | 2.0387 | | 1.3653 | 81.0 | 121500 | 2.0344 | | 1.3724 | 82.0 | 123000 | 2.0184 | | 1.3684 | 83.0 | 124500 | 2.0285 | | 1.3576 | 84.0 | 126000 | 2.0544 | | 1.348 | 85.0 | 127500 | 2.0412 | | 1.3387 | 86.0 | 129000 | 2.0459 | | 1.3416 | 87.0 | 130500 | 2.0329 | | 1.3421 | 88.0 | 132000 | 2.0274 | | 1.3266 | 89.0 | 133500 | 2.0233 | | 1.3183 | 90.0 | 135000 | 2.0319 | | 1.322 | 91.0 | 136500 | 2.0080 | | 1.32 | 92.0 | 138000 | 2.0472 | | 1.304 | 93.0 | 139500 | 2.0538 | | 1.3061 | 94.0 | 141000 | 2.0340 | | 1.3199 | 95.0 | 142500 | 2.0456 | | 1.2985 | 96.0 | 144000 | 2.0167 | | 1.3021 | 97.0 | 145500 | 2.0204 | | 1.2787 | 98.0 | 147000 | 2.0645 | | 1.2879 | 99.0 | 148500 | 2.0345 | | 1.2695 | 100.0 | 150000 | 2.0340 | | 1.2884 | 101.0 | 151500 | 2.0602 | | 1.2747 | 102.0 | 153000 | 2.0667 | | 1.2607 | 103.0 | 154500 | 2.0551 | | 1.2551 | 104.0 | 156000 | 2.0544 | | 1.2557 | 105.0 | 157500 | 2.0553 | | 1.2495 | 106.0 | 159000 | 2.0370 | | 1.26 | 107.0 | 160500 | 2.0568 | | 1.2499 | 108.0 | 162000 | 2.0427 | | 1.2438 | 109.0 | 163500 | 2.0184 | | 1.2496 | 110.0 | 165000 | 2.0227 | | 1.2332 | 111.0 | 166500 | 2.0621 | | 1.2231 | 112.0 | 168000 | 2.0661 | | 1.211 | 113.0 | 169500 | 2.0673 | | 1.217 | 114.0 | 171000 | 2.0544 | | 1.2206 | 115.0 | 172500 | 2.0542 | | 1.2083 | 116.0 | 174000 | 2.0592 | | 1.2205 | 117.0 | 175500 | 2.0451 | | 1.2065 | 118.0 | 177000 | 2.0402 | | 1.1988 | 119.0 | 178500 | 2.0615 | | 1.218 | 120.0 | 180000 | 2.0374 | | 1.1917 | 121.0 | 181500 | 2.0349 | | 1.1854 | 122.0 | 183000 | 2.0790 | | 1.1819 | 123.0 | 184500 | 2.0766 | | 1.2029 | 124.0 | 186000 | 2.0364 | | 1.1851 | 125.0 | 187500 | 2.0568 | | 1.1734 | 126.0 | 189000 | 2.0445 | | 1.1701 | 127.0 | 190500 | 2.0770 | | 1.1824 | 128.0 | 192000 | 2.0566 | | 1.1604 | 129.0 | 193500 | 2.0542 | | 1.1733 | 130.0 | 195000 | 2.0525 | | 1.1743 | 131.0 | 196500 | 2.0577 | | 1.1692 | 132.0 | 198000 | 2.0723 | | 1.1519 | 133.0 | 199500 | 2.0567 | | 1.1401 | 134.0 | 201000 | 2.0795 | | 1.1692 | 135.0 | 202500 | 2.0625 | | 1.157 | 136.0 | 204000 | 2.0793 | | 1.1495 | 137.0 | 205500 | 2.0782 | | 1.1479 | 138.0 | 207000 | 2.0392 | | 1.1247 | 139.0 | 208500 | 2.0796 | | 1.143 | 140.0 | 210000 | 2.0369 | | 1.1324 | 141.0 | 211500 | 2.0699 | | 1.1341 | 142.0 | 213000 | 2.0694 | | 1.1317 | 143.0 | 214500 | 2.0569 | | 1.1254 | 144.0 | 216000 | 2.0545 | | 1.1156 | 145.0 | 217500 | 2.0708 | | 1.1353 | 146.0 | 219000 | 2.0767 | | 1.1312 | 147.0 | 220500 | 2.0523 | | 1.1224 | 148.0 | 222000 | 2.0565 | | 1.106 | 149.0 | 223500 | 2.0696 | | 1.1069 | 150.0 | 225000 | 2.0478 | | 1.1011 | 151.0 | 226500 | 2.0475 | | 1.0985 | 152.0 | 228000 | 2.0888 | | 1.1107 | 153.0 | 229500 | 2.0756 | | 1.1058 | 154.0 | 231000 | 2.0812 | | 1.1027 | 155.0 | 232500 | 2.0597 | | 1.0996 | 156.0 | 234000 | 2.0684 | | 1.0987 | 157.0 | 235500 | 2.0629 | | 1.0881 | 158.0 | 237000 | 2.0701 | | 1.1143 | 159.0 | 238500 | 2.0740 | | 1.0823 | 160.0 | 240000 | 2.0869 | | 1.0925 | 161.0 | 241500 | 2.0567 | | 1.1034 | 162.0 | 243000 | 2.0833 | | 1.0759 | 163.0 | 244500 | 2.0585 | | 1.0998 | 164.0 | 246000 | 2.0293 | | 1.0891 | 165.0 | 247500 | 2.0608 | | 1.1036 | 166.0 | 249000 | 2.0831 | | 1.076 | 167.0 | 250500 | 2.0979 | | 1.0895 | 168.0 | 252000 | 2.0882 | | 1.0825 | 169.0 | 253500 | 2.0742 | | 1.0793 | 170.0 | 255000 | 2.0841 | | 1.079 | 171.0 | 256500 | 2.0829 | | 1.0653 | 172.0 | 258000 | 2.0888 | | 1.0834 | 173.0 | 259500 | 2.0784 | | 1.0721 | 174.0 | 261000 | 2.0859 | | 1.0712 | 175.0 | 262500 | 2.0810 | | 1.0494 | 176.0 | 264000 | 2.0605 | | 1.0654 | 177.0 | 265500 | 2.0623 | | 1.077 | 178.0 | 267000 | 2.0756 | | 1.056 | 179.0 | 268500 | 2.0782 | | 1.0523 | 180.0 | 270000 | 2.0966 | | 1.0656 | 181.0 | 271500 | 2.0750 | | 1.0636 | 182.0 | 273000 | 2.0769 | | 1.0851 | 183.0 | 274500 | 2.0872 | | 1.0562 | 184.0 | 276000 | 2.0893 | | 1.0534 | 185.0 | 277500 | 2.0661 | | 1.0514 | 186.0 | 279000 | 2.0712 | | 1.062 | 187.0 | 280500 | 2.0769 | | 1.0683 | 188.0 | 282000 | 2.0765 | | 1.0606 | 189.0 | 283500 | 2.0735 | | 1.0555 | 190.0 | 285000 | 2.0710 | | 1.0568 | 191.0 | 286500 | 2.0860 | | 1.0502 | 192.0 | 288000 | 2.0587 | | 1.0437 | 193.0 | 289500 | 2.0998 | | 1.0534 | 194.0 | 291000 | 2.0418 | | 1.062 | 195.0 | 292500 | 2.0724 | | 1.0457 | 196.0 | 294000 | 2.0612 | | 1.0501 | 197.0 | 295500 | 2.1012 | | 1.0728 | 198.0 | 297000 | 2.0721 | | 1.0413 | 199.0 | 298500 | 2.0535 | | 1.0461 | 200.0 | 300000 | 2.0531 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

