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ali2066/finetuned_sentence_itr2_3e-05_all_27_02_2022-18_35_02
204c99011b72a484b6c763dfac69df6b2bbc7ef7
2022-02-27T17:40:35.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
ali2066
null
ali2066/finetuned_sentence_itr2_3e-05_all_27_02_2022-18_35_02
5
null
transformers
16,900
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr2_3e-05_all_27_02_2022-18_35_02 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. --> # finetuned_sentence_itr2_3e-05_all_27_02_2022-18_35_02 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3962 - Accuracy: 0.8231 - F1: 0.8873 ## Model description More information needed ## Intended uses & 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: 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.3591 | 0.8366 | 0.8950 | | No log | 2.0 | 390 | 0.3558 | 0.8415 | 0.9012 | | 0.3647 | 3.0 | 585 | 0.4049 | 0.8427 | 0.8983 | | 0.3647 | 4.0 | 780 | 0.5030 | 0.8378 | 0.8949 | | 0.3647 | 5.0 | 975 | 0.5719 | 0.8354 | 0.8943 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-18_51_55
9fd3fdf08e332c8fae7a2f69331ca3bc11d43061
2022-02-27T17:54:05.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
ali2066
null
ali2066/finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-18_51_55
5
null
transformers
16,901
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-18_51_55 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. --> # finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-18_51_55 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6049 - Accuracy: 0.6926 - F1: 0.4160 ## Model description More information needed ## Intended uses & 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 48 | 0.5835 | 0.71 | 0.0333 | | No log | 2.0 | 96 | 0.5718 | 0.715 | 0.3871 | | No log | 3.0 | 144 | 0.5731 | 0.715 | 0.4 | | No log | 4.0 | 192 | 0.6009 | 0.705 | 0.3516 | | No log | 5.0 | 240 | 0.6122 | 0.7 | 0.4000 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_sentence_itr3_2e-05_webDiscourse_27_02_2022-18_59_05
f478430483ba43b56c06e875ae7956b32a5271ae
2022-02-27T18:01:35.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
ali2066
null
ali2066/finetuned_sentence_itr3_2e-05_webDiscourse_27_02_2022-18_59_05
5
null
transformers
16,902
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr3_2e-05_webDiscourse_27_02_2022-18_59_05 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. --> # finetuned_sentence_itr3_2e-05_webDiscourse_27_02_2022-18_59_05 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6049 - Accuracy: 0.6926 - F1: 0.4160 ## Model description More information needed ## Intended uses & 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 48 | 0.5835 | 0.71 | 0.0333 | | No log | 2.0 | 96 | 0.5718 | 0.715 | 0.3871 | | No log | 3.0 | 144 | 0.5731 | 0.715 | 0.4 | | No log | 4.0 | 192 | 0.6009 | 0.705 | 0.3516 | | No log | 5.0 | 240 | 0.6122 | 0.7 | 0.4000 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_sentence_itr0_2e-05_all_27_02_2022-19_05_42
639cf081932373c3bd34d89f43502dead4922187
2022-02-27T18:11:13.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
ali2066
null
ali2066/finetuned_sentence_itr0_2e-05_all_27_02_2022-19_05_42
5
null
transformers
16,903
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_2e-05_all_27_02_2022-19_05_42 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. --> # finetuned_sentence_itr0_2e-05_all_27_02_2022-19_05_42 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4917 - Accuracy: 0.8231 - F1: 0.8833 ## Model description More information needed ## Intended uses & 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.3883 | 0.8146 | 0.8833 | | No log | 2.0 | 390 | 0.3607 | 0.8390 | 0.8964 | | 0.4085 | 3.0 | 585 | 0.3812 | 0.8488 | 0.9042 | | 0.4085 | 4.0 | 780 | 0.3977 | 0.8549 | 0.9077 | | 0.4085 | 5.0 | 975 | 0.4233 | 0.8573 | 0.9092 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_sentence_itr0_0.0002_all_27_02_2022-19_11_17
71ea8de2a8d395696fb16f67baca4dd96efb88d7
2022-02-27T18:16:49.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
ali2066
null
ali2066/finetuned_sentence_itr0_0.0002_all_27_02_2022-19_11_17
5
null
transformers
16,904
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_0.0002_all_27_02_2022-19_11_17 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. --> # finetuned_sentence_itr0_0.0002_all_27_02_2022-19_11_17 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4064 - Accuracy: 0.8289 - F1: 0.8901 ## Model description More information needed ## Intended uses & 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: 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.4163 | 0.8085 | 0.8780 | | No log | 2.0 | 390 | 0.4098 | 0.8268 | 0.8878 | | 0.312 | 3.0 | 585 | 0.5892 | 0.8244 | 0.8861 | | 0.312 | 4.0 | 780 | 0.7580 | 0.8232 | 0.8845 | | 0.312 | 5.0 | 975 | 0.9028 | 0.8183 | 0.8824 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_sentence_itr0_2e-05_editorials_27_02_2022-19_38_42
c2bdac5868f90c4f7ff416e9f3a8273c754153b2
2022-02-27T18:42:31.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
ali2066
null
ali2066/finetuned_sentence_itr0_2e-05_editorials_27_02_2022-19_38_42
5
null
transformers
16,905
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_2e-05_editorials_27_02_2022-19_38_42 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. --> # finetuned_sentence_itr0_2e-05_editorials_27_02_2022-19_38_42 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0914 - Accuracy: 0.9746 - F1: 0.9870 ## Model description More information needed ## Intended uses & 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 104 | 0.0501 | 0.9828 | 0.9913 | | No log | 2.0 | 208 | 0.0435 | 0.9828 | 0.9913 | | No log | 3.0 | 312 | 0.0414 | 0.9828 | 0.9913 | | No log | 4.0 | 416 | 0.0424 | 0.9799 | 0.9898 | | 0.0547 | 5.0 | 520 | 0.0482 | 0.9828 | 0.9913 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
aryanbhosale/DialoGPT-medium-harrypotter
530611a9dab90202e60c132da89b4925f9a2e941
2022-02-28T05:49:40.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
aryanbhosale
null
aryanbhosale/DialoGPT-medium-harrypotter
5
null
transformers
16,906
--- tags: - conversational --- # Harry Potter DialoGPT Model
ppang/model5
fc47892ac93302daa9a592f91389ebf8ee818af6
2022-02-28T10:54:18.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
ppang
null
ppang/model5
5
null
transformers
16,907
Entry not found
frahman/distilbert-base-uncased-finetuned-clinc
190099e400fafebb150505779e3f89317dbe0676
2022-02-28T15:10:11.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:clinc_oos", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
frahman
null
frahman/distilbert-base-uncased-finetuned-clinc
5
null
transformers
16,908
--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9187096774193548 --- <!-- 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-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: 0.7703 - Accuracy: 0.9187 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2896 | 1.0 | 318 | 3.2887 | 0.7419 | | 2.6309 | 2.0 | 636 | 1.8797 | 0.8310 | | 1.5443 | 3.0 | 954 | 1.1537 | 0.8974 | | 1.0097 | 4.0 | 1272 | 0.8560 | 0.9135 | | 0.7918 | 5.0 | 1590 | 0.7703 | 0.9187 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
frahman/distilbert-base-uncased-distilled-clinc
b62f3f4de0c22facf5d041a14b0d395ab2240164
2022-02-28T15:54:22.000Z
[ "pytorch", "distilbert", "text-classification", "dataset:clinc_oos", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
frahman
null
frahman/distilbert-base-uncased-distilled-clinc
5
null
transformers
16,909
--- 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: 0.9406451612903226 --- <!-- 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: 0.1002 - Accuracy: 0.9406 ## Model description More information needed ## Intended uses & 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9039 | 1.0 | 318 | 0.5777 | 0.7335 | | 0.4486 | 2.0 | 636 | 0.2860 | 0.8768 | | 0.2528 | 3.0 | 954 | 0.1792 | 0.9210 | | 0.176 | 4.0 | 1272 | 0.1398 | 0.9274 | | 0.1417 | 5.0 | 1590 | 0.1209 | 0.9329 | | 0.1245 | 6.0 | 1908 | 0.1110 | 0.94 | | 0.1135 | 7.0 | 2226 | 0.1061 | 0.9390 | | 0.1074 | 8.0 | 2544 | 0.1026 | 0.94 | | 0.1032 | 9.0 | 2862 | 0.1006 | 0.9410 | | 0.1017 | 10.0 | 3180 | 0.1002 | 0.9406 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT
acca2f503cf8fccc6562a7c7a7e7380abc320832
2022-03-12T11:50:46.000Z
[ "pytorch", "tensorboard", "roberta", "token-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
StivenLancheros
null
StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT
5
null
transformers
16,910
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT 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-biomedical-clinical-es-finetuned-ner-CRAFT This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on the CRAFT dataset. It achieves the following results on the evaluation set: - Loss: 0.1720 - Precision: 0.8253 - Recall: 0.8147 - F1: 0.8200 - Accuracy: 0.9660 ## Model description This model performs Named Entity Recognition for 6 entity tags: Sequence, Cell, Protein, Gene, Taxon, and Chemical from the [CRAFT](https://github.com/UCDenver-ccp/CRAFT/releases)(Colorado Richly Annotated Full Text) Corpus in English. Entity tags have been normalized and replaced from the original three letter code to a full name e.g. B-Protein, I-Chemical. ## Intended uses & 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1133 | 1.0 | 1360 | 0.1629 | 0.7985 | 0.7782 | 0.7882 | 0.9610 | | 0.049 | 2.0 | 2720 | 0.1530 | 0.8165 | 0.8084 | 0.8124 | 0.9651 | | 0.0306 | 3.0 | 4080 | 0.1603 | 0.8198 | 0.8075 | 0.8136 | 0.9650 | | 0.0158 | 4.0 | 5440 | 0.1720 | 0.8253 | 0.8147 | 0.8200 | 0.9660 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
ali2066/twitter-roberta-base-sentiment_token_itr0_2e-05_all_01_03_2022-04_19_45
06d19c7765ef6af7d8603d157accfc48319f35cb
2022-03-01T03:23:18.000Z
[ "pytorch", "tensorboard", "roberta", "token-classification", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
token-classification
false
ali2066
null
ali2066/twitter-roberta-base-sentiment_token_itr0_2e-05_all_01_03_2022-04_19_45
5
null
transformers
16,911
