Split (1)

train · 880k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-14 06:27:53	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 519 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-14 06:27:45	card stringlengths 11 1.01M
ali2066/finetuned_token_itr0_0.0002_editorials_16_02_2022-21_07_38	ali2066	2022-02-16T20:08:50Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_itr0_0.0002_editorials_16_02_2022-21_07_38 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_token_itr0_0.0002_editorials_16_02_2022-21_07_38 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: 0.1146 - Precision: 0.4662 - Recall: 0.4718 - F1: 0.4690 - Accuracy: 0.9773 ## Model description More information needed ## Intended uses & 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: 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 \| 15 \| 0.0756 \| 0.2960 \| 0.4505 \| 0.3573 \| 0.9775 \| \| No log \| 2.0 \| 30 \| 0.0626 \| 0.3615 \| 0.4231 \| 0.3899 \| 0.9808 \| \| No log \| 3.0 \| 45 \| 0.0602 \| 0.4898 \| 0.5275 \| 0.5079 \| 0.9833 \| \| No log \| 4.0 \| 60 \| 0.0719 \| 0.5517 \| 0.5275 \| 0.5393 \| 0.9849 \| \| No log \| 5.0 \| 75 \| 0.0754 \| 0.5765 \| 0.5385 \| 0.5568 \| 0.9849 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_token_itr0_2e-05_editorials_16_02_2022-21_05_05	ali2066	2022-02-16T20:06:17Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_itr0_2e-05_editorials_16_02_2022-21_05_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_token_itr0_2e-05_editorials_16_02_2022-21_05_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: 0.1114 - Precision: 0.0637 - Recall: 0.0080 - F1: 0.0141 - Accuracy: 0.9707 ## Model description More information needed ## Intended uses & 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 \| 15 \| 0.0921 \| 0.08 \| 0.0110 \| 0.0193 \| 0.9801 \| \| No log \| 2.0 \| 30 \| 0.0816 \| 0.08 \| 0.0110 \| 0.0193 \| 0.9801 \| \| No log \| 3.0 \| 45 \| 0.0781 \| 0.08 \| 0.0110 \| 0.0193 \| 0.9801 \| \| No log \| 4.0 \| 60 \| 0.0746 \| 0.08 \| 0.0110 \| 0.0193 \| 0.9801 \| \| No log \| 5.0 \| 75 \| 0.0737 \| 0.08 \| 0.0110 \| 0.0193 \| 0.9801 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_token_itr0_2e-05_essays_16_02_2022-21_01_51	ali2066	2022-02-16T20:02:54Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_itr0_2e-05_essays_16_02_2022-21_01_51 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_token_itr0_2e-05_essays_16_02_2022-21_01_51 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: 0.2525 - Precision: 0.3997 - Recall: 0.5117 - F1: 0.4488 - Accuracy: 0.9115 ## Model description More information needed ## Intended uses & 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 \| 11 \| 0.4652 \| 0.1528 \| 0.3588 \| 0.2144 \| 0.7851 \| \| No log \| 2.0 \| 22 \| 0.3646 \| 0.2913 \| 0.4847 \| 0.3639 \| 0.8521 \| \| No log \| 3.0 \| 33 \| 0.3453 \| 0.3789 \| 0.5611 \| 0.4523 \| 0.8708 \| \| No log \| 4.0 \| 44 \| 0.3270 \| 0.3673 \| 0.5496 \| 0.4404 \| 0.8729 \| \| No log \| 5.0 \| 55 \| 0.3268 \| 0.4011 \| 0.5725 \| 0.4717 \| 0.8760 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_token_itr0_3e-05_webDiscourse_16_02_2022-20_59_50	ali2066	2022-02-16T20:00:45Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_itr0_3e-05_webDiscourse_16_02_2022-20_59_50 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_token_itr0_3e-05_webDiscourse_16_02_2022-20_59_50 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: 0.5450 - Precision: 0.0049 - Recall: 0.0146 - F1: 0.0074 - Accuracy: 0.7431 ## Model description More information needed ## Intended uses & 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: 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 \| 0.6830 \| 0.0109 \| 0.0323 \| 0.0163 \| 0.5685 \| \| No log \| 2.0 \| 20 \| 0.7187 \| 0.0256 \| 0.0323 \| 0.0286 \| 0.5668 \| \| No log \| 3.0 \| 30 \| 0.6839 \| 0.0076 \| 0.0484 \| 0.0131 \| 0.5848 \| \| No log \| 4.0 \| 40 \| 0.6988 \| 0.0092 \| 0.0484 \| 