Izarel/distilbert-base-uncased_fine_tuned_body_text

Izarel

2022-08-01T03:52:20Z

4

0

transformers

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

text-classification

2022-07-31T19:03:36Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - recall - precision - f1 model-index: - name: distilbert-base-uncased_fine_tuned_body_text results: [] ---  # distilbert-base-uncased_fine_tuned_body_text 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: - Loss: 1.2153 - Accuracy: {'accuracy': 0.8827265261428963} - Recall: {'recall': 0.8641975308641975} - Precision: {'precision': 0.8900034993584509} - F1: {'f1': 0.8769106999195494} ## Model description More information needed ## 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------------------------------:|:------------------------------:|:---------------------------------:|:--------------------------:| | 0.3056 | 1.0 | 2284 | 0.3040 | {'accuracy': 0.8874897344648235} | {'recall': 0.8466417487824216} | {'precision': 0.914261252446184} | {'f1': 0.8791531902381653} | | 0.2279 | 2.0 | 4568 | 0.2891 | {'accuracy': 0.8908294552422666} | {'recall': 0.8606863744478424} | {'precision': 0.9086452230060983} | {'f1': 0.8840158213122382} | | 0.1467 | 3.0 | 6852 | 0.3580 | {'accuracy': 0.8882562277580072} | {'recall': 0.8452825914599615} | {'precision': 0.9170557876628164} | {'f1': 0.8797076678257796} | | 0.0921 | 4.0 | 9136 | 0.4560 | {'accuracy': 0.8754448398576512} | {'recall': 0.8948918337297542} | {'precision': 0.8543468858131488} | {'f1': 0.8741494717043756} | | 0.0587 | 5.0 | 11420 | 0.5701 | {'accuracy': 0.8768135778811935} | {'recall': 0.8139087099331748} | {'precision': 0.9221095855254716} | {'f1': 0.8646372277704246} | | 0.0448 | 6.0 | 13704 | 0.6738 | {'accuracy': 0.8767040788393101} | {'recall': 0.8794880507418734} | {'precision': 0.8673070479168994} | {'f1': 0.873355078168935} | | 0.0289 | 7.0 | 15988 | 0.7965 | {'accuracy': 0.8798248015329866} | {'recall': 0.8491335372069317} | {'precision': 0.8967703349282297} | {'f1': 0.8723020536389552} | | 0.0214 | 8.0 | 18272 | 0.8244 | {'accuracy': 0.8811387900355871} | {'recall': 0.8576282704723072} | {'precision': 0.8922931887815225} | {'f1': 0.8746173837712965} | | 0.0147 | 9.0 | 20556 | 0.8740 | {'accuracy': 0.8806460443471119} | {'recall': 0.8669158455091177} | {'precision': 0.8839357893521191} | {'f1': 0.8753430924062213} | | 0.0099 | 10.0 | 22840 | 0.9716 | {'accuracy': 0.8788940596769779} | {'recall': 0.8694076339336279} | {'precision': 0.8787635947338294} | {'f1': 0.8740605784559327} | | 0.0092 | 11.0 | 25124 | 1.0296 | {'accuracy': 0.8822885299753627} | {'recall': 0.8669158455091177} | {'precision': 0.8870089233978444} | {'f1': 0.876847290640394} | | 0.0039 | 12.0 | 27408 | 1.0974 | {'accuracy': 0.8787845606350945} | {'recall': 0.8628383735417374} | {'precision': 0.8836561883772184} | {'f1': 0.8731232091690544} | | 0.0053 | 13.0 | 29692 | 1.0833 | {'accuracy': 0.8799890500958116} | {'recall': 0.8503794314191868} | {'precision': 0.8960496479293472} | {'f1': 0.8726173872617387} | | 0.0032 | 14.0 | 31976 | 1.1731 | {'accuracy': 0.8813030385984123} | {'recall': 0.8705402650356778} | {'precision': 0.8823326828148318} | {'f1': 0.8763968072976055} | | 0.0017 | 15.0 | 34260 | 1.2153 | {'accuracy': 0.8827265261428963} | {'recall': 0.8641975308641975} | {'precision': 0.8900034993584509} | {'f1': 0.8769106999195494} | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