--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: twitter-roberta-base-sentiment_token_itr0_2e-05_all_01_03_2022-04_19_45 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. --> # twitter-roberta-base-sentiment_token_itr0_2e-05_all_01_03_2022-04_19_45 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2858 - Precision: 0.3206 - Recall: 0.4721 - F1: 0.3819 - Accuracy: 0.8762 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 30 | 0.3772 | 0.0269 | 0.0326 | 0.0294 | 0.8143 | | No log | 2.0 | 60 | 0.3052 | 0.2015 | 0.3596 | 0.2583 | 0.8537 | | No log | 3.0 | 90 | 0.2937 | 0.2737 | 0.4273 | 0.3337 | 0.8722 | | No log | 4.0 | 120 | 0.2852 | 0.2728 | 0.4348 | 0.3353 | 0.8750 | | No log | 5.0 | 150 | 0.2676 | 0.2851 | 0.4474 | 0.3483 | 0.8797 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_sentence_itr0_2e-05_all_01_03_2022-13_11_55
1a65109d7c58991e8a2106d3d8f0e988f43c6876
2022-03-01T12:17:50.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
ali2066
null
ali2066/finetuned_sentence_itr0_2e-05_all_01_03_2022-13_11_55
5
null
transformers
16,912
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: finetuned_sentence_itr0_2e-05_all_01_03_2022-13_11_55 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. --> # finetuned_sentence_itr0_2e-05_all_01_03_2022-13_11_55 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6168 - Accuracy: 0.8286 - F1: 0.8887 - Precision: 0.8628 - Recall: 0.9162 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 390 | 0.3890 | 0.8110 | 0.8749 | 0.8631 | 0.8871 | | 0.4535 | 2.0 | 780 | 0.3921 | 0.8439 | 0.8984 | 0.8721 | 0.9264 | | 0.266 | 3.0 | 1170 | 0.4454 | 0.8415 | 0.8947 | 0.8860 | 0.9034 | | 0.16 | 4.0 | 1560 | 0.5610 | 0.8427 | 0.8957 | 0.8850 | 0.9067 | | 0.16 | 5.0 | 1950 | 0.6180 | 0.8488 | 0.9010 | 0.8799 | 0.9231 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
coastalcph/fairlex-cail-minilm
96c5fdef6fdc4d1148c33ee191d7a52026675ebb
2022-03-01T13:12:22.000Z
[ "pytorch", "xlm-roberta", "fill-mask", "zh", "transformers", "legal", "fairlex", "license:cc-by-nc-sa-4.0", "autotrain_compatible" ]
fill-mask
false
coastalcph
null
coastalcph/fairlex-cail-minilm
5
null
transformers
16,913
--- language: zh pipeline_tag: fill-mask license: cc-by-nc-sa-4.0 tags: - legal - fairlex widget: - text: "上述事实,被告人在庭审过程中亦无异议,且有<mask>的陈述,现场辨认笔录及照片,被告人的前科刑事判决书,释放证明材料,抓获经过,被告人的供述及身份证明等证据证实,足以认定。" --- # FairLex: A multilingual benchmark for evaluating fairness in legal text processing We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian and Chinese) and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP. --- Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. FairLex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland. --- ## Pre-training details For the purpose of this work, we release four domain-specific BERT models with continued pre-training on the corpora of the examined datasets (ECtHR, SCOTUS, FSCS, SPC). We train mini-sized BERT models with 6 Transformer blocks, 384 hidden units, and 12 attention heads. We warm-start all models from the public MiniLMv2 (Wang et al., 2021) using the distilled version of RoBERTa (Liu et al., 2019). For the English datasets (ECtHR, SCOTUS) and the one distilled from XLM-R (Conneau et al., 2021) for the rest (trilingual FSCS, and Chinese SPC). ## Models list | Model name | Training corpora | Language | |-----------------------------------|------------------|--------------------| | `coastalcph/fairlex-ecthr-minlm` | ECtHR | `en` | | `coastalcph/fairlex-scotus-minlm` | SCOTUS | `en` | | `coastalcph/fairlex-fscs-minlm` | FSCS | [`de`, `fr`, `it`] | | `coastalcph/fairlex-cail-minlm` | CAIL | `zh` | ## Load Pretrained Model ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("coastalcph/fairlex-cail-minlm") model = AutoModel.from_pretrained("coastalcph/fairlex-cail-minlm") ``` ## Evaluation on downstream tasks Consider the experiments in the article: _Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. Fairlex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland._ ## Author - Publication ``` @inproceedings{chalkidis-2022-fairlex, author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and Tomada, Letizia and Schwemer, Sebastian Felix and Søgaard, Anders}, title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, year={2022}, address={Dublin, Ireland} } ``` Ilias Chalkidis on behalf of [CoAStaL NLP Group](https://coastalcph.github.io) | Github: [@ilias.chalkidis](https://github.com/iliaschalkidis) | Twitter: [@KiddoThe2B](https://twitter.com/KiddoThe2B) |
ali2066/finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32
3b4a4675fea6cd912bb3346a707ffbdd299dc363
2022-03-01T12:31:32.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
ali2066
null
ali2066/finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32
5
null
transformers
16,914
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32 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. --> # finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4787 - Accuracy: 0.8138 - F1: 0.8785 - Precision: 0.8489 - Recall: 0.9101 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 390 | 0.4335 | 0.7732 | 0.8533 | 0.8209 | 0.8883 | | 0.5141 | 2.0 | 780 | 0.4196 | 0.8037 | 0.8721 | 0.8446 | 0.9015 | | 0.3368 | 3.0 | 1170 | 0.4519 | 0.8098 | 0.8779 | 0.8386 | 0.9212 | | 0.2677 | 4.0 | 1560 | 0.4787 | 0.8122 | 0.8785 | 0.8452 | 0.9146 | | 0.2677 | 5.0 | 1950 | 0.4912 | 0.8146 | 0.8794 | 0.8510 | 0.9097 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/twitter-roberta-base_sentence_itr0_1e-05_all_01_03_2022-13_38_07
3e94bce8bb2c53a2f66f401348275884d0c1937d
2022-03-01T12:47:58.000Z
[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers" ]
text-classification
false
ali2066
null
ali2066/twitter-roberta-base_sentence_itr0_1e-05_all_01_03_2022-13_38_07
5
null
transformers
16,915
Entry not found
ali2066/bert_base_uncased_itr0_0.0001_all_01_03_2022-14_08_15
4a63d707050963ae2ea5d27772f7e4f960a75573
2022-03-01T13:18:27.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
ali2066
null
ali2066/bert_base_uncased_itr0_0.0001_all_01_03_2022-14_08_15
5
null
transformers
16,916
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: bert_base_uncased_itr0_0.0001_all_01_03_2022-14_08_15 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_base_uncased_itr0_0.0001_all_01_03_2022-14_08_15 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7632 - Accuracy: 0.8263 - F1: 0.8871 - Precision: 0.8551 - Recall: 0.9215 ## Model description More information needed ## Intended uses & 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 390 | 0.3986 | 0.8305 | 0.8903 | 0.8868 | 0.8938 | | 0.4561 | 2.0 | 780 | 0.4018 | 0.8439 | 0.9009 | 0.8805 | 0.9223 | | 0.3111 | 3.0 | 1170 | 0.4306 | 0.8354 | 0.8924 | 0.8974 | 0.8875 | | 0.1739 | 4.0 | 1560 | 0.5499 | 0.8378 | 0.9002 | 0.8547 | 0.9509 | | 0.1739 | 5.0 | 1950 | 0.6223 | 0.85 | 0.9052 | 0.8814 | 0.9303 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-14_57_21
5a903832f0c9b3443ea96b727830fd711b7ff248
2022-03-01T13:58:54.000Z
[ "pytorch", "tensorboard", "roberta", "token-classification", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
token-classification
false
ali2066
null
ali2066/twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-14_57_21
5
null
transformers
16,917
--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-14_57_21 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. --> # twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-14_57_21 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5905 - Precision: 0.0024 - Recall: 0.0143 - F1: 0.0041 - Accuracy: 0.6867 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 10 | 0.6081 | 0.0 | 0.0 | 0.0 | 0.6904 | | No log | 2.0 | 20 | 0.6014 | 0.0025 | 0.0130 | 0.0042 | 0.6934 | | No log | 3.0 | 30 | 0.5953 | 0.0 | 0.0 | 0.0 | 0.6930 | | No log | 4.0 | 40 | 0.5858 | 0.0 | 0.0 | 0.0 | 0.6941 | | No log | 5.0 | 50 | 0.5815 | 0.0 | 0.0 | 0.0 | 0.6947 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/correct_twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_36_04
bf746b65a2f8b0ac1930444eca439343862fdd1c
2022-03-01T14:39:23.000Z
[ "pytorch", "tensorboard", "roberta", "token-classification", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
token-classification
false
ali2066
null
ali2066/correct_twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_36_04
5
null
transformers
16,918
--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_36_04 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. --> # correct_twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_36_04 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2876 - Precision: 0.2345 - Recall: 0.4281 - F1: 0.3030 - Accuracy: 0.8728 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 30 | 0.3907 | 0.0433 | 0.0824 | 0.0568 | 0.7626 | | No log | 2.0 | 60 | 0.3046 | 0.2302 | 0.4095 | 0.2947 | 0.8598 | | No log | 3.0 | 90 | 0.2945 | 0.2084 | 0.4095 | 0.2762 | 0.8668 | | No log | 4.0 | 120 | 0.2687 | 0.2847 | 0.4607 | 0.3519 | 0.8761 | | No log | 5.0 | 150 | 0.2643 | 0.2779 | 0.4444 | 0.3420 | 0.8788 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/bert_base_uncased_itr0_0.0001_webDiscourse_01_03_2022-16_08_12
447e3f28d2c4318688f9bc30b589a9e31073472c
2022-03-01T15:11:41.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
ali2066
null
ali2066/bert_base_uncased_itr0_0.0001_webDiscourse_01_03_2022-16_08_12
5
null
transformers
16,919
Entry not found
batterydata/batteryscibert-uncased-squad-v1
c434942fe8c6b4f73715cffd77ea5af08ae9f734
2022-03-03T20:28:37.000Z
[ "pytorch", "bert", "question-answering", "en", "dataset:squad", "dataset:batterydata/battery-device-data-qa", "transformers", "question answering", "license:apache-2.0", "autotrain_compatible" ]
question-answering
false
batterydata
null
batterydata/batteryscibert-uncased-squad-v1
5
null
transformers
16,920