0.0155 \| 0.5918 \| \| No log \| 5.0 \| 50 \| 0.7055 \| 0.0100 \| 0.0484 \| 0.0165 \| 0.5946 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_token_itr0_2e-05_webDiscourse_16_02_2022-20_58_45	ali2066	2022-02-16T19:59:45Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_itr0_2e-05_webDiscourse_16_02_2022-20_58_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. --> # finetuned_token_itr0_2e-05_webDiscourse_16_02_2022-20_58_45 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: 0.6373 - Precision: 0.0024 - Recall: 0.0072 - F1: 0.0036 - Accuracy: 0.6329 ## Model description More information needed ## Intended uses & 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 \| 10 \| 0.5913 \| 0.0 \| 0.0 \| 0.0 \| 0.7023 \| \| No log \| 2.0 \| 20 \| 0.5833 \| 0.0 \| 0.0 \| 0.0 \| 0.7062 \| \| No log \| 3.0 \| 30 \| 0.5717 \| 0.0 \| 0.0 \| 0.0 \| 0.7059 \| \| No log \| 4.0 \| 40 \| 0.5696 \| 0.0 \| 0.0 \| 0.0 \| 0.7008 \| \| No log \| 5.0 \| 50 \| 0.5669 \| 0.0 \| 0.0 \| 0.0 \| 0.7010 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
arampacha/wav2vec2-xls-r-300m-hy-cv	arampacha	2022-02-16T19:45:37Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "hy", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - hy-AM license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_8_0 - generated_from_trainer - hy datasets: - common_voice 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 is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HY-AM dataset. It achieves the following results on the evaluation set: - Loss: 0.5891 - Wer: 0.6569 Note: If you aim for best performance use [this model](https://huggingface.co/arampacha/wav2vec2-xls-r-300m-hy). It is trained using noizy student procedure and achieves considerably better results. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1200 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| 9.167 \| 16.67 \| 100 \| 3.5599 \| 1.0 \| \| 3.2645 \| 33.33 \| 200 \| 3.1771 \| 1.0 \| \| 3.1509 \| 50.0 \| 300 \| 3.1321 \| 1.0 \| \| 3.0757 \| 66.67 \| 400 \| 2.8594 \| 1.0 \| \| 2.5274 \| 83.33 \| 500 \| 1.5286 \| 0.9797 \| \| 1.6826 \| 100.0 \| 600 \| 0.8058 \| 0.7974 \| \| 1.2868 \| 116.67 \| 700 \| 0.6713 \| 0.7279 \| \| 1.1262 \| 133.33 \| 800 \| 0.6308 \| 0.7034 \| \| 1.0408 \| 150.0 \| 900 \| 0.6056 \| 0.6745 \| \| 0.9617 \| 166.67 \| 1000 \| 0.5891 \| 0.6569 \| \| 0.9196 \| 183.33 \| 1100 \| 0.5913 \| 0.6432 \| \| 0.8853 \| 200.0 \| 1200 \| 0.5924 \| 0.6347 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0
ali2066/finetuned_token_itr0_0.0002_all_16_02_2022-20_30_01	ali2066	2022-02-16T19:32:19Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_itr0_0.0002_all_16_02_2022-20_30_01 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_token_itr0_0.0002_all_16_02_2022-20_30_01 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: 0.1577 - Precision: 0.4469 - Recall: 0.5280 - F1: 0.4841 - Accuracy: 0.9513 ## Model description More information needed ## Intended uses & 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: 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 \| 38 \| 0.3553 \| 0.1068 \| 0.0810 \| 0.0922 \| 0.8412 \| \| No log \| 2.0 \| 76 \| 0.2812 \| 0.2790 \| 0.4017 \| 0.3293 \| 0.8684 \| \| No log \| 3.0 \| 114 \| 0.2793 \| 0.3086 \| 0.4586 \| 0.3689 \| 0.8747 \| \| No log \| 4.0 \| 152 \| 0.2766 \| 0.3057 \| 0.4190 \| 0.3535 \| 0.8763 \| \| No log \| 5.0 \| 190 \| 0.2805 \| 0.2699 \| 0.4845 \| 0.3467 \| 0.8793 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_token_itr0_2e-05_all_16_02_2022-20_25_06	ali2066	2022-02-16T19:27:31Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_itr0_2e-05_all_16_02_2022-20_25_06 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_token_itr0_2e-05_all_16_02_2022-20_25_06 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: 0.1778 - Precision: 0.3270 - Recall: 0.3348 - F1: 0.3309 - Accuracy: 0.9439 ## Model description More information needed ## Intended uses & 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 \| 38 \| 0.4023 \| 0.1050 \| 0.2331 \| 0.1448 \| 0.8121 \| \| No log \| 2.0 \| 76 \| 0.3629 \| 0.1856 \| 0.3414 \| 0.2405 \| 0.8368 \| \| No log \| 3.0 \| 114 \| 0.3329 \| 0.1794 \| 0.3594 \| 0.2394 \| 0.8504 \| \| No log \| 4.0 \| 152 \| 0.3261 \| 0.1786 \| 0.3684 \| 0.2405 \| 0.8503 \| \| No log \| 5.0 \| 190 \| 0.3244 \| 0.1872 \| 0.3684 \| 0.2482 \| 0.8534 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_token_itr0_3e-05_all_16_02_2022-20_12_04	ali2066	2022-02-16T19:14:21Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_itr0_3e-05_all_16_02_2022-20_12_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. --> # finetuned_token_itr0_3e-05_all_16_02_2022-20_12_04 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: 0.1620 - Precision: 0.3509 - Recall: 0.3793 - F1: 0.3646 - Accuracy: 0.9468 ## Model description More information needed ## Intended uses & 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: 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 \| 38 \| 0.2997 \| 0.1125 \| 0.2057 \| 0.1454 \| 0.8669 \| \| No log \| 2.0 \| 76 \| 0.2620 \| 0.1928 \| 0.2849 \| 0.2300 \| 0.8899 \| \| No log \| 3.0 \| 114 \| 0.2497 \| 0.1923 \| 0.2906 \| 0.2314 \| 0.8918 \| \| No log \| 4.0 \| 152 \| 0.2474 \| 0.1819 \| 0.3377 \| 0.2365 \| 0.8905 \| \| No log \| 5.0 \| 190 \| 0.2418 \| 0.2128 \| 0.3264 \| 0.2576 \| 0.8997 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
AWTStress/stress_score	AWTStress	2022-02-16T18:44:04Z	9	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- tags: - generated_from_keras_callback model-index: - name: stress_score results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # stress_score This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Datasets 1.18.3 - Tokenizers 0.11.0
Harveenchadha/model-entailment	Harveenchadha	2022-02-16T16:10:23Z	0	0	keras	[ "keras", "tf-keras", "nlp", "region:us" ]	null	2022-03-02T23:29:04Z	--- tags: - nlp library_name: keras --- ## Multimodal entailment Author: Sayak Paul Date created: 2021/08/08 Last modified: 2021/08/15 Description: Training a multimodal model for predicting entailment. ### What is multimodal entailment? On social media platforms, to audit and moderate content we may want to find answers to the following questions in near real-time: Does a given piece of information contradict the other? Does a given piece of information imply the other? In NLP, this task is called analyzing textual entailment. However, that's only when the information comes from text content. In practice, it's often the case the information available comes not just from text content, but from a multimodal combination of text, images, audio, video, etc. Multimodal entailment is simply the extension of textual entailment to a variety of new input modalities.
ali2066/finetuned_token_3e-05_all_16_02_2022-16_25_56	ali2066	2022-02-16T15:29:08Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_3e-05_all_16_02_2022-16_25_56 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_token_3e-05_all_16_02_2022-16_25_56 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: 0.1630 - Precision: 0.3684 - Recall: 0.3714 - F1: 0.3699 - Accuracy: 0.9482 ## Model description More information needed ## Intended uses & 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: 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 \| 38 \| 0.3339 \| 0.1075 \| 0.2324 \| 0.1470 \| 0.8379 \| \| No log \| 2.0 \| 76 \| 0.3074 \| 0.1589 \| 0.2926 \| 0.2060 \| 0.8489 \| \| No log \| 3.0 \| 114 \| 0.2914 \| 0.2142 \| 0.3278 \| 0.2591 \| 0.8591 \| \| No log \| 4.0 \| 152 \| 0.2983 \| 0.1951 \| 0.3595 \| 0.2529 \| 0.8454 \| \| No log \| 5.0 \| 190 \| 0.2997 \| 0.1851 \| 0.3528 \| 0.2428 \| 0.8487 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