reachrkr/Cartpole-v1

reachrkr

2022-08-01T02:16:58Z

0

null

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

reinforcement-learning

2022-08-01T02:16:50Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Cartpole-v1 results: - metrics: - type: mean_reward value: 40.00 +/- 18.57 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 --- # **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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5

notmaineyy/bert-base-multilingual-cased-finetuned-ner

notmaineyy

2022-08-01T01:37:57Z

5

0

transformers

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

token-classification

2022-07-21T01:33:49Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: notmaineyy/bert-base-multilingual-cased-finetuned-ner results: [] ---  # notmaineyy/bert-base-multilingual-cased-finetuned-ner This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0248 - Validation Loss: 0.0568 - Train Precision: 0.9424 - Train Recall: 0.9471 - Train F1: 0.9448 - Train Accuracy: 0.9863 - 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 10530, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.1335 | 0.0705 | 0.9152 | 0.9204 | 0.9178 | 0.9806 | 0 | | 0.0497 | 0.0562 | 0.9335 | 0.9472 | 0.9403 | 0.9851 | 1 | | 0.0248 | 0.0568 | 0.9424 | 0.9471 | 0.9448 | 0.9863 | 2 | ### Framework versions - Transformers 4.21.0 - TensorFlow 2.8.2 - Datasets 2.4.0 - Tokenizers 0.12.1

RedPandaAINLP/opus-mt-en-ro-finetuned-en-to-ro

RedPandaAINLP

2022-08-01T00:11:22Z

15

0

transformers

[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "generated_from_trainer", "dataset:wmt16", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-07-31T22:39:13Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: opus-mt-en-ro-finetuned-en-to-ro results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16 type: wmt16 config: ro-en split: train args: ro-en metrics: - name: Bleu type: bleu value: 28.1505 ---  # opus-mt-en-ro-finetuned-en-to-ro This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.2886 - Bleu: 28.1505 - Gen Len: 34.1036 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.7437 | 1.0 | 38145 | 1.2886 | 28.1505 | 34.1036 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

elopezlopez/distilbert-base-uncased_fold_4_ternary

elopezlopez

2022-08-01T00:10:08Z

7

0

transformers

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

text-classification

2022-07-31T23:52:47Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_4_ternary results: [] ---  # distilbert-base-uncased_fold_4_ternary This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2981 - F1: 0.7565 ## Model description More information needed ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.5588 | 0.6984 | | 0.5547 | 2.0 | 578 | 0.5283 | 0.7336 | | 0.5547 | 3.0 | 867 | 0.7038 | 0.7202 | | 0.2479 | 4.0 | 1156 | 0.8949 | 0.7284 | | 0.2479 | 5.0 | 1445 | 0.9959 | 0.7286 | | 0.1181 | 6.0 | 1734 | 1.0663 | 0.7311 | | 0.0508 | 7.0 | 2023 | 1.2377 | 0.7054 | | 0.0508 | 8.0 | 2312 | 1.2981 | 0.7565 | | 0.0185 | 9.0 | 2601 | 1.3532 | 0.7407 | | 0.0185 | 10.0 | 2890 | 1.5365 | 0.7333 | | 0.0103 | 11.0 | 3179 | 1.5184 | 0.7423 | | 0.0103 | 12.0 | 3468 | 1.6009 | 0.7420 | | 0.0123 | 13.0 | 3757 | 1.6395 | 0.7402 | | 0.008 | 14.0 | 4046 | 1.6838 | 0.7429 | | 0.008 | 15.0 | 4335 | 1.6176 | 0.7490 | | 0.0012 | 16.0 | 4624 | 1.7873 | 0.7345 | | 0.0012 | 17.0 | 4913 | 1.6761 | 0.7412 | | 0.0044 | 18.0 | 5202 | 1.7356 | 0.7417 | | 0.0044 | 19.0 | 5491 | 1.7686 | 0.7502 | | 0.0045 | 20.0 | 5780 | 1.7668 | 0.7406 | | 0.0017 | 21.0 | 6069 | 1.8411 | 0.7381 | | 0.0017 | 22.0 | 6358 | 1.8147 | 0.7469 | | 0.0012 | 23.0 | 6647 | 1.8028 | 0.7489 | | 0.0012 | 24.0 | 6936 | 1.8147 | 0.7453 | | 0.0026 | 25.0 | 7225 | 1.8257 | 0.7475 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