--- language: en tags: question answering license: apache-2.0 datasets: - squad - batterydata/battery-device-data-qa metrics: squad --- # BatterySciBERT-uncased for QA **Language model:** batteryscibert-uncased **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD v1 **Eval data:** SQuAD v1 **Code:** See [example](https://github.com/ShuHuang/batterybert) **Infrastructure**: 8x DGX A100 ## Hyperparameters ``` batch_size = 32 n_epochs = 3 base_LM_model = "batteryscibert-uncased" max_seq_len = 386 learning_rate = 2e-5 doc_stride=128 max_query_length=64 ``` ## Performance Evaluated on the SQuAD v1.0 dev set. ``` "exact": 79.81, "f1": 87.66, ``` Evaluated on the battery device dataset. ``` "precision": 66.65, "recall": 85.29, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "batterydata/batteryscibert-uncased-squad-v1" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'What is the electrolyte?', 'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
batterydata/batterybert-cased-abstract
7316b880b09f305e26a8e98f5e86d412b4b9d855
2022-03-05T14:54:39.000Z
[ "pytorch", "bert", "text-classification", "en", "dataset:batterydata/paper-abstracts", "transformers", "Text Classification", "license:apache-2.0" ]
text-classification
false
batterydata
null
batterydata/batterybert-cased-abstract
5
null
transformers
16,921
--- language: en tags: Text Classification license: apache-2.0 datasets: - batterydata/paper-abstracts metrics: glue --- # BatteryBERT-cased for Battery Abstract Classification **Language model:** batterybert-cased **Language:** English **Downstream-task:** Text Classification **Training data:** training\_data.csv **Eval data:** val\_data.csv **Code:** See [example](https://github.com/ShuHuang/batterybert) **Infrastructure**: 8x DGX A100 ## Hyperparameters ``` batch_size = 32 n_epochs = 11 base_LM_model = "batterybert-cased" learning_rate = 2e-5 ``` ## Performance ``` "Validation accuracy": 97.29, "Test accuracy": 96.85, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline model_name = "batterydata/batterybert-cased-abstract" # a) Get predictions nlp = pipeline('text-classification', model=model_name, tokenizer=model_name) input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'} res = nlp(input) # b) Load model & tokenizer model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
batterydata/batteryscibert-cased-abstract
3bf4862fa015bb25727d7cb9793064eb18e77141
2022-03-05T14:54:32.000Z
[ "pytorch", "bert", "text-classification", "en", "dataset:batterydata/paper-abstracts", "transformers", "Text Classification", "license:apache-2.0" ]
text-classification
false
batterydata
null
batterydata/batteryscibert-cased-abstract
5
null
transformers
16,922
--- language: en tags: Text Classification license: apache-2.0 datasets: - batterydata/paper-abstracts metrics: glue --- # BatterySciBERT-cased for Battery Abstract Classification **Language model:** batteryscibert-cased **Language:** English **Downstream-task:** Text Classification **Training data:** training\_data.csv **Eval data:** val\_data.csv **Code:** See [example](https://github.com/ShuHuang/batterybert) **Infrastructure**: 8x DGX A100 ## Hyperparameters ``` batch_size = 32 n_epochs = 11 base_LM_model = "batteryscibert-cased" learning_rate = 2e-5 ``` ## Performance ``` "Validation accuracy": 97.06, "Test accuracy": 97.19, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline model_name = "batterydata/batteryscibert-cased-abstract" # a) Get predictions nlp = pipeline('text-classification', model=model_name, tokenizer=model_name) input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'} res = nlp(input) # b) Load model & tokenizer model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
batterydata/batteryonlybert-cased-abstract
35fab45605285d77522d99fc1eab7d07be4d6aa2
2022-03-05T14:54:53.000Z
[ "pytorch", "bert", "text-classification", "en", "dataset:batterydata/paper-abstracts", "transformers", "Text Classification", "license:apache-2.0" ]
text-classification
false
batterydata
null
batterydata/batteryonlybert-cased-abstract
5
null
transformers
16,923
--- language: en tags: Text Classification license: apache-2.0 datasets: - batterydata/paper-abstracts metrics: glue --- # BatteryOnlyBERT-cased for Battery Abstract Classification **Language model:** batteryonlybert-cased **Language:** English **Downstream-task:** Text Classification **Training data:** training\_data.csv **Eval data:** val\_data.csv **Code:** See [example](https://github.com/ShuHuang/batterybert) **Infrastructure**: 8x DGX A100 ## Hyperparameters ``` batch_size = 32 n_epochs = 14 base_LM_model = "batteryonlybert-cased" learning_rate = 2e-5 ``` ## Performance ``` "Validation accuracy": 97.33, "Test accuracy": 97.34, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline model_name = "batterydata/batteryonlybert-cased-abstract" # a) Get predictions nlp = pipeline('text-classification', model=model_name, tokenizer=model_name) input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'} res = nlp(input) # b) Load model & tokenizer model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
batterydata/batteryonlybert-uncased-abstract
ab2a1b254413a35d634944e752344bcae38d28fa
2022-03-05T14:53:56.000Z
[ "pytorch", "bert", "text-classification", "en", "dataset:batterydata/paper-abstracts", "transformers", "Text Classification", "license:apache-2.0" ]
text-classification
false
batterydata
null
batterydata/batteryonlybert-uncased-abstract
5
null
transformers
16,924
--- language: en tags: Text Classification license: apache-2.0 datasets: - batterydata/paper-abstracts metrics: glue --- # BatteryOnlyBERT-uncased for Battery Abstract Classification **Language model:** batteryonlybert-uncased **Language:** English **Downstream-task:** Text Classification **Training data:** training\_data.csv **Eval data:** val\_data.csv **Code:** See [example](https://github.com/ShuHuang/batterybert) **Infrastructure**: 8x DGX A100 ## Hyperparameters ``` batch_size = 16 n_epochs = 13 base_LM_model = "batteryonlybert-uncased" learning_rate = 3e-5 ``` ## Performance ``` "Validation accuracy": 97.18, "Test accuracy": 97.08, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline model_name = "batterydata/batteryonlybert-uncased-abstract" # a) Get predictions nlp = pipeline('text-classification', model=model_name, tokenizer=model_name) input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'} res = nlp(input) # b) Load model & tokenizer model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
armageddon/electra-base-squad2-covid-qa-deepset
64d25cb635299915b3bb6d6f4c0f702a5bf3dcdc
2022-03-02T06:38:05.000Z
[ "pytorch", "tensorboard", "electra", "question-answering", "dataset:covid_qa_deepset", "transformers", "generated_from_trainer", "license:cc-by-4.0", "model-index", "autotrain_compatible" ]
question-answering
false
armageddon
null
armageddon/electra-base-squad2-covid-qa-deepset
5
null
transformers
16,925
--- license: cc-by-4.0 tags: - generated_from_trainer datasets: - covid_qa_deepset model-index: - name: electra-base-squad2-covid-qa-deepset results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # electra-base-squad2-covid-qa-deepset This model is a fine-tuned version of [deepset/electra-base-squad2](https://huggingface.co/deepset/electra-base-squad2) on the covid_qa_deepset 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 - distributed_type: tpu - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.9.0+cu102 - Datasets 1.18.3 - Tokenizers 0.11.6
Cheatham/xlm-roberta-large-finetuned-r01
b7c853a26475505eeaf1a2ef6b4b3bb0e7df3c12
2022-03-02T10:30:34.000Z
[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]
text-classification
false
Cheatham
null
Cheatham/xlm-roberta-large-finetuned-r01
5
null
transformers
16,926
Entry not found
evs/distilbert-base-uncased-finetuned-emotion
2b1eef0e539edc8c5559ab2209b2152e9097af33
2022-03-02T12:46:57.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers" ]
text-classification
false
evs
null
evs/distilbert-base-uncased-finetuned-emotion
5
null
transformers
16,927
Entry not found
Cheatham/xlm-roberta-large-finetuned-d1r01
772617dbe405bf288be5bbc9f2881559aa2c72b5
2022-03-02T13:37:04.000Z
[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]
text-classification
false
Cheatham
null
Cheatham/xlm-roberta-large-finetuned-d1r01
5
null
transformers
16,928
Entry not found
lucasmtz/distilbert-base-uncased-finetuned-ner
b4aa38ba70b824d8b9bf8559617c13038a1f850e
2022-03-02T15:56:12.000Z
[ "pytorch", "tensorboard", "distilbert", "token-classification", "dataset:conll2003", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
lucasmtz
null
lucasmtz/distilbert-base-uncased-finetuned-ner
5
null
transformers
16,929
--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9252181597260577 - name: Recall type: recall value: 0.9370175634858485 - name: F1 type: f1 value: 0.9310804802134283 - name: Accuracy type: accuracy value: 0.9834146186474335 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0610 - Precision: 0.9252 - Recall: 0.9370 - F1: 0.9311 - Accuracy: 0.9834 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.244 | 1.0 | 878 | 0.0714 | 0.9104 | 0.9181 | 0.9142 | 0.9797 | | 0.0568 | 2.0 | 1756 | 0.0605 | 0.9183 | 0.9351 | 0.9266 | 0.9827 | | 0.0302 | 3.0 | 2634 | 0.0610 | 0.9252 | 0.9370 | 0.9311 | 0.9834 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
Akash7897/distilbert-base-uncased-finetuned-sst2
0f3e476bb26b0ed34c676b9db35ad06d5c1e5323
2022-03-03T08:57:39.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
Akash7897
null
Akash7897/distilbert-base-uncased-finetuned-sst2
5
null
transformers
16,930
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-sst2 results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.9036697247706422 --- <!-- 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-sst2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3010 - Accuracy: 0.9037 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1793 | 1.0 | 4210 | 0.3010 | 0.9037 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
batterydata/batteryonlybert-uncased
e675b6d643afd3cd7f3aa2f37e0cd124248e4a38
2022-03-05T16:03:58.000Z
[ "pytorch", "bert", "fill-mask", "en", "dataset:batterypapers", "transformers", "exbert", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
batterydata
null
batterydata/batteryonlybert-uncased
5
null
transformers
16,931