mohamed-illiyas/wav2vec2-base-lj-demo-colab	mohamed-illiyas	2022-02-16T15:24:20Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-lj-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-lj-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7050 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---:\| \| No log \| 0.41 \| 20 \| 15.5667 \| 1.0 \| \| No log \| 0.82 \| 40 \| 11.6885 \| 1.0 \| \| 8.569 \| 1.22 \| 60 \| 6.0060 \| 1.0 \| \| 8.569 \| 1.63 \| 80 \| 3.7050 \| 1.0 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
ali2066/finetuned_token_3e-05_all_16_02_2022-16_12_51	ali2066	2022-02-16T15:16:04Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_3e-05_all_16_02_2022-16_12_51 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_token_3e-05_all_16_02_2022-16_12_51 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: 0.1630 - Precision: 0.3684 - Recall: 0.3714 - F1: 0.3699 - Accuracy: 0.9482 ## Model description More information needed ## Intended uses & 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: 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 \| 38 \| 0.3339 \| 0.1075 \| 0.2324 \| 0.1470 \| 0.8379 \| \| No log \| 2.0 \| 76 \| 0.3074 \| 0.1589 \| 0.2926 \| 0.2060 \| 0.8489 \| \| No log \| 3.0 \| 114 \| 0.2914 \| 0.2142 \| 0.3278 \| 0.2591 \| 0.8591 \| \| No log \| 4.0 \| 152 \| 0.2983 \| 0.1951 \| 0.3595 \| 0.2529 \| 0.8454 \| \| No log \| 5.0 \| 190 \| 0.2997 \| 0.1851 \| 0.3528 \| 0.2428 \| 0.8487 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_token_3e-05_all_16_02_2022-16_09_36	ali2066	2022-02-16T15:12:47Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_3e-05_all_16_02_2022-16_09_36 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_token_3e-05_all_16_02_2022-16_09_36 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: 0.1630 - Precision: 0.3684 - Recall: 0.3714 - F1: 0.3699 - Accuracy: 0.9482 ## Model description More information needed ## Intended uses & 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: 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 \| 38 \| 0.3339 \| 0.1075 \| 0.2324 \| 0.1470 \| 0.8379 \| \| No log \| 2.0 \| 76 \| 0.3074 \| 0.1589 \| 0.2926 \| 0.2060 \| 0.8489 \| \| No log \| 3.0 \| 114 \| 0.2914 \| 0.2142 \| 0.3278 \| 0.2591 \| 0.8591 \| \| No log \| 4.0 \| 152 \| 0.2983 \| 0.1951 \| 0.3595 \| 0.2529 \| 0.8454 \| \| No log \| 5.0 \| 190 \| 0.2997 \| 0.1851 \| 0.3528 \| 0.2428 \| 0.8487 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_token_2e-05_all_16_02_2022-16_06_20	ali2066	2022-02-16T15:09:31Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_2e-05_all_16_02_2022-16_06_20 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_token_2e-05_all_16_02_2022-16_06_20 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: 0.1750 - Precision: 0.3286 - Recall: 0.3334 - F1: 0.3310 - Accuracy: 0.9447 ## Model description More information needed ## Intended uses & 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 \| 38 \| 0.3355 \| 0.0975 \| 0.2358 \| 0.1380 \| 0.8361 \| \| No log \| 2.0 \| 76 \| 0.3177 \| 0.1359 \| 0.2709 \| 0.1810 \| 0.8398 \| \| No log \| 3.0 \| 114 \| 0.3000 \| 0.1542 \| 0.3043 \| 0.2047 \| 0.8471 \| \| No log \| 4.0 \| 152 \| 0.3033 \| 0.1589 \| 0.3060 \| 0.2091 \| 0.8434 \| \| No log \| 5.0 \| 190 \| 0.3029 \| 0.1629 \| 0.3110 \| 0.2138 \| 0.8447 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
philschmid/distilbert-onnx	philschmid	2022-02-16T14:51:05Z	57,058	2	transformers	[ "transformers", "onnx", "distilbert", "question-answering", "en", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- language: "en" datasets: - squad metrics: - squad license: apache-2.0 --- # ONNX Conversion of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) # DistilBERT base cased distilled SQuAD This model is a fine-tune checkpoint of [DistilBERT-base-cased](https://huggingface.co/distilbert-base-cased), fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1. This model reaches a F1 score of 87.1 on the dev set (for comparison, BERT bert-base-cased version reaches a F1 score of 88.7).