keithanpai/tiny-random-vit-finetuned-eurosat

keithanpai

2022-08-01T00:08:25Z

73

0

transformers

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

image-classification

2022-08-01T00:06:15Z

--- tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: tiny-random-vit-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.6646706586826348 ---  # tiny-random-vit-finetuned-eurosat This model is a fine-tuned version of [hf-internal-testing/tiny-random-vit](https://huggingface.co/hf-internal-testing/tiny-random-vit) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0488 - Accuracy: 0.6647 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1192 | 0.99 | 70 | 1.0867 | 0.6627 | | 1.067 | 1.99 | 140 | 1.0563 | 0.6657 | | 0.9719 | 2.99 | 210 | 1.0488 | 0.6647 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

elopezlopez/xlnet-base-cased_fold_3_binary

elopezlopez

2022-07-31T23:37:52Z

6

0

transformers

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

text-classification

2022-07-31T23:14:01Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlnet-base-cased_fold_3_binary results: [] ---  # xlnet-base-cased_fold_3_binary This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3616 - F1: 0.7758 ## Model description More information needed ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.4668 | 0.7666 | | 0.4142 | 2.0 | 578 | 0.4259 | 0.7631 | | 0.4142 | 3.0 | 867 | 0.6744 | 0.7492 | | 0.235 | 4.0 | 1156 | 0.8879 | 0.7678 | | 0.235 | 5.0 | 1445 | 1.0036 | 0.7639 | | 0.1297 | 6.0 | 1734 | 1.1427 | 0.7616 | | 0.0894 | 7.0 | 2023 | 1.2126 | 0.7626 | | 0.0894 | 8.0 | 2312 | 1.5098 | 0.7433 | | 0.0473 | 9.0 | 2601 | 1.3616 | 0.7758 | | 0.0473 | 10.0 | 2890 | 1.5966 | 0.7579 | | 0.0325 | 11.0 | 3179 | 1.6669 | 0.7508 | | 0.0325 | 12.0 | 3468 | 1.7401 | 0.7437 | | 0.0227 | 13.0 | 3757 | 1.7797 | 0.7515 | | 0.0224 | 14.0 | 4046 | 1.7349 | 0.7418 | | 0.0224 | 15.0 | 4335 | 1.7527 | 0.7595 | | 0.0152 | 16.0 | 4624 | 1.7492 | 0.7634 | | 0.0152 | 17.0 | 4913 | 1.8178 | 0.7628 | | 0.0117 | 18.0 | 5202 | 1.7736 | 0.7688 | | 0.0117 | 19.0 | 5491 | 1.8449 | 0.7704 | | 0.0055 | 20.0 | 5780 | 1.8687 | 0.7652 | | 0.0065 | 21.0 | 6069 | 1.8083 | 0.7669 | | 0.0065 | 22.0 | 6358 | 1.8568 | 0.7559 | | 0.0054 | 23.0 | 6647 | 1.8760 | 0.7678 | | 0.0054 | 24.0 | 6936 | 1.8948 | 0.7697 | | 0.0048 | 25.0 | 7225 | 1.9109 | 0.7680 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

elopezlopez/distilbert-base-uncased_fold_2_ternary

elopezlopez

2022-07-31T23:35:04Z

5

0

transformers

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

text-classification

2022-07-31T23:17:46Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_2_ternary results: [] ---  # distilbert-base-uncased_fold_2_ternary This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5810 - F1: 0.7620 ## Model description More information needed ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 294 | 0.5886 | 0.7239 | | 0.557 | 2.0 | 588 | 0.5085 | 0.7524 | | 0.557 | 3.0 | 882 | 0.6332 | 0.7530 | | 0.2456 | 4.0 | 1176 | 0.8749 | 0.7161 | | 0.2456 | 5.0 | 1470 | 1.0601 | 0.7371 | | 0.1112 | 6.0 | 1764 | 1.1885 | 0.7451 | | 0.0484 | 7.0 | 2058 | 1.3027 | 0.7240 | | 0.0484 | 8.0 | 2352 | 1.4647 | 0.7259 | | 0.0259 | 9.0 | 2646 | 1.4476 | 0.7322 | | 0.0259 | 10.0 | 2940 | 1.4826 | 0.7388 | | 0.0164 | 11.0 | 3234 | 1.5869 | 0.7333 | | 0.0109 | 12.0 | 3528 | 1.5954 | 0.7539 | | 0.0109 | 13.0 | 3822 | 1.5810 | 0.7620 | | 0.0082 | 14.0 | 4116 | 1.7165 | 0.7335 | | 0.0082 | 15.0 | 4410 | 1.8152 | 0.7414 | | 0.004 | 16.0 | 4704 | 1.7411 | 0.7474 | | 0.004 | 17.0 | 4998 | 1.8692 | 0.7355 | | 0.0034 | 18.0 | 5292 | 1.8727 | 0.7303 | | 0.0009 | 19.0 | 5586 | 1.9813 | 0.7305 | | 0.0009 | 20.0 | 5880 | 1.9764 | 0.7391 | | 0.0012 | 21.0 | 6174 | 2.0170 | 0.7291 | | 0.0012 | 22.0 | 6468 | 2.0240 | 0.7391 | | 0.0004 | 23.0 | 6762 | 2.0311 | 0.7352 | | 0.0014 | 24.0 | 7056 | 2.0174 | 0.7334 | | 0.0014 | 25.0 | 7350 | 2.0282 | 0.7381 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