--- language: en tags: - exbert license: apache-2.0 datasets: - batterypapers --- # BatteryOnlyBERT-cased model Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective. It was introduced in [this paper](paper_link) and first released in [this repository](https://github.com/ShuHuang/batterybert). This model is case-sensitive: it makes a difference between english and English. ## Model description BatteryOnlyBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Training data The BatteryOnlyBERT model was pretrained on the full text of battery papers only. The paper corpus contains 1.87B tokens form a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at [Github](https://github.com/ShuHuang/batterybert/blob/main/corpus.txt). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 28,996. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 8 NVIDIA DGX A100 GPUs for 1,500,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=batterybert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='batterydata/batteryonlybert-cased') >>> unmasker("Hello I'm a <mask> model.") ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('batterydata/batteryonlybert-cased') model = BertModel.from_pretrained('batterydata/batteryonlybert-cased') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('batterydata/batteryonlybert-cased') model = TFBertModel.from_pretrained('batterydata/batteryonlybert-cased') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results Final loss: 1.0614. ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
daisyxie21/bert-base-uncased-8-200-0.01
7b67fb16b7a22c16c86549b2acd0e424a6591f67
2022-03-04T14:21:12.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
daisyxie21
null
daisyxie21/bert-base-uncased-8-200-0.01
5
null
transformers
16,932
Entry not found
daisyxie21/bert-base-uncased-8-10-0.01
c4494f588452be44ba13f5581221312585928b2f
2022-03-04T16:27:40.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
daisyxie21
null
daisyxie21/bert-base-uncased-8-10-0.01
5
null
transformers
16,933
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: bert-base-uncased-8-10-0.01 results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-8-10-0.01 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8324 - Matthews Correlation: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 1.0 | 400 | 0.8324 | 0.0 | | 1.0904 | 2.0 | 800 | 1.3157 | 0.0 | | 0.9461 | 3.0 | 1200 | 0.4407 | 0.0 | | 0.9565 | 4.0 | 1600 | 2.1082 | 0.0 | | 1.024 | 5.0 | 2000 | 0.7220 | 0.0 | | 1.024 | 6.0 | 2400 | 0.7414 | 0.0 | | 0.8362 | 7.0 | 2800 | 0.4442 | 0.0 | | 0.6765 | 8.0 | 3200 | 0.5481 | 0.0 | | 0.5902 | 9.0 | 3600 | 0.5642 | 0.0 | | 0.5476 | 10.0 | 4000 | 0.4449 | 0.0 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.9.0 - Datasets 1.18.3 - Tokenizers 0.11.0
crabz/distil-slovakbert
bf7ccaca15902d4cc2fc93e5991dd9ccd6f9eb73
2022-03-06T12:30:11.000Z
[ "pytorch", "roberta", "fill-mask", "sk", "dataset:c4-sk", "transformers", "license:mit", "autotrain_compatible" ]
fill-mask
false
crabz
null
crabz/distil-slovakbert
5
null
transformers
16,934
--- language: sk license: mit tags: - fill-mask - roberta datasets: - c4-sk inference: false ---
DrishtiSharma/distilbert-base-uncased-finetuned-emotion
fe3eb73a0d54f7d79b66549500e4037e8be2754b
2022-03-05T06:20:40.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers" ]
text-classification
false
DrishtiSharma
null
DrishtiSharma/distilbert-base-uncased-finetuned-emotion
5
null
transformers
16,935
Entry not found
jonghyuk/LJP
94323916202ddcea4b0be236efef057bceaa76c7
2022-03-10T05:00:05.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
jonghyuk
null
jonghyuk/LJP
5
1
transformers
16,936
Entry not found
anjandash/finetuned-bert-java-cmpx-v1
8b2aab3dfdf17df37a9724942d5c64410aef156f
2022-03-07T12:19:40.000Z
[ "pytorch", "tf", "bert", "text-classification", "java", "dataset:giganticode/java-cmpx-v1", "transformers", "license:mit" ]
text-classification
false
anjandash
null
anjandash/finetuned-bert-java-cmpx-v1
5
null
transformers
16,937
--- language: - java license: mit datasets: - giganticode/java-cmpx-v1 ---
Anthos23/FS-finbert-fine-tuned-f1
c89eafc9ef0e473b992a631ff579cadb01a686aa
2022-03-07T16:12:42.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
Anthos23
null
Anthos23/FS-finbert-fine-tuned-f1
5
null
transformers
16,938
Entry not found
SuperAI2-Machima/mt5-small-translation_thai-english
0cd39ca186940c639791daf1430d73b0483b6637
2022-03-08T01:37:11.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
SuperAI2-Machima
null
SuperAI2-Machima/mt5-small-translation_thai-english
5
null
transformers
16,939
Entry not found
aaraki/distilbert-base-uncased-finetuned-cola
1c70c1b8645681d3c68d6e0b9240fd2e1b74acfd
2022-03-09T02:08:47.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
aaraki
null
aaraki/distilbert-base-uncased-finetuned-cola
5
null
transformers
16,940
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.40967417350821667 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5026 - Matthews Correlation: 0.4097 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5335 | 1.0 | 535 | 0.5026 | 0.4097 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6
ctoraman/RoBERTa-TR-medium-wp-28k
f2db5487b399ef828b6d0826c1539625d7f6d2c9
2022-04-20T07:01:13.000Z
[ "pytorch", "roberta", "fill-mask", "tr", "dataset:oscar", "arxiv:2204.08832", "transformers", "license:cc-by-nc-sa-4.0", "autotrain_compatible" ]
fill-mask
false
ctoraman
null
ctoraman/RoBERTa-TR-medium-wp-28k
5
null
transformers
16,941
--- language: - tr tags: - roberta license: cc-by-nc-sa-4.0 datasets: - oscar --- # RoBERTa Turkish medium WordPiece 28k (uncased) Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hidden size). Tokenization algorithm is WordPiece. Vocabulary size is 28.6k. The details and performance comparisons can be found at this paper: https://arxiv.org/abs/2204.08832 The following code can be used for model loading and tokenization, example max length (514) can be changed: ``` model = AutoModel.from_pretrained([model_path]) #for sequence classification: #model = AutoModelForSequenceClassification.from_pretrained([model_path], num_labels=[num_classes]) tokenizer = PreTrainedTokenizerFast(tokenizer_file=[file_path]) tokenizer.mask_token = "[MASK]" tokenizer.cls_token = "[CLS]" tokenizer.sep_token = "[SEP]" tokenizer.pad_token = "[PAD]" tokenizer.unk_token = "[UNK]" tokenizer.bos_token = "[CLS]" tokenizer.eos_token = "[SEP]" tokenizer.model_max_length = 514 ``` ### BibTeX entry and citation info ```bibtex @misc{https://doi.org/10.48550/arxiv.2204.08832, doi = {10.48550/ARXIV.2204.08832}, url = {https://arxiv.org/abs/2204.08832}, author = {Toraman, Cagri and Yilmaz, Eyup Halit and Şahinuç, Furkan and Ozcelik, Oguzhan}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Impact of Tokenization on Language Models: An Analysis for Turkish}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International} } ```
ArnavL/twteval-pretrained
3bfcc098686d2ad3781678f6b1fea6fbffa5093e
2022-03-10T04:52:52.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "license:mit", "autotrain_compatible" ]
fill-mask
false
ArnavL
null
ArnavL/twteval-pretrained
5
null
transformers
16,942
--- license: mit --- # Pretrained Model BASE MODEL : BERT-BASE-UNCASED DATASET : [TWTEVAL SENTIMENT](https://huggingface.co/datasets/ArnavL/TWTEval-Pretraining-Processed)
amanm27/bert-base-uncased-wiki
b8618da1fb9b25fcb4a28fc99ffe3075848d2089
2022-03-10T06:15:01.000Z
[ "pytorch", "tensorboard", "bert", "fill-mask", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
fill-mask
false
amanm27
null
amanm27/bert-base-uncased-wiki
5
null
transformers
16,943
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-uncased-wiki results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-wiki This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7509 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9294 | 1.0 | 2319 | 1.7732 | | 1.8219 | 2.0 | 4638 | 1.7363 | | 1.7957 | 3.0 | 6957 | 1.7454 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0 - Datasets 1.18.3 - Tokenizers 0.11.0
Yangdf/mt5-base-chinese-qg
030427d42fd45048eb3b3ecdd76382d911038cf9
2022-06-14T06:05:26.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
Yangdf
null
Yangdf/mt5-base-chinese-qg
5
null
transformers
16,944
Entry not found
kazandaev/mt5-base-en-ru
4cbf141a45169f204019a8bb70dc1f0a90e47de9
2022-03-21T19:31:50.000Z
[ "pytorch", "tf", "jax", "tensorboard", "mt5", "text2text-generation", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
text2text-generation
false
kazandaev
null
kazandaev/mt5-base-en-ru
5
null
transformers
16,945
--- tags: - generated_from_trainer metrics: - bleu model-index: - name: mt5-base-en-ru results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-base-en-ru This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7194 - Bleu: 14.3528 - Gen Len: 17.8655 ## Model description More information needed ## Intended uses & 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: 4 - seed: 42 - gradient_accumulation_steps: 10 - total_train_batch_size: 160 - 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 | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.5319 | 1.0 | 9641 | 0.8010 | 14.0075 | 17.8566 | | 0.5903 | 2.0 | 19282 | 0.7652 | 14.268 | 17.8691 | | 0.6942 | 3.0 | 28923 | 0.7194 | 14.3528 | 17.8655 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0
Sarahliu186/distilbert-base-uncased-finetuned-cola
520b7778ca93b55a7d10eb28423cdcb18f320316
2022-03-10T20:47:06.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
Sarahliu186
null
Sarahliu186/distilbert-base-uncased-finetuned-cola
5
null
transformers
16,946
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.548847644400088 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7415 - Matthews Correlation: 0.5488 ## Model description More information needed ## Intended uses & 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 | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5273 | 1.0 | 535 | 0.5063 | 0.4092 | | 0.3491 | 2.0 | 1070 | 0.4956 | 0.5259 | | 0.2352 | 3.0 | 1605 | 0.6045 | 0.5301 | | 0.1737 | 4.0 | 2140 | 0.7415 | 0.5488 | | 0.1264 | 5.0 | 2675 | 0.8459 | 0.5466 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6
sanchit-gandhi/wav2vec2-2-bert-large-long-run
e56a48c1e52b868a64a4e86902aa3f753efb7aa2
2022-03-12T06:47:06.000Z