ali2066/finetuned_token_2e-05_all_16_02_2022-15_48_32	ali2066	2022-02-16T14:50:50Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_2e-05_all_16_02_2022-15_48_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_token_2e-05_all_16_02_2022-15_48_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: 0.1750 - Precision: 0.3286 - Recall: 0.3334 - F1: 0.3310 - Accuracy: 0.9447 ## Model description More information needed ## Intended uses & 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 \| 38 \| 0.3355 \| 0.0975 \| 0.2358 \| 0.1380 \| 0.8361 \| \| No log \| 2.0 \| 76 \| 0.3177 \| 0.1359 \| 0.2709 \| 0.1810 \| 0.8398 \| \| No log \| 3.0 \| 114 \| 0.3000 \| 0.1542 \| 0.3043 \| 0.2047 \| 0.8471 \| \| No log \| 4.0 \| 152 \| 0.3033 \| 0.1589 \| 0.3060 \| 0.2091 \| 0.8434 \| \| No log \| 5.0 \| 190 \| 0.3029 \| 0.1629 \| 0.3110 \| 0.2138 \| 0.8447 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_token_2e-05_all_16_02_2022-15_46_07	ali2066	2022-02-16T14:48:28Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_2e-05_all_16_02_2022-15_46_07 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_token_2e-05_all_16_02_2022-15_46_07 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: 0.1750 - Precision: 0.3286 - Recall: 0.3334 - F1: 0.3310 - Accuracy: 0.9447 ## Model description More information needed ## Intended uses & 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 \| 38 \| 0.3355 \| 0.0975 \| 0.2358 \| 0.1380 \| 0.8361 \| \| No log \| 2.0 \| 76 \| 0.3177 \| 0.1359 \| 0.2709 \| 0.1810 \| 0.8398 \| \| No log \| 3.0 \| 114 \| 0.3000 \| 0.1542 \| 0.3043 \| 0.2047 \| 0.8471 \| \| No log \| 4.0 \| 152 \| 0.3033 \| 0.1589 \| 0.3060 \| 0.2091 \| 0.8434 \| \| No log \| 5.0 \| 190 \| 0.3029 \| 0.1629 \| 0.3110 \| 0.2138 \| 0.8447 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_token_2e-05_all_16_02_2022-15_41_15	ali2066	2022-02-16T14:43:38Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_2e-05_all_16_02_2022-15_41_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. --> # finetuned_token_2e-05_all_16_02_2022-15_41_15 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: 0.1742 - Precision: 0.3447 - Recall: 0.3410 - F1: 0.3428 - Accuracy: 0.9455 ## Model description More information needed ## Intended uses & 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 \| 38 \| 0.3692 \| 0.0868 \| 0.2030 \| 0.1216 \| 0.8238 \| \| No log \| 2.0 \| 76 \| 0.3198 \| 0.1674 \| 0.3029 \| 0.2157 \| 0.8567 \| \| No log \| 3.0 \| 114 \| 0.3156 \| 0.1520 \| 0.3096 \| 0.2039 \| 0.8510 \| \| No log \| 4.0 \| 152 \| 0.3129 \| 0.1753 \| 0.3266 \| 0.2281 \| 0.8500 \| \| No log \| 5.0 \| 190 \| 0.3038 \| 0.1716 \| 0.3401 \| 0.2281 \| 0.8595 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
marcopost-it/biobert-it	marcopost-it	2022-02-16T14:15:27Z	153	1	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	Hi! This model has been trained on Italian biomedical data. For further information, do not hesitate to send me a message! ;) [email protected] (Marco Postiglione)
ali2066/finetuned_token_2e-05_16_02_2022-14_30_32	ali2066	2022-02-16T13:32:52Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_2e-05_16_02_2022-14_30_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_token_2e-05_16_02_2022-14_30_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: 0.1722 - Precision: 0.3378 - Recall: 0.3615 - F1: 0.3492 - Accuracy: 0.9448 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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 \| 38 \| 0.3781 \| 0.1512 \| 0.2671 \| 0.1931 \| 0.8216 \| \| No log \| 2.0 \| 76 \| 0.3020 \| 0.1748 \| 0.2938 \| 0.2192 \| 0.8551 \| \| No log \| 3.0 \| 114 \| 0.2723 \| 0.1938 \| 0.3339 \| 0.2452 \| 0.8663 \| \| No log \| 4.0 \| 152 \| 0.2574 \| 0.2119 \| 0.3506 \| 0.2642 \| 0.8727 \| \| No log \| 5.0 \| 190 \| 0.2521 \| 0.2121 \| 0.3623 \| 0.2676 \| 0.8756 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_token_2e-05_16_02_2022-14_28_10	ali2066	2022-02-16T13:30:28Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_2e-05_16_02_2022-14_28_10 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_token_2e-05_16_02_2022-14_28_10 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: 0.1722 - Precision: 0.3378 - Recall: 0.3615 - F1: 0.3492 - Accuracy: 0.9448 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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 \| 38 \| 0.3781 \| 0.1512 \| 0.2671 \| 0.1931 \| 0.8216 \| \| No log \| 2.0 \| 76 \| 