keithanpai/vit-base-patch32-384-finetuned-eurosat

keithanpai

2022-07-31T22:51:54Z

54

0

transformers

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

image-classification

2022-07-31T19:46:59Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch32-384-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8423153692614771 ---  # vit-base-patch32-384-finetuned-eurosat This model is a fine-tuned version of [google/vit-base-patch32-384](https://huggingface.co/google/vit-base-patch32-384) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4381 - Accuracy: 0.8423 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.607 | 0.99 | 70 | 0.5609 | 0.8014 | | 0.5047 | 1.99 | 140 | 0.4634 | 0.8373 | | 0.4089 | 2.99 | 210 | 0.4381 | 0.8423 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

wenkai-li/distilroberta-base-finetuned-wikitextepoch_150

wenkai-li

2022-07-31T22:09:24Z

5

0

transformers

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

fill-mask

2022-07-31T18:31:03Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-wikitextepoch_150 results: [] ---  # distilroberta-base-finetuned-wikitextepoch_150 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8929 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 2.2428 | 1.0 | 1121 | 2.0500 | | 2.1209 | 2.0 | 2242 | 1.9996 | | 2.0665 | 3.0 | 3363 | 1.9501 | | 2.0179 | 4.0 | 4484 | 1.9311 | | 1.9759 | 5.0 | 5605 | 1.9255 | | 1.9089 | 6.0 | 6726 | 1.8805 | | 1.9143 | 7.0 | 7847 | 1.8715 | | 1.8744 | 8.0 | 8968 | 1.8671 | | 1.858 | 9.0 | 10089 | 1.8592 | | 1.8141 | 10.0 | 11210 | 1.8578 | | 1.7917 | 11.0 | 12331 | 1.8574 | | 1.7752 | 12.0 | 13452 | 1.8423 | | 1.7722 | 13.0 | 14573 | 1.8287 | | 1.7354 | 14.0 | 15694 | 1.8396 | | 1.7217 | 15.0 | 16815 | 1.8244 | | 1.6968 | 16.0 | 17936 | 1.8278 | | 1.659 | 17.0 | 19057 | 1.8412 | | 1.6442 | 18.0 | 20178 | 1.8328 | | 1.6441 | 19.0 | 21299 | 1.8460 | | 1.6267 | 20.0 | 22420 | 1.8343 | | 1.612 | 21.0 | 23541 | 1.8249 | | 1.5963 | 22.0 | 24662 | 1.8253 | | 1.6101 | 23.0 | 25783 | 1.7843 | | 1.5747 | 24.0 | 26904 | 1.8047 | | 1.5559 | 25.0 | 28025 | 1.8618 | | 1.5484 | 26.0 | 29146 | 1.8660 | | 1.5411 | 27.0 | 30267 | 1.8318 | | 1.5247 | 28.0 | 31388 | 1.8216 | | 1.5278 | 29.0 | 32509 | 1.8075 | | 1.4954 | 30.0 | 33630 | 1.8073 | | 1.4863 | 31.0 | 34751 | 1.7958 | | 1.4821 | 32.0 | 35872 | 1.8080 | | 1.4357 | 33.0 | 36993 | 1.8373 | | 1.4602 | 34.0 | 38114 | 1.8199 | | 1.447 | 35.0 | 39235 | 1.8325 | | 1.4292 | 36.0 | 40356 | 1.8075 | | 1.4174 | 37.0 | 41477 | 1.8168 | | 1.4103 | 38.0 | 42598 | 1.8095 | | 1.4168 | 39.0 | 43719 | 1.8233 | | 1.4005 | 40.0 | 44840 | 1.8388 | | 1.3799 | 41.0 | 45961 | 1.8235 | | 1.3657 | 42.0 | 47082 | 1.8298 | | 1.3559 | 43.0 | 48203 | 1.8165 | | 1.3723 | 44.0 | 49324 | 1.8059 | | 1.3535 | 45.0 | 50445 | 1.8451 | | 1.3533 | 46.0 | 51566 | 1.8458 | | 1.3469 | 47.0 | 52687 | 1.8237 | | 1.3247 | 48.0 | 53808 | 1.8264 | | 1.3142 | 49.0 | 54929 | 1.8209 | | 1.2958 | 50.0 | 56050 | 1.8244 | | 1.293 | 51.0 | 57171 | 1.8311 | | 1.2784 | 52.0 | 58292 | 1.8287 | | 1.2731 | 53.0 | 59413 | 1.8600 | | 1.2961 | 54.0 | 60534 | 1.8086 | | 1.2739 | 55.0 | 61655 | 1.8303 | | 1.2716 | 56.0 | 62776 | 1.8214 | | 1.2459 | 57.0 | 63897 | 1.8440 | | 1.2492 | 58.0 | 65018 | 1.8503 | | 1.2393 | 59.0 | 66139 | 1.8316 | | 1.2077 | 60.0 | 67260 | 1.8283 | | 1.2426 | 61.0 | 68381 | 1.8413 | | 1.2032 | 62.0 | 69502 | 1.8461 | | 1.2123 | 63.0 | 70623 | 1.8469 | | 1.2069 | 64.0 | 71744 | 1.8478 | | 1.198 | 65.0 | 72865 | 1.8479 | | 1.1972 | 66.0 | 73986 | 1.8516 | | 1.1885 | 67.0 | 75107 | 1.8341 | | 1.1784 | 68.0 | 76228 | 1.8322 | | 1.1866 | 69.0 | 77349 | 1.8559 | | 1.1648 | 70.0 | 78470 | 1.8758 | | 1.1595 | 71.0 | 79591 | 1.8684 | | 1.1661 | 72.0 | 80712 | 1.8553 | | 1.1478 | 73.0 | 81833 | 1.8658 | | 1.1488 | 74.0 | 82954 | 1.8452 | | 1.1538 | 75.0 | 84075 | 1.8505 | | 1.1267 | 76.0 | 85196 | 1.8430 | | 1.1339 | 77.0 | 86317 | 1.8333 | | 1.118 | 78.0 | 87438 | 1.8419 | | 1.12 | 79.0 | 88559 | 1.8669 | | 1.1144 | 80.0 | 89680 | 1.8647 | | 1.104 | 81.0 | 90801 | 1.8643 | | 1.0864 | 82.0 | 91922 | 1.8528 | | 1.0863 | 83.0 | 93043 | 1.8456 | | 1.0912 | 84.0 | 94164 | 1.8509 | | 1.0873 | 85.0 | 95285 | 1.8690 | | 1.0862 | 86.0 | 96406 | 1.8577 | | 1.0879 | 87.0 | 97527 | 1.8612 | | 1.0783 | 88.0 | 98648 | 1.8410 | | 1.0618 | 89.0 | 99769 | 1.8517 | | 1.0552 | 90.0 | 100890 | 1.8459 | | 1.0516 | 91.0 | 102011 | 1.8723 | | 1.0424 | 92.0 | 103132 | 1.8832 | | 1.0478 | 93.0 | 104253 | 1.8922 | | 1.0523 | 94.0 | 105374 | 1.8753 | | 1.027 | 95.0 | 106495 | 1.8625 | | 1.0364 | 96.0 | 107616 | 1.8673 | | 1.0203 | 97.0 | 108737 | 1.8806 | | 1.0309 | 98.0 | 109858 | 1.8644 | | 1.0174 | 99.0 | 110979 | 1.8659 | | 1.0184 | 100.0 | 112100 | 1.8590 | | 1.0234 | 101.0 | 113221 | 1.8614 | | 1.013 | 102.0 | 114342 | 1.8866 | | 1.0092 | 103.0 | 115463 | 1.8770 | | 1.0051 | 104.0 | 116584 | 1.8445 | | 1.0105 | 105.0 | 117705 | 1.8512 | | 1.0233 | 106.0 | 118826 | 1.8896 | | 0.9967 | 107.0 | 119947 | 1.8687 | | 0.9795 | 108.0 | 121068 | 1.8618 | | 0.9846 | 109.0 | 122189 | 1.8877 | | 0.9958 | 110.0 | 123310 | 1.8522 | | 0.9689 | 111.0 | 124431 | 1.8765 | | 0.9879 | 112.0 | 125552 | 1.8692 | | 0.99 | 113.0 | 126673 | 1.8689 | | 0.9798 | 114.0 | 127794 | 1.8898 | | 0.9676 | 115.0 | 128915 | 1.8782 | | 0.9759 | 116.0 | 130036 | 1.8840 | | 0.9576 | 117.0 | 131157 | 1.8662 | | 0.9637 | 118.0 | 132278 | 1.8984 | | 0.9645 | 119.0 | 133399 | 1.8872 | | 0.9793 | 120.0 | 134520 | 1.8705 | | 0.9643 | 121.0 | 135641 | 1.9036 | | 0.961 | 122.0 | 136762 | 1.8683 | | 0.9496 | 123.0 | 137883 | 1.8785 | | 0.946 | 124.0 | 139004 | 1.8912 | | 0.9681 | 125.0 | 140125 | 1.8837 | | 0.9403 | 126.0 | 141246 | 1.8824 | | 0.9452 | 127.0 | 142367 | 1.8824 | | 0.9437 | 128.0 | 143488 | 1.8665 | | 0.945 | 129.0 | 144609 | 1.8655 | | 0.9453 | 130.0 | 145730 | 1.8695 | | 0.9238 | 131.0 | 146851 | 1.8697 | | 0.9176 | 132.0 | 147972 | 1.8618 | | 0.9405 | 133.0 | 149093 | 1.8679 | | 0.9184 | 134.0 | 150214 | 1.9025 | | 0.9298 | 135.0 | 151335 | 1.9045 | | 0.9215 | 136.0 | 152456 | 1.9014 | | 0.9249 | 137.0 | 153577 | 1.8505 | | 0.9246 | 138.0 | 154698 | 1.8542 | | 0.9205 | 139.0 | 155819 | 1.8731 | | 0.9368 | 140.0 | 156940 | 1.8673 | | 0.9251 | 141.0 | 158061 | 1.8835 | | 0.9224 | 142.0 | 159182 | 1.8727 | | 0.9326 | 143.0 | 160303 | 1.8380 | | 0.916 | 144.0 | 161424 | 1.8857 | | 0.9361 | 145.0 | 162545 | 1.8547 | | 0.9121 | 146.0 | 163666 | 1.8587 | | 0.9156 | 147.0 | 164787 | 1.8863 | | 0.9131 | 148.0 | 165908 | 1.8809 | | 0.9185 | 149.0 | 167029 | 1.8734 | | 0.9183 | 150.0 | 168150 | 1.8929 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.5.0 - Datasets 2.4.0 - Tokenizers 0.12.1