[ "pytorch", "tensorboard", "speech-encoder-decoder", "automatic-speech-recognition", "dataset:librispeech_asr", "transformers", "generated_from_trainer", "model-index" ]
automatic-speech-recognition
false
sanchit-gandhi
null
sanchit-gandhi/wav2vec2-2-bert-large-long-run
5
null
transformers
16,947
--- tags: - generated_from_trainer datasets: - librispeech_asr model-index: - name: '' 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. --> # This model was trained from scratch on the librispeech_asr dataset. It achieves the following results on the evaluation set: - Loss: 12.7395 - Wer: 2.0272 ## Model description More information needed ## Intended uses & 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 6.6781 | 1.68 | 1500 | 6.6386 | 1.1672 | | 6.6836 | 3.36 | 3000 | 6.6587 | 1.9518 | | 6.6622 | 5.04 | 4500 | 6.5888 | 1.9276 | | 5.844 | 6.73 | 6000 | 6.7220 | 1.9423 | | 6.4588 | 8.41 | 7500 | 7.7569 | 1.9964 | | 6.4097 | 10.09 | 9000 | 9.2515 | 2.0168 | | 6.2676 | 11.77 | 10500 | 9.8159 | 2.0179 | | 6.4948 | 13.45 | 12000 | 10.7091 | 2.0223 | | 6.2728 | 15.13 | 13500 | 11.7747 | 2.0255 | | 6.319 | 16.82 | 15000 | 12.2084 | 2.0259 | | 5.8157 | 18.5 | 16500 | 12.7395 | 2.0272 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0
StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-Concat_CRAFT_es
1fcde623c6418569ed81c5cac7a0d0edad63c1fe
2022-03-11T18:47:48.000Z
[ "pytorch", "tensorboard", "roberta", "token-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
StivenLancheros
null
StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-Concat_CRAFT_es
5
null
transformers
16,948
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: roberta-base-biomedical-clinical-es-finetuned-ner-Concat_CRAFT_es 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-biomedical-clinical-es-finetuned-ner-Concat_CRAFT_es This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1874 - Precision: 0.8559 - Recall: 0.8425 - F1: 0.8492 - Accuracy: 0.9696 ## Model description More information needed ## Intended uses & 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.072 | 1.0 | 2719 | 0.1500 | 0.8138 | 0.8224 | 0.8181 | 0.9644 | | 0.0305 | 2.0 | 5438 | 0.1555 | 0.8417 | 0.8253 | 0.8334 | 0.9674 | | 0.014 | 3.0 | 8157 | 0.1743 | 0.8429 | 0.8412 | 0.8421 | 0.9685 | | 0.0076 | 4.0 | 10876 | 0.1874 | 0.8559 | 0.8425 | 0.8492 | 0.9696 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6
IsaacBot/t5-small-finetuned-mfaqs-en
259ceda9588be6810e98edcd29cc4139d5f59166
2022-03-11T14:18:42.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "dataset:mfaq", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
IsaacBot
null
IsaacBot/t5-small-finetuned-mfaqs-en
5
null
transformers
16,949
--- license: apache-2.0 tags: - generated_from_trainer datasets: - mfaq model-index: - name: t5-small-finetuned-mfaqs-en results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-mfaqs-en This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the mfaq 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.1 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6
jfealko/wav2vec2-large-xls-r-300m-irish-custom-data
d05abee9624f7ae4ff5b43c9b08d08e2432e9b24
2022-03-11T20:16:54.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
jfealko
null
jfealko/wav2vec2-large-xls-r-300m-irish-custom-data
5
null
transformers
16,950
Entry not found
anton-l/xtreme_s_xlsr_minds14
97f45602b9d5267e3ac469f6744dd92f4b7f9783
2022-03-14T10:58:42.000Z
[ "pytorch", "tensorboard", "wav2vec2", "audio-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
audio-classification
false
anton-l
null
anton-l/xtreme_s_xlsr_minds14
5
null
transformers
16,951
--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: xtreme_s_xlsr_minds14 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. --> # xtreme_s_xlsr_minds14 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2566 - F1: {'f1': 0.9460569664921582, 'accuracy': 0.9468540012217471} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 64 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1500 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------:| | 2.551 | 2.7 | 200 | 2.5921 | {'f1': 0.03454307545755678, 'accuracy': 0.1148442272449603} | | 1.6934 | 5.41 | 400 | 1.5353 | {'f1': 0.5831241711045994, 'accuracy': 0.6053756872327428} | | 0.5914 | 8.11 | 600 | 0.7337 | {'f1': 0.7990425247664236, 'accuracy': 0.7947464874770922} | | 0.3896 | 10.81 | 800 | 0.5076 | {'f1': 0.8738199236080776, 'accuracy': 0.872327428222358} | | 0.5052 | 13.51 | 1000 | 0.4917 | {'f1': 0.8744760456867134, 'accuracy': 0.8747709224190593} | | 0.4806 | 16.22 | 1200 | 0.4751 | {'f1': 0.8840798740258787, 'accuracy': 0.8845448992058644} | | 0.2103 | 18.92 | 1400 | 0.5228 | {'f1': 0.8721632556623751, 'accuracy': 0.8729383017715333} | | 0.4198 | 21.62 | 1600 | 0.5910 | {'f1': 0.8755207264572983, 'accuracy': 0.8766035430665852} | | 0.11 | 24.32 | 1800 | 0.4464 | {'f1': 0.896423086249818, 'accuracy': 0.8955406230910201} | | 0.1233 | 27.03 | 2000 | 0.3760 | {'f1': 0.9012283567348968, 'accuracy': 0.9016493585827734} | | 0.1827 | 29.73 | 2200 | 0.4178 | {'f1': 0.9042381720184095, 'accuracy': 0.9059254734270006} | | 0.1235 | 32.43 | 2400 | 0.4152 | {'f1': 0.9063257163259107, 'accuracy': 0.9071472205253512} | | 0.1873 | 35.14 | 2600 | 0.2903 | {'f1': 0.9369340598806323, 'accuracy': 0.9376908979841173} | | 0.017 | 37.84 | 2800 | 0.3046 | {'f1': 0.9300781160576355, 'accuracy': 0.9303604153940135} | | 0.0436 | 40.54 | 3000 | 0.3111 | {'f1': 0.9315034391389341, 'accuracy': 0.9321930360415394} | | 0.0455 | 43.24 | 3200 | 0.2748 | {'f1': 0.9417365311433034, 'accuracy': 0.9425778863775198} | | 0.046 | 45.95 | 3400 | 0.2800 | {'f1': 0.9390712658440112, 'accuracy': 0.9395235186316433} | | 0.0042 | 48.65 | 3600 | 0.2566 | {'f1': 0.9460569664921582, 'accuracy': 0.9468540012217471} | ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4.dev0 - Tokenizers 0.11.6
anwesham/indicbert_hi_ur
f0d4a1286a8fcfc2af5fb363092c9dbbe3b16401
2022-03-13T02:51:04.000Z
[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
anwesham
null
anwesham/indicbert_hi_ur
5
null
transformers
16,952
Entry not found
GPL/dbpedia-entity-distilbert-tas-b-gpl-self_miner
27dc9c694c8307bcee5a42e9aa4f7d8f3e417909
2022-03-14T14:23:21.000Z
[ "pytorch", "distilbert", "feature-extraction", "sentence-transformers", "sentence-similarity", "transformers" ]
sentence-similarity
false
GPL
null
GPL/dbpedia-entity-distilbert-tas-b-gpl-self_miner
5
null
sentence-transformers
16,953
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch def cls_pooling(model_output, attention_mask): return model_output[0][:,0] # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 140000 with parameters: ``` {'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `gpl.toolkit.loss.MarginDistillationLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "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": 140000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
cambridgeltl/guardian_news_distilbert-base-uncased
35332e79269692b1cf536a172abbfb4330054d01
2022-03-14T15:47:45.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
cambridgeltl
null
cambridgeltl/guardian_news_distilbert-base-uncased
5
null
transformers
16,954
Entry not found
Simply-divine/finetune_indian_asr
c79091f0adb512172b65a1f3c57c28127a65ed30
2022-03-15T22:57:29.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "model-index" ]
automatic-speech-recognition
false
Simply-divine
null
Simply-divine/finetune_indian_asr
5
1
transformers
16,955
--- tags: - generated_from_trainer model-index: - name: finetune_indian_asr 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. --> # finetune_indian_asr This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-indian-english-enm-700](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-indian-english-enm-700) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4215 - Wer: 0.3403 ## Model description More information needed ## Intended uses & 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: 32 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.0566 | 3.45 | 500 | 2.9944 | 1.0 | | 2.7241 | 6.9 | 1000 | 1.4455 | 0.7654 | | 0.9755 | 10.34 | 1500 | 0.4299 | 0.4034 | | 0.4624 | 13.79 | 2000 | 0.3628 | 0.3297 | | 0.3158 | 17.24 | 2500 | 0.3835 | 0.2952 | | 0.2604 | 20.69 | 3000 | 0.3802 | 0.2877 | | 0.2 | 24.14 | 3500 | 0.3842 | 0.2799 | | 1.7441 | 27.59 | 4000 | 0.4215 | 0.3403 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
GleamEyeBeast/ASCEND_Dataset_Model
048727f41ca27be9533ea7d796a7395c330ea3aa
2022-03-16T22:58:29.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
GleamEyeBeast
null
GleamEyeBeast/ASCEND_Dataset_Model
5
null
transformers
16,956
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: ASCEND_Dataset_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. --> # ASCEND_Dataset_Model This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.9199 - Wer: 0.9540 - Cer: 0.9868 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - 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: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | 16.9063 | 1.0 | 687 | 4.7768 | 1.0 | 1.0 | | 5.0252 | 2.0 | 1374 | 4.7004 | 1.0 | 1.0 | | 4.9378 | 3.0 | 2061 | 4.6715 | 1.0 | 1.0 | | 5.1468 | 4.0 | 2748 | 4.6605 | 1.0 | 1.0 | | 4.9353 | 5.0 | 3435 | 4.6470 | 1.0 | 1.0 | | 4.913 | 6.0 | 4122 | 4.6177 | 1.0 | 1.0 | | 4.8034 | 7.0 | 4809 | 4.7699 | 1.0 | 1.0 | | 4.6905 | 8.0 | 5496 | 4.3596 | 1.0 | 1.0 | | 4.5251 | 9.0 | 6183 | 4.2670 | 1.0 | 1.0 | | 4.4527 | 10.0 | 6870 | 4.2087 | 1.0 | 1.0 | | 4.3731 | 11.0 | 7557 | 4.1950 | 0.9982 | 0.9997 | | 4.3461 | 12.0 | 8244 | 4.2287 | 0.9928 | 0.9988 | | 4.3224 | 13.0 | 8931 | 4.1565 | 0.9802 | 0.9971 | | 4.2504 | 14.0 | 9618 | 4.1254 | 0.9619 | 0.9937 | | 4.2196 | 15.0 | 10305 | 4.0377 | 0.9562 | 0.9913 | | 4.1911 | 16.0 | 10992 | 4.0576 | 0.9601 | 0.9887 | | 4.1079 | 17.0 | 11679 | 4.0630 | 0.9544 | 0.9857 | | 4.1117 | 18.0 | 12366 | 4.0009 | 0.9558 | 0.9880 | | 4.0324 | 19.0 | 13053 | 3.9245 | 0.9540 | 0.9877 | | 3.9871 | 20.0 | 13740 | 3.9199 | 0.9540 | 0.9868 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
ScandinavianMrT/gpt2_supervised_SARC_3epochs_withcontext