0.3020 \| 0.1748 \| 0.2938 \| 0.2192 \| 0.8551 \| \| No log \| 3.0 \| 114 \| 0.2723 \| 0.1938 \| 0.3339 \| 0.2452 \| 0.8663 \| \| No log \| 4.0 \| 152 \| 0.2574 \| 0.2119 \| 0.3506 \| 0.2642 \| 0.8727 \| \| No log \| 5.0 \| 190 \| 0.2521 \| 0.2121 \| 0.3623 \| 0.2676 \| 0.8756 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_token_2e-05_16_02_2022-14_23_23	ali2066	2022-02-16T13:25:42Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_2e-05_16_02_2022-14_23_23 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_token_2e-05_16_02_2022-14_23_23 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: 0.1722 - Precision: 0.3378 - Recall: 0.3615 - F1: 0.3492 - Accuracy: 0.9448 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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 \| 38 \| 0.3781 \| 0.1512 \| 0.2671 \| 0.1931 \| 0.8216 \| \| No log \| 2.0 \| 76 \| 0.3020 \| 0.1748 \| 0.2938 \| 0.2192 \| 0.8551 \| \| No log \| 3.0 \| 114 \| 0.2723 \| 0.1938 \| 0.3339 \| 0.2452 \| 0.8663 \| \| No log \| 4.0 \| 152 \| 0.2574 \| 0.2119 \| 0.3506 \| 0.2642 \| 0.8727 \| \| No log \| 5.0 \| 190 \| 0.2521 \| 0.2121 \| 0.3623 \| 0.2676 \| 0.8756 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_token_2e-05_16_02_2022-14_15_41	ali2066	2022-02-16T13:18:14Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_2e-05_16_02_2022-14_15_41 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_token_2e-05_16_02_2022-14_15_41 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: 0.1746 - Precision: 0.3191 - Recall: 0.3382 - F1: 0.3284 - Accuracy: 0.9439 ## Model description More information needed ## Intended uses & 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 \| 38 \| 0.2908 \| 0.1104 \| 0.1905 \| 0.1398 \| 0.8731 \| \| No log \| 2.0 \| 76 \| 0.2253 \| 0.1682 \| 0.3206 \| 0.2206 \| 0.9114 \| \| No log \| 3.0 \| 114 \| 0.2041 \| 0.2069 \| 0.3444 \| 0.2585 \| 0.9249 \| \| No log \| 4.0 \| 152 \| 0.1974 \| 0.2417 \| 0.3603 \| 0.2894 \| 0.9269 \| \| No log \| 5.0 \| 190 \| 0.1958 \| 0.2707 \| 0.3683 \| 0.3120 \| 0.9299 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
NeonBohdan/tts-tacotron2-ljspeech-pl	NeonBohdan	2022-02-16T12:18:17Z	0	1	null	[ "license:apache-2.0", "region:us" ]	null	2022-03-02T23:29:04Z	--- license: apache-2.0 ---
joe5campbell/BERT_Tweet_Sentiment_TEST	joe5campbell	2022-02-16T11:03:42Z	7	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: BERT_Tweet_Sentiment_TEST results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # BERT_Tweet_Sentiment_TEST This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5541 - Train Accuracy: 0.9375 - Validation Loss: 0.6546 - Validation Accuracy: 1.0 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.6902 \| 0.625 \| 0.6677 \| 1.0 \| 0 \| \| 0.5541 \| 0.9375 \| 0.6546 \| 1.0 \| 1 \| ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Tokenizers 0.11.0
chaitanya97/wav2vec2-large-xls-r-300m-turkish-colab	chaitanya97	2022-02-16T10:38:44Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-turkish-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 33.1265 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - 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: 5 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---:\| \| 21.4247 \| 4.0 \| 4 \| 33.1265 \| 1.0 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
joe5campbell/BERT_Tweet_Sentiment_100_2epochs	joe5campbell	2022-02-16T10:34:00Z	7	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: BERT_Tweet_Sentiment_100_2epochs results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # BERT_Tweet_Sentiment_100_2epochs This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6279 - Train Accuracy: 0.6824 - Validation Loss: 0.7791 - Validation Accuracy: 0.2667 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.7045 \| 0.4882 \| 0.7236 \| 0.2667 \| 0 \| \| 0.6279 \| 0.6824 \| 0.7791 \| 0.2667 \| 1 \| ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Tokenizers 0.11.0