elopezlopez/distilbert-base-uncased_fold_4_binary

elopezlopez

2022-07-31T22:04:12Z

4

0

transformers

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

text-classification

2022-07-31T21:54:02Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased_fold_4_binary results: [] ---  # distilbert-base-uncased_fold_4_binary This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2977 - F1: 0.8083 ## Model description More information needed ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.3701 | 0.7903 | | 0.4005 | 2.0 | 578 | 0.3669 | 0.7994 | | 0.4005 | 3.0 | 867 | 0.5038 | 0.7955 | | 0.1945 | 4.0 | 1156 | 0.6353 | 0.8006 | | 0.1945 | 5.0 | 1445 | 0.8974 | 0.7826 | | 0.0909 | 6.0 | 1734 | 0.8533 | 0.7764 | | 0.0389 | 7.0 | 2023 | 0.9969 | 0.7957 | | 0.0389 | 8.0 | 2312 | 1.0356 | 0.7952 | | 0.0231 | 9.0 | 2601 | 1.1538 | 0.7963 | | 0.0231 | 10.0 | 2890 | 1.2011 | 0.7968 | | 0.0051 | 11.0 | 3179 | 1.2329 | 0.7935 | | 0.0051 | 12.0 | 3468 | 1.2829 | 0.8056 | | 0.0066 | 13.0 | 3757 | 1.2946 | 0.7956 | | 0.004 | 14.0 | 4046 | 1.2977 | 0.8083 | | 0.004 | 15.0 | 4335 | 1.3970 | 0.7957 | | 0.0007 | 16.0 | 4624 | 1.3361 | 0.7917 | | 0.0007 | 17.0 | 4913 | 1.5782 | 0.7954 | | 0.0107 | 18.0 | 5202 | 1.4641 | 0.7900 | | 0.0107 | 19.0 | 5491 | 1.4490 | 0.7957 | | 0.0058 | 20.0 | 5780 | 1.4607 | 0.7932 | | 0.0016 | 21.0 | 6069 | 1.5048 | 0.7939 | | 0.0016 | 22.0 | 6358 | 1.5219 | 0.7945 | | 0.0027 | 23.0 | 6647 | 1.4783 | 0.7937 | | 0.0027 | 24.0 | 6936 | 1.4715 | 0.7981 | | 0.0004 | 25.0 | 7225 | 1.4989 | 0.7900 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

oMateos2020/pegasus-newsroom-cnn_full-adam8bit

oMateos2020

2022-07-31T21:02:17Z

10

0

transformers

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

text2text-generation

2022-07-29T07:55:23Z

--- tags: - generated_from_trainer model-index: - name: pegasus-newsroom-cnn_full-adam8bit results: [] ---  # pegasus-newsroom-cnn_full-adam8bit This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-newsroom) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 2.9826 - eval_rouge1: 38.2456 - eval_rouge2: 17.3966 - eval_rougeL: 26.9273 - eval_rougeLsum: 35.3265 - eval_gen_len: 69.658 - eval_runtime: 13626.7467 - eval_samples_per_second: 0.183 - eval_steps_per_second: 0.012 - epoch: 0.22 - step: 250 ## Model description More information needed ## 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: 0.0016 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.1 ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

DS-20202/DoubleHardDebias

DS-20202

2022-07-31T20:32:45Z

0

null

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

null

2022-07-31T12:08:09Z

--- title: Double Hard Debiasing emoji: 👁 colorFrom: blue colorTo: pink sdk: gradio sdk_version: 3.1.1 app_file: app.py pinned: false license: mit --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

neuralmagic/oBERT-teacher-squadv1

neuralmagic

2022-07-31T19:52:34Z

396

0

transformers

[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]

null

2022-05-25T13:47:26Z

--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # SQuADv1 teacher This model is used as a teacher for all runs on the SQuADv1 downstream task in the paper [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). SQuADv1 dev-set: ``` EM = 81.41 F1 = 88.54 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```

neuralmagic/oBERT-teacher-qqp

neuralmagic

2022-07-31T19:52:34Z

8

0

transformers

[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:qqp", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]

null

2022-05-25T13:55:22Z

--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: qqp --- # QQP teacher This model is used as a teacher for all runs on the QQP downstream task in the paper [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). QQP dev-set: ``` accuracy = 91.06 F1 = 88.00 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```

neuralmagic/oBERT-6-downstream-pruned-block4-90-squadv1

neuralmagic

2022-07-31T19:52:34Z

2

0

transformers

[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]

null

2022-05-25T14:00:31Z

--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-6-downstream-pruned-block4-90-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 6 Layers - Sparsity 90% - 4-block`. ``` Pruning method: oBERT downstream block-4 Paper: https://arxiv.org/abs/2203.07259 Dataset: SQuADv1 Sparsity: 90% Number of layers: 6 ``` The dev-set performance of this model: ``` EM = 77.65 F1 = 85.34 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```

neuralmagic/oBERT-6-downstream-pruned-block4-80-squadv1

neuralmagic

2022-07-31T19:52:34Z

6

0

transformers

[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]