0cca0d1b5aee0ce4fa577eef9ced7f3c3103df07
2022-03-15T17:08:24.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-generation
false
ScandinavianMrT
null
ScandinavianMrT/gpt2_supervised_SARC_3epochs_withcontext
5
null
transformers
16,957
--- license: mit tags: - generated_from_trainer model-index: - name: gpt2_supervised_SARC_3epochs_withcontext 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. --> # gpt2_supervised_SARC_3epochs_withcontext This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0949 ## Model description More information needed ## Intended uses & 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 | |:-------------:|:-----:|:-----:|:---------------:| | 3.185 | 1.0 | 16989 | 3.1178 | | 3.1342 | 2.0 | 33978 | 3.1008 | | 3.1062 | 3.0 | 50967 | 3.0949 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6
bitsanlp/Homophobia-Transphobia-v2-mBERT-EDA
217eec3a25cbc122d372c5f801177e46bc731a13
2022-03-15T17:31:42.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
bitsanlp
null
bitsanlp/Homophobia-Transphobia-v2-mBERT-EDA
5
null
transformers
16,958
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: Homophobia-Transphobia-v2-mBERT-EDA 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. --> # Homophobia-Transphobia-v2-mBERT-EDA This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5401 - Accuracy: 0.9317 - F1: 0.4498 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.1699 | 1.0 | 189 | 0.4125 | 0.9229 | 0.4634 | | 0.0387 | 2.0 | 378 | 0.4658 | 0.9229 | 0.3689 | | 0.0148 | 3.0 | 567 | 0.5250 | 0.9355 | 0.4376 | | 0.0005 | 4.0 | 756 | 0.5336 | 0.9317 | 0.4531 | | 0.0016 | 5.0 | 945 | 0.5401 | 0.9317 | 0.4498 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.4 - Tokenizers 0.11.6
csclarke/MARS-Encoder
95b6f74bd5f787d56ec7b0bc3b7397fdda9023af
2022-03-16T00:36:53.000Z
[ "pytorch", "roberta", "text-classification", "transformers", "license:cc" ]
text-classification
false
csclarke
null
csclarke/MARS-Encoder
5
null
transformers
16,959
--- license: cc --- # MARS Encoder for Multi-agent Response Selection This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class and is the model used in the paper [One Agent To Rule Them All: Towards Multi-agent Conversational AI](https://csclarke.com/assets/pdf/ACL_2022.pdf). ## Training Data This model was trained on the [BBAI dataset](https://github.com/ChrisIsKing/black-box-multi-agent-integation/tree/main/data). The model will predict a score between 0 and 1 ranking the correctness of a response to a user question from a conversational agent. ## Usage and Performance Pre-trained models can be used like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('csclarke/MARS-Encoder') scores = model.predict([('question 1', 'response 1'), ('question 1', 'response 2')]) ``` The model will predict scores for the pairs `('question 1', 'response 1')` and `('question 1', 'response 2')`. You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class
clapika2010/rayyan_predictions
8eef0be48a59cfd53ef62e88d40a365c62ba77ba
2022-03-16T06:23:43.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
clapika2010
null
clapika2010/rayyan_predictions
5
null
transformers
16,960
Entry not found
PSW/speaker-change-bart-samsum
7b18a696658d8ca05f932db8e1a6c8abb5ef44d2
2022-03-16T01:34:31.000Z
[ "pytorch", "bart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
PSW
null
PSW/speaker-change-bart-samsum
5
null
transformers
16,961
Entry not found
aws-ai/vascl-roberta-base
50dff1759d2d12e6e3eec09f1e9f50a6ab56928b
2022-03-16T04:22:10.000Z
[ "pytorch", "roberta", "transformers", "license:apache-2.0" ]
null
false
aws-ai
null
aws-ai/vascl-roberta-base
5
null
transformers
16,962
--- license: apache-2.0 ---
cambridgeltl/guardian_news_electra_small
c5fd8b2fe2cebbf70c9f3178e1409397bab5a216
2022-03-16T10:32:03.000Z
[ "pytorch", "electra", "text-classification", "transformers" ]
text-classification
false
cambridgeltl
null
cambridgeltl/guardian_news_electra_small
5
null
transformers
16,963
Entry not found
anton-l/xtreme_s_xlsr_minds14_upd
c204f31b6c99dce29eb278ccebbcc78cb8d5378c
2022-03-16T11:52:27.000Z
[ "pytorch", "tensorboard", "wav2vec2", "audio-classification", "dataset:xtreme_s", "transformers", "minds14", "google/xtreme_s", "generated_from_trainer", "license:apache-2.0", "model-index" ]
audio-classification
false
anton-l
null
anton-l/xtreme_s_xlsr_minds14_upd
5
null
transformers
16,964
--- license: apache-2.0 tags: - minds14 - google/xtreme_s - generated_from_trainer datasets: - xtreme_s metrics: - f1 - accuracy model-index: - name: xtreme_s_xlsr_minds14_upd 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. --> # xtreme_s_xlsr_minds14_upd This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - MINDS14.FR-FR dataset. It achieves the following results on the evaluation set: - Loss: 2.6303 - F1: 0.0223 - Accuracy: 0.0833 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 64 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4.dev0 - Tokenizers 0.11.6
Rustem/roberta-base-trained-50k-docs
579bbb1d54fd620949f64238dc23b54f2a4462f6
2022-03-16T12:38:46.000Z
[ "pytorch", "roberta", "text-classification", "transformers", "license:apache-2.0" ]
text-classification
false
Rustem
null
Rustem/roberta-base-trained-50k-docs
5
null
transformers
16,965
--- license: apache-2.0 ---
ScandinavianMrT/distilbert-IMDB-POS
6a1810c20a91f42e3c1abb62c1e4d3a50b7210d4
2022-03-16T18:15:20.000Z
[ "pytorch", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
ScandinavianMrT
null
ScandinavianMrT/distilbert-IMDB-POS
5
null
transformers
16,966
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-IMDB 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-IMDB 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: 0.1905 - Accuracy: 0.9295 ## Model description More information needed ## Intended uses & 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1928 | 1.0 | 2000 | 0.1905 | 0.9295 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
ScandinavianMrT/distilbert-SARC_withcontext_3.0
cf97125be65f960ad30b30144c35b6a36b8ec9e5
2022-03-16T20:03:20.000Z
[ "pytorch", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
ScandinavianMrT
null
ScandinavianMrT/distilbert-SARC_withcontext_3.0
5
null
transformers
16,967
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-SARC_withcontext_3.0 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-SARC_withcontext_3.0 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 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 ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
KoichiYasuoka/roberta-small-belarusian
55acabfaccc375eb04e38cd89efeff44ec66a5ca
2022-03-17T07:58:19.000Z
[ "pytorch", "roberta", "fill-mask", "be", "dataset:cc100", "transformers", "belarusian", "masked-lm", "license:cc-by-sa-4.0", "autotrain_compatible" ]
fill-mask
false
KoichiYasuoka
null
KoichiYasuoka/roberta-small-belarusian
5
null
transformers
16,968
--- language: - "be" tags: - "belarusian" - "masked-lm" license: "cc-by-sa-4.0" datasets: - "cc100" pipeline_tag: "fill-mask" mask_token: "[MASK]" --- # roberta-small-belarusian ## Model Description This is a RoBERTa model pre-trained on [CC-100](https://data.statmt.org/cc-100/). You can fine-tune `roberta-small-belarusian` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-small-belarusian-upos), dependency-parsing, and so on. ## How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-small-belarusian") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-small-belarusian") ```
cambridgeltl/guardian_news_electra_base
103d92e5752b2caba814bf5b9bc879e5b7c74d1b
2022-03-17T09:34:31.000Z
[ "pytorch", "electra", "text-classification", "transformers" ]
text-classification
false
cambridgeltl
null
cambridgeltl/guardian_news_electra_base
5
null
transformers
16,969
Entry not found
taehyunzzz/distilbert-base-uncased-finetuned-ner
141e4069dc9c6136a01e3e12b81f0159d9edbde5
2022-03-17T10:46:16.000Z
[ "pytorch", "tensorboard", "distilbert", "token-classification", "dataset:conll2003", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
taehyunzzz
null
taehyunzzz/distilbert-base-uncased-finetuned-ner
5
null
transformers
16,970
--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9032328767123288 - name: Recall type: recall value: 0.9220270723794608 - name: F1 type: f1 value: 0.912533215234721 - name: Accuracy type: accuracy value: 0.979951387675346 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0722 - Precision: 0.9032 - Recall: 0.9220 - F1: 0.9125 - Accuracy: 0.9800 ## Model description More information needed ## Intended uses & 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 220 | 0.0974 | 0.8663 | 0.8865 | 0.8763 | 0.9735 | | No log | 2.0 | 440 | 0.0754 | 0.8947 | 0.9176 | 0.9060 | 0.9790 | | 0.1921 | 3.0 | 660 | 0.0722 | 0.9032 | 0.9220 | 0.9125 | 0.9800 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2 - Datasets 2.0.0 - Tokenizers 0.11.6
facebook/regnet-y-008
8afb013500166812b7b3fcdc04f75062fc3a6894
2022-06-30T10:21:48.000Z
[ "pytorch", "tf", "regnet", "image-classification", "dataset:imagenet-1k", "arxiv:2003.13678", "transformers", "vision", "license:apache-2.0" ]
image-classification
false
facebook
null
facebook/regnet-y-008
5
null
transformers
16,971
--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # RegNet RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls). Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/regnet_architecture.png) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python >>> from transformers import AutoFeatureExtractor, RegNetForImageClassification >>> import torch >>> from datasets import load_dataset >>> dataset = load_dataset("huggingface/cats-image") >>> image = dataset["test"]["image"][0] >>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040") >>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040") >>> inputs = feature_extractor(image, return_tensors="pt") >>> with torch.no_grad(): ... logits = model(**inputs).logits >>> # model predicts one of the 1000 ImageNet classes >>> predicted_label = logits.argmax(-1).item() >>> print(model.config.id2label[predicted_label]) 'tabby, tabby cat' ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).