premrawat/en_ner_skills	premrawat	2022-02-16T09:14:23Z	6	5	spacy	[ "spacy", "token-classification", "en", "model-index", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - spacy - token-classification language: - en model-index: - name: en_ner_skills results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.3980582524 - name: NER Recall type: recall value: 0.3404507711 - name: NER F Score type: f_score value: 0.3670076726 --- \| Feature \| Description \| \| --- \| --- \| \| Name \| `en_ner_skills` \| \| Version \| `0.1.0` \| \| spaCy \| `>=3.2.1,<3.3.0` \| \| Default Pipeline \| `tok2vec`, `ner` \| \| Components \| `tok2vec`, `ner` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| n/a \| \| License \| n/a \| \| Author \| [n/a]() \| ### Label Scheme <details> <summary>View label scheme (1 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `ner` \| `SKILL` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `ENTS_F` \| 36.70 \| \| `ENTS_P` \| 39.81 \| \| `ENTS_R` \| 34.05 \| \| `TOK2VEC_LOSS` \| 607659.90 \| \| `NER_LOSS` \| 491709.76 \|
Minowa/distilbert-base-uncased-finetuned-ner	Minowa	2022-02-16T07:09:20Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:04Z	--- 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: 0.9239501818582607 - name: Recall type: recall value: 0.9378006488421524 - name: F1 type: f1 value: 0.9308238951809905 - name: Accuracy type: accuracy value: 0.9837800054013695 --- <!-- 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: 0.0596 - Precision: 0.9240 - Recall: 0.9378 - F1: 0.9308 - Accuracy: 0.9838 ## Model description More information needed ## Intended uses & 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.2381 \| 1.0 \| 878 \| 0.0707 \| 0.9100 \| 0.9240 \| 0.9170 \| 0.9805 \| \| 0.0563 \| 2.0 \| 1756 \| 0.0583 \| 0.9246 \| 0.9382 \| 0.9314 \| 0.9835 \| \| 0.03 \| 3.0 \| 2634 \| 0.0596 \| 0.9240 \| 0.9378 \| 0.9308 \| 0.9838 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
jatinshah/bert-finetuned-ner	jatinshah	2022-02-16T03:50:43Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9330024813895782 - name: Recall type: recall value: 0.9491753618310333 - name: F1 type: f1 value: 0.9410194377242012 - name: Accuracy type: accuracy value: 0.9861511744275033 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0599 - Precision: 0.9330 - Recall: 0.9492 - F1: 0.9410 - Accuracy: 0.9862 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0852 \| 1.0 \| 1756 \| 0.0647 \| 0.9147 \| 0.9345 \| 0.9245 \| 0.9826 \| \| 0.0305 \| 2.0 \| 3512 \| 0.0599 \| 0.9333 \| 0.9463 \| 0.9398 \| 0.9858 \| \| 0.0212 \| 3.0 \| 5268 \| 0.0599 \| 0.9330 \| 0.9492 \| 0.9410 \| 0.9862 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.9.1 - Datasets 1.18.3 - Tokenizers 0.10.3
ali2066/finetuned_token_2e-05_16_02_2022-01_55_54	ali2066	2022-02-16T01:18:01Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_2e-05_16_02_2022-01_55_54 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_token_2e-05_16_02_2022-01_55_54 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: 0.1722 - Precision: 0.3378 - Recall: 0.3615 - F1: 0.3492 - Accuracy: 0.9448 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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 \| 38 \| 0.3781 \| 0.1512 \| 0.2671 \| 0.1931 \| 0.8216 \| \| No log \| 2.0 \| 76 \| 0.3020 \| 0.1748 \| 0.2938 \| 0.2192 \| 0.8551 \| \| No log \| 3.0 \| 114 \| 0.2723 \| 0.1938 \| 0.3339 \| 0.2452 \| 0.8663 \| \| No log \| 4.0 \| 152 \| 0.2574 \| 0.2119 \| 0.3506 \| 0.2642 \| 0.8727 \| \| No log \| 5.0 \| 190 \| 0.2521 \| 0.2121 \| 0.3623 \| 0.2676 \| 0.8756 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/finetuned_token_2e-05_16_02_2022-01_30_30	ali2066	2022-02-16T00:32:55Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_token_2e-05_16_02_2022-01_30_30 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_token_2e-05_16_02_2022-01_30_30 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: 0.1748 - Precision: 0.3384 - Recall: 0.3492 - F1: 0.3437 - Accuracy: 0.9442 ## Model description More information needed ## Intended uses & 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 \| 38 \| 0.3180 \| 0.0985 \| 0.1648 \| 0.1233 \| 0.8643 \| \| No log \| 2.0 \| 76 \| 0.2667 \| 0.1962 \| 0.2698 \| 0.2272 \| 0.8926 \| \| No log \| 3.0 \| 114 \| 0.2374 \| 0.2268 \| 0.3005 \| 0.2585 \| 0.9062 \| \| No log \| 4.0 \| 152 \| 0.2305 \| 0.2248 \| 0.3247 \| 0.2657 \| 0.9099 \| \| No log \| 5.0 \| 190 \| 0.2289 \| 0.2322 \| 0.3166 \| 0.2679 \| 0.9102 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ncats/EpiExtract4GARD-v2	ncats	2022-02-16T00:08:16Z	24	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "ncats", "en", "dataset:ncats/EpiSet4NER", "license:other", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- language: - en widget: - text: "27 patients have been diagnosed with PKU in Iceland since 1947. Incidence 1972-2008 is 1/8400 living births." example_title: "Named Entity