null

2022-05-25T14:00:18Z

--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-6-downstream-pruned-block4-80-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 6 Layers - Sparsity 80% - 4-block`. ``` Pruning method: oBERT downstream block-4 Paper: https://arxiv.org/abs/2203.07259 Dataset: SQuADv1 Sparsity: 80% Number of layers: 6 ``` The dev-set performance of this model: ``` EM = 79.55 F1 = 87.00 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```

neuralmagic/oBERT-6-downstream-pruned-block4-80-QAT-squadv1

neuralmagic

2022-07-31T19:52:34Z

8

0

transformers

[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]

null

2022-05-25T19:20:49Z

--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-6-downstream-pruned-block4-80-QAT-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 6 Layers - Sparsity 80% - 4-block + QAT`. ``` Pruning method: oBERT downstream block-4 + QAT Paper: https://arxiv.org/abs/2203.07259 Dataset: SQuADv1 Sparsity: 80% Number of layers: 6 ``` The dev-set performance of this model: ``` EM = 78.28 F1 = 86.10 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```

neuralmagic/oBERT-3-downstream-pruned-block4-80-squadv1

neuralmagic

2022-07-31T19:52:33Z

6

0

transformers

[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]

null

2022-05-25T14:01:27Z

--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-3-downstream-pruned-block4-80-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 80% - 4-block`. ``` Pruning method: oBERT downstream block-4 Paper: https://arxiv.org/abs/2203.07259 Dataset: SQuADv1 Sparsity: 80% Number of layers: 3 ``` The dev-set performance of this model: ``` EM = 74.07 F1 = 82.79 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```

neuralmagic/oBERT-6-downstream-dense-QAT-squadv1

neuralmagic

2022-07-31T19:52:33Z

2

0

transformers

[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]

null

2022-05-25T19:20:36Z

--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-6-downstream-dense-QAT-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 6 Layers - 0% Sparsity - QAT`, and it represents an upper bound for performance of the corresponding pruned and quantized models: - 80% unstructured QAT: `neuralmagic/oBERT-6-downstream-pruned-unstructured-80-QAT-squadv1` - 80% block-4 QAT: `neuralmagic/oBERT-6-downstream-pruned-block4-80-QAT-squadv1` - 90% unstructured QAT: `neuralmagic/oBERT-6-downstream-pruned-unstructured-90-QAT-squadv1` - 90% block-4 QAT: `neuralmagic/oBERT-6-downstream-pruned-block4-90-QAT-squadv1` SQuADv1 dev-set: ``` EM = 80.85 F1 = 87.94 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```

neuralmagic/oBERT-3-downstream-pruned-block4-90-QAT-squadv1

neuralmagic

2022-07-31T19:52:33Z

14

0

transformers

[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]

null

2022-05-25T19:21:41Z

--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-3-downstream-pruned-block4-90-QAT-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 90% - 4-block + QAT`. ``` Pruning method: oBERT downstream block-4 + QAT Paper: https://arxiv.org/abs/2203.07259 Dataset: SQuADv1 Sparsity: 90% Number of layers: 3 ``` The dev-set performance of this model: ``` EM = 70.00 F1 = 79.66 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```

neuralmagic/oBERT-3-downstream-pruned-block4-90-squadv1

neuralmagic

2022-07-31T19:52:33Z

5

0

transformers

[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]

null

2022-05-25T14:01:41Z

--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-3-downstream-pruned-block4-90-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 90% - 4-block`. ``` Pruning method: oBERT downstream block-4 Paper: https://arxiv.org/abs/2203.07259 Dataset: SQuADv1 Sparsity: 90% Number of layers: 3 ``` The dev-set performance of this model: ``` EM = 71.36 F1 = 80.69 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```

neuralmagic/oBERT-3-downstream-pruned-block4-80-QAT-squadv1

neuralmagic

2022-07-31T19:52:33Z

5

0

transformers

[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]

null

2022-05-25T19:21:28Z

--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-3-downstream-pruned-block4-80-QAT-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 80% - 4-block + QAT`. ``` Pruning method: oBERT downstream block-4 + QAT Paper: https://arxiv.org/abs/2203.07259 Dataset: SQuADv1 Sparsity: 80% Number of layers: 3 ``` The dev-set performance of this model: ``` EM = 72.70 F1 = 82.04 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```

neuralmagic/oBERT-3-downstream-pruned-unstructured-80-squadv1

neuralmagic

2022-07-31T19:52:33Z

6

0

transformers

[ "transformers", "pytorch", "bert", "oBERT", "sparsity", "pruning", "compression", "en", "dataset:squad", "arxiv:2203.07259", "endpoints_compatible", "region:us" ]

null

2022-05-25T14:01:00Z

--- tags: - bert - oBERT - sparsity - pruning - compression language: en datasets: squad --- # oBERT-3-downstream-pruned-unstructured-80-squadv1 This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 80% - unstructured`. ``` Pruning method: oBERT downstream unstructured Paper: https://arxiv.org/abs/2203.07259 Dataset: SQuADv1 Sparsity: 80% Number of layers: 3 ``` The dev-set performance of this model: ``` EM = 75.62 F1 = 84.08 ``` Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) If you find the model useful, please consider citing our work. ## Citation info ```bibtex @article{kurtic2022optimal, title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, journal={arXiv preprint arXiv:2203.07259}, year={2022} } ```