ScandinavianMrT/distilbert-IMDB-NEG
6c6fad72459086ad9bfd923a18079f6035e640dd
2022-03-18T16:43:11.000Z
[ "pytorch", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
ScandinavianMrT
null
ScandinavianMrT/distilbert-IMDB-NEG
5
null
transformers
16,972
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-IMDB-NEG 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-IMDB-NEG 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: 0.1871 - Accuracy: 0.9346 ## Model description More information needed ## Intended uses & 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1865 | 1.0 | 2000 | 0.1871 | 0.9346 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
Rustem/roberta-base-best
4658858b2f4de6e3150177644200c21b490014db
2022-03-18T23:14:57.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
Rustem
null
Rustem/roberta-base-best
5
null
transformers
16,973
Entry not found
ShengdingHu/CAPITALIZE_T5-LowRankAdapter
136570061af98f96e305d7cc3212062e5158fa03
2022-03-19T17:41:42.000Z
[ "pytorch", "transformers" ]
null
false
ShengdingHu
null
ShengdingHu/CAPITALIZE_T5-LowRankAdapter
5
null
transformers
16,974
Entry not found
ShengdingHu/Capitalize_T5-LoRA
60cf82b08f5ad24d03d1cb39489e81f2939f3af0
2022-03-19T18:48:58.000Z
[ "pytorch", "transformers" ]
null
false
ShengdingHu
null
ShengdingHu/Capitalize_T5-LoRA
5
null
transformers
16,975
Entry not found
Ketzu/koelectra-sts-v0.6
a6fa4782965a009e8281049ae5a01259477615a5
2022-03-22T13:18:11.000Z
[ "pytorch", "tensorboard", "electra", "text-classification", "transformers", "generated_from_trainer", "model-index" ]
text-classification
false
Ketzu
null
Ketzu/koelectra-sts-v0.6
5
null
transformers
16,976
--- tags: - generated_from_trainer metrics: - spearmanr model-index: - name: koelectra-sts-v0.6 results: - task: name: Text Classification type: text-classification metrics: - name: Spearmanr type: spearmanr value: 0.8698381401893762 --- <!-- 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. --> # koelectra-sts-v0.6 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0059 - Pearson: 0.9988 - Spearmanr: 0.8698 ## Model description More information needed ## Intended uses & 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 | Pearson | Spearmanr | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:---------:| | 0.0036 | 1.0 | 6250 | 0.0082 | 0.9983 | 0.8698 | | 0.0038 | 2.0 | 12500 | 0.0065 | 0.9986 | 0.8697 | | 0.0105 | 3.0 | 18750 | 0.0071 | 0.9985 | 0.8698 | | 0.0008 | 4.0 | 25000 | 0.0059 | 0.9988 | 0.8698 | | 0.0008 | 5.0 | 31250 | 0.0059 | 0.9988 | 0.8698 | ### Framework versions - Transformers 4.10.0 - Pytorch 1.10.1+cu113 - Datasets 1.17.0 - Tokenizers 0.10.3
beston91/gpt2-xl_ft_logits_10k
77dcd18c485e3bbb76a35f930e9666381da838c7
2022-03-24T05:04:35.000Z
[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "model-index" ]
text-generation
false
beston91
null
beston91/gpt2-xl_ft_logits_10k
5
null
transformers
16,977
--- tags: - generated_from_trainer model-index: - name: gpt2-xl_ft_logits_10k 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. --> # gpt2-xl_ft_logits_10k This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.3791 ## Model description More information needed ## Intended uses & 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-07 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100.0 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.99 | 54 | 6.1576 | | No log | 1.99 | 108 | 6.2663 | | No log | 2.99 | 162 | 6.3520 | | No log | 3.99 | 216 | 6.3791 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
sanchit-gandhi/wav2vec2-2-roberta-no-adapter-regularisation
8c0ea7fedc5b488b32246c81be831c18c4bab6c2
2022-03-22T09:45:38.000Z
[ "pytorch", "tensorboard", "speech-encoder-decoder", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
sanchit-gandhi
null
sanchit-gandhi/wav2vec2-2-roberta-no-adapter-regularisation
5
null
transformers
16,978
Entry not found
claytonsamples/distilbert-base-uncased-finetuned-emotion
d11d27f570b12c4cdbd0db0dfa9125b8c24c2498
2022-03-21T03:56:58.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers" ]
text-classification
false
claytonsamples
null
claytonsamples/distilbert-base-uncased-finetuned-emotion
5
null
transformers
16,979
Entry not found
cammy/PRIMERA-100-MDS-own2
5b347294d13904bcfab0d6d2a0b265524c5543e2
2022-03-21T04:41:09.000Z
[ "pytorch", "tensorboard", "led", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
cammy
null
cammy/PRIMERA-100-MDS-own2
5
null
transformers
16,980
Entry not found
ScandinavianMrT/distilbert_ONION_1epoch
f0f57a61a31cd1ff0a370b6c0489ab021516af38
2022-03-21T15:06:40.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers" ]
text-classification
false
ScandinavianMrT
null
ScandinavianMrT/distilbert_ONION_1epoch
5
null
transformers
16,981
Entry not found
mimicheng/codeparrot-ds
e51b27e27ea9b28ea51a99709c9256744023bf0c
2022-03-22T03:45:36.000Z
[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-generation
false
mimicheng
null
mimicheng/codeparrot-ds
5
null
transformers
16,982
--- license: mit tags: - generated_from_trainer model-index: - name: codeparrot-ds results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # codeparrot-ds This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 1.7397 - eval_runtime: 603.8598 - eval_samples_per_second: 154.281 - eval_steps_per_second: 4.822 - epoch: 0.08 - step: 5000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES
5a2bddd46579f735c53982ca1f48ea02f4a51dd7
2022-03-21T22:25:59.000Z
[ "pytorch", "tensorboard", "roberta", "token-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
StivenLancheros
null
StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES
5
null
transformers
16,983
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES 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-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on the CRAFT dataset. It achieves the following results on the evaluation set: - Loss: 0.2224 - Precision: 0.8298 - Recall: 0.8306 - F1: 0.8302 - Accuracy: 0.9659 ## Model description This model performs Named Entity Recognition for 6 entity tags: Sequence, Cell, Protein, Gene, Taxon, and Chemical from the CRAFT(Colorado Richly Annotated Full Text) Corpus in English. Entity tags have been normalized and replaced from the original three letter code to a full name e.g. B-Protein, I-Chemical. This model is trained on augmented data created using Entity Replacement. 20% of the entities were replaced using a list of entities for each entity tag obtained from the official ontologies for each entity class. Three datasets (original, augmented, MT translated CRAFT) were concatenated. ## Intended uses & 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0624 | 1.0 | 4078 | 0.1844 | 0.8002 | 0.7923 | 0.7963 | 0.9607 | | 0.0284 | 2.0 | 8156 | 0.1937 | 0.8394 | 0.7988 | 0.8186 | 0.9637 | | 0.0118 | 3.0 | 12234 | 0.2007 | 0.8285 | 0.8232 | 0.8258 | 0.9649 | | 0.0043 | 4.0 | 16312 | 0.2224 | 0.8298 | 0.8306 | 0.8302 | 0.9659 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
danyaljj/gpt-j-6B-step-383500
2e05b8303ea9490a8f9de37df763d34d3ce424e7
2022-03-22T23:12:19.000Z
[ "pytorch", "gptj", "text-generation", "transformers" ]
text-generation
false
danyaljj
null
danyaljj/gpt-j-6B-step-383500
5
null
transformers
16,984
Entry not found
edmz/distilbert-base-uncased-finetuned-ner
883087cba003e84f215451c8ad32a2f56f37c67f
2022-03-22T09:56:14.000Z
[ "pytorch", "tensorboard", "distilbert", "token-classification", "dataset:conll2003", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
edmz
null
edmz/distilbert-base-uncased-finetuned-ner
5
null
transformers
16,985
--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9247134038800705 - name: Recall type: recall value: 0.9384718648618414 - name: F1 type: f1 value: 0.9315418355449449 - name: Accuracy type: accuracy value: 0.9836529143565221 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0612 - Precision: 0.9247 - Recall: 0.9385 - F1: 0.9315 - Accuracy: 0.9837 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2421 | 1.0 | 878 | 0.0701 | 0.9083 | 0.9217 | 0.9149 | 0.9801 | | 0.0555 | 2.0 | 1756 | 0.0599 | 0.9204 | 0.9357 | 0.9280 | 0.9830 | | 0.0311 | 3.0 | 2634 | 0.0612 | 0.9247 | 0.9385 | 0.9315 | 0.9837 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
PSW/ut_del_three_per_each_ver1
914ba1d439aefed653f4171a1d03c5b5adc98057
2022-03-22T14:26:01.000Z
[ "pytorch", "bart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
PSW
null
PSW/ut_del_three_per_each_ver1
5
null
transformers
16,986
Entry not found
vinaykudari/t5-acled-t2s
9b7597e67017095f19f02f915312444b1a8dd32b
2022-05-09T14:54:42.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
vinaykudari
null
vinaykudari/t5-acled-t2s
5
null
transformers
16,987
Entry not found
gayanin/bart-med-term-conditional-masking
cec6198b3d2fd17f1416dc3236e26020bd17aa61
2022-03-23T19:06:03.000Z
[ "pytorch", "tensorboard", "bart", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
gayanin
null
gayanin/bart-med-term-conditional-masking
5
null
transformers
16,988
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-med-term-conditional-masking results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-med-term-conditional-masking This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5115 - Rouge2 Precision: 0.7409 - Rouge2 Recall: 0.5343 - Rouge2 Fmeasure: 0.6025 ## Model description More information needed ## Intended uses & 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: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.6278 | 1.0 | 15827 | 0.5546 | 0.7255 | 0.5244 | 0.5908 | | 0.5356 | 2.0 | 31654 | 0.5286 | 0.7333 | 0.5293 | 0.5966 | | 0.4757 | 3.0 | 47481 | 0.5154 | 0.7376 | 0.532 | 0.5998 | | 0.4337 | 4.0 | 63308 | 0.5107 | 0.7406 | 0.5342 | 0.6023 | | 0.4045 | 5.0 | 79135 | 0.5115 | 0.7409 | 0.5343 | 0.6025 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
ScandinavianMrT/distilbert_ONION_3epoch
4dfa0918608ee174b2da35c7d4b41f444dce42b7
2022-03-23T15:02:16.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers" ]
text-classification
false
ScandinavianMrT
null
ScandinavianMrT/distilbert_ONION_3epoch
5
null
transformers
16,989
Entry not found
Zohar/distilgpt2-finetuned-hotel-reviews
45046a43f7dc9dac8c4f4addd8fdb14a8ca6ea1e
2022-03-23T18:42:18.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-generation
false
Zohar
null
Zohar/distilgpt2-finetuned-hotel-reviews
5
null
transformers
16,990
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-hotel-reviews 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. --> # distilgpt2-finetuned-hotel-reviews This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6253 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7533 | 1.0 | 1259 | 3.6803 | | 3.6644 | 2.0 | 2518 | 3.6366 | | 3.6426 | 3.0 | 3777 | 3.6253 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cu102 - Datasets 1.18.2 - Tokenizers 0.11.0
ScandinavianMrT/distilbert_ONION_1epoch_2.0
a42ec13b704de443c4c31cf984aec8a295059aba
2022-03-23T18:30:14.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers" ]
text-classification
false
ScandinavianMrT
null
ScandinavianMrT/distilbert_ONION_1epoch_2.0
5
null
transformers
16,991
Entry not found
huggingtweets/radagasttbrown
29ef5189032f5ce62b0ccb9df7fa1d500bc6c0f5
2022-03-23T21:33:16.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/radagasttbrown
5
null
transformers
16,992
--- language: en thumbnail: http://www.huggingtweets.com/radagasttbrown/1648071147429/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(&#39;https://pbs.twimg.com/profile_images/1362404255798280192/yIKMf5AN_400x400.png&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </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">Radagast 🌋</div> <div style="text-align: center; font-size: 14px;">@radagasttbrown</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 Radagast 🌋. | Data | Radagast 🌋 | | --- | --- | | Tweets downloaded | 3228 | | Retweets | 457 | | Short tweets | 230 | | Tweets kept | 2541 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1b1t67ko/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 @radagasttbrown's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/boipgvkp) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/boipgvkp/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/radagasttbrown') 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)
yy642/bert-base-uncased-finetuned-mnli-max-length-32-epoch-1
b84b147a8513514adf169cbc0e48330e2affb216
2022-03-23T22:33:11.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers" ]
text-classification
false
yy642
null
yy642/bert-base-uncased-finetuned-mnli-max-length-32-epoch-1
5
1
transformers
16,993
Entry not found
radev/pegasus-samsum
6dfeef4cc0c82b837ab787b899b5575c74e1d269
2022-07-04T15:38:01.000Z
[ "pytorch", "tensorboard", "pegasus", "text2text-generation", "dataset:samsum", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
text2text-generation
false
radev
null
radev/pegasus-samsum
5
null
transformers
16,994
--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3
ademarcarneiro/distilbert-base-uncased-finetuned-emotion
fc2d62513c3616837266ee1e3aa926d8ed0fc24d
2022-03-24T03:20:32.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers" ]
text-classification
false
ademarcarneiro
null
ademarcarneiro/distilbert-base-uncased-finetuned-emotion
5
null
transformers
16,995
Entry not found
Helsinki-NLP/opus-mt-tc-base-uk-fi
829baaf04fcd60145ec90e7f6daebd99b12d4d68
2022-06-01T13:10:14.000Z
[ "pytorch", "marian", "text2text-generation", "fi", "uk", "transformers", "translation", "opus-mt-tc", "license:cc-by-4.0", "model-index", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-tc-base-uk-fi
5
null
transformers
16,996
--- language: - fi - uk tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-base-uk-fi results: - task: name: Translation ukr-fin type: translation args: ukr-fin dataset: name: flores101-devtest type: flores_101 args: ukr fin devtest metrics: - name: BLEU type: bleu value: 19.6 --- # opus-mt-tc-base-uk-fi Neural machine translation model for translating from Ukrainian (uk) to Finnish (fi). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Model info * Release: 2022-03-17 * source language(s): ukr * target language(s): fin * model: transformer-align * data: opusTCv20210807+pft+pbt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+pft+pbt_transformer-align_2022-03-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-fin/opusTCv20210807+pft+pbt_transformer-align_2022-03-17.zip) * more information released models: [OPUS-MT ukr-fin README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-fin/README.md) ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Африка є колискою людства.", "Один, два, три, чотири, п'ять, шість, сім, вісім, дев'ять, десять." ] model_name = "pytorch-models/opus-mt-tc-base-uk-fi" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Afrikka on ihmiskunnan kehto. # Yksi, kaksi, kolme, neljä, viisi, kuusi, seitsemän, kahdeksan, yhdeksän, kymmenen. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-uk-fi") print(pipe("Африка є колискою людства.")) # expected output: Afrikka on ihmiskunnan kehto. ``` ## Benchmarks * test set translations: [opusTCv20210807+pft+pbt_transformer-align_2022-03-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-fin/opusTCv20210807+pft+pbt_transformer-align_2022-03-17.test.txt) * test set scores: [opusTCv20210807+pft+pbt_transformer-align_2022-03-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-fin/opusTCv20210807+pft+pbt_transformer-align_2022-03-17.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | ukr-fin | flores101-devtest | 0.54827 | 19.6 | 1012 | 18781 | ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 1bdabf7 * port time: Thu Mar 24 09:10:42 EET 2022 * port machine: LM0-400-22516.local
athiban2001/cord-scibert
50d2e6a9ca0efb5d511d5e6df94948a3817327a8
2022-03-25T05:17:09.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "license:mit", "autotrain_compatible" ]
fill-mask
false
athiban2001
null
athiban2001/cord-scibert
5
null
transformers
16,997
--- license: mit ---
elihoole/distilgpt2-music-search
4003f257348c26832ccb3aa2380c276372df0660
2022-03-24T08:17:25.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-generation
false
elihoole
null
elihoole/distilgpt2-music-search
5
null
transformers
16,998
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-music-search 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. --> # distilgpt2-music-search This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.6516 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 256 | 4.6572 | | 5.0184 | 2.0 | 512 | 4.6461 | | 5.0184 | 3.0 | 768 | 4.6516 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.7.1 - Datasets 2.0.0 - Tokenizers 0.11.6
Helsinki-NLP/opus-mt-tc-base-zle-bat
7340f670bf424c307a2f551bba1526f5059652e4
2022-06-01T13:09:59.000Z
[ "pytorch", "marian", "text2text-generation", "bat", "lt", "lv", "ru", "zle", "transformers", "translation", "opus-mt-tc", "license:cc-by-4.0", "model-index", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-tc-base-zle-bat
5
null
transformers
16,999
--- language: - bat - lt - lv - ru - zle tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-base-zle-bat results: - task: name: Translation rus-lav type: translation args: rus-lav dataset: name: flores101-devtest type: flores_101 args: rus lav devtest metrics: - name: BLEU type: bleu value: 20.0 - task: name: Translation rus-lit type: translation args: rus-lit dataset: name: flores101-devtest type: flores_101 args: rus lit devtest metrics: - name: BLEU type: bleu value: 20.6 - task: name: Translation ukr-lav type: translation args: ukr-lav dataset: name: flores101-devtest type: flores_101 args: ukr lav devtest metrics: - name: BLEU type: bleu value: 21.4 - task: name: Translation ukr-lit type: translation args: ukr-lit dataset: name: flores101-devtest type: flores_101 args: ukr lit devtest metrics: - name: BLEU type: bleu value: 20.5 - task: name: Translation rus-lav type: translation args: rus-lav dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: rus-lav metrics: - name: BLEU type: bleu value: 55.3 - task: name: Translation rus-lit type: translation args: rus-lit dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: rus-lit metrics: - name: BLEU type: bleu value: 47.2 --- # opus-mt-tc-base-zle-bat Neural machine translation model for translating from East Slavic languages (zle) to Baltic languages (bat). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Model info * Release: 2022-03-14 * source language(s): rus * target language(s): lav lit * valid target language labels: >>lav<< >>lit<< * model: transformer-align * data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807_transformer-align_2022-03-14.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-bat/opusTCv20210807_transformer-align_2022-03-14.zip) * more information released models: [OPUS-MT zle-bat README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-bat/README.md) * more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian) This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>lav<<` ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>lav<< Африка - колыбель человечества.", ">>lit<< Том — наш капітан." ] model_name = "pytorch-models/opus-mt-tc-base-zle-bat" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Āfrika ir cilvēces šūpulis. # Tomas yra mūsų kapitonas. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-zle-bat") print(pipe(">>lav<< Африка - колыбель человечества.")) # expected output: Āfrika ir cilvēces šūpulis. ``` ## Benchmarks * test set translations: [opusTCv20210807_transformer-align_2022-03-14.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-bat/opusTCv20210807_transformer-align_2022-03-14.test.txt) * test set scores: [opusTCv20210807_transformer-align_2022-03-14.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-bat/opusTCv20210807_transformer-align_2022-03-14.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | rus-lav | tatoeba-test-v2021-08-07 | 0.74223 | 55.3 | 274 | 1518 | | rus-lit | tatoeba-test-v2021-08-07 | 0.70795 | 47.2 | 3598 | 20662 | | rus-lav | flores101-devtest | 0.50134 | 20.0 | 1012 | 22092 | | rus-lit | flores101-devtest | 0.53732 | 20.6 | 1012 | 20695 | | ukr-lav | flores101-devtest | 0.51379 | 21.4 | 1012 | 22092 | | ukr-lit | flores101-devtest | 0.54085 | 20.5 | 1012 | 20695 | ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 1bdabf7 * port time: Wed Mar 23 22:11:57 EET 2022 * port machine: LM0-400-22516.local