Recognition Ex. 1" - text: "A retrospective epidemiological study of MPSs in Estonia was undertaken, and live-birth prevalence of MPS patients born between 1985 and 2006 was estimated. The live-birth prevalence for all MPS subtypes was found to be 4.05 per 100,000 live births, which is consistent with most other European studies. MPS II had the highest calculated incidence, with 2.16 per 100,000 live births (4.2 per 100,000 male live births)" example_title: "Named Entity Recognition Ex. 2" - text: "A retrospective study conducted between January 2015 and December 2020 revealed a total of 304,086 newborns have been screened in Kuwait. Six newborns were diagnosed with classic homocystinuria with an incidence of 1:50,000, which is not as high as in Qatar but higher than the global incidence." example_title: "Named Entity Recognition Ex. 3" tags: - token-classification - ncats model-index: - name: EpiExtract4GARD-v2 results: - task: name: NER type: token-classification metrics: - name: Token-Level Precision type: precision value: - name: Token-Level Recall type: recall value: - name: Token-Level F1 Score type: f_score value: - name: Token-Level Precision type: precision value: - name: Token-Level Recall type: recall value: - name: Token-Level F1 Score type: f_score value: datasets: - ncats/EpiSet4NER license: other --- ## DOCUMENTATION UPDATES IN PROGRESS ## Model description EpiExtract4GARD-v2 is a fine-tuned [BioBERT-base-cased](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) model that is ready to use for Named Entity Recognition of locations (LOC), epidemiologic types (EPI), and epidemiologic rates (STAT). This model was fine-tuned on EpiSet4NER-v2 for epidemiological information from rare disease abstracts. See dataset documentation for details on the weakly supervised teaching methods and dataset biases and limitations. See [EpiExtract4GARD on GitHub](https://github.com/ncats/epi4GARD/tree/master/EpiExtract4GARD#epiextract4gard) for details on the entire pipeline. #### How to use You can use this model with the Hosted inference API to the right with this [test sentence](https://pubmed.ncbi.nlm.nih.gov/21659675/): "27 patients have been diagnosed with PKU in Iceland since 1947. Incidence 1972-2008 is 1/8400 living births." See code below for use with Transformers pipeline for NER.: ~~~ from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("ncats/EpiExtract4GARD") tokenizer = AutoTokenizer.from_pretrained("ncats/EpiExtract4GARD") NER_pipeline = pipeline('ner', model=model, tokenizer=tokenizer,aggregation_strategy='simple') sample = "The live-birth prevalence of mucopolysaccharidoses in Estonia. Previous studies on the prevalence of mucopolysaccharidoses (MPS) in different populations have shown considerable variations. There are, however, few data with regard to the prevalence of MPSs in Fenno-Ugric populations or in north-eastern Europe, except for a report about Scandinavian countries. A retrospective epidemiological study of MPSs in Estonia was undertaken, and live-birth prevalence of MPS patients born between 1985 and 2006 was estimated. The live-birth prevalence for all MPS subtypes was found to be 4.05 per 100,000 live births, which is consistent with most other European studies. MPS II had the highest calculated incidence, with 2.16 per 100,000 live births (4.2 per 100,000 male live births), forming 53% of all diagnosed MPS cases, and was twice as high as in other studied European populations. The second most common subtype was MPS IIIA, with a live-birth prevalence of 1.62 in 100,000 live births. With 0.27 out of 100,000 live births, MPS VI had the third-highest live-birth prevalence. No cases of MPS I were diagnosed in Estonia, making the prevalence of MPS I in Estonia much lower than in other European populations. MPSs are the third most frequent inborn error of metabolism in Estonia after phenylketonuria and galactosemia." sample2 = "Early Diagnosis of Classic Homocystinuria in Kuwait through Newborn Screening: A 6-Year Experience. Kuwait is a small Arabian Gulf country with a high rate of consanguinity and where a national newborn screening program was expanded in October 2014 to include a wide range of endocrine and metabolic disorders. A retrospective study conducted between January 2015 and December 2020 revealed a total of 304,086 newborns have been screened in Kuwait. Six newborns were diagnosed with classic homocystinuria with an incidence of 1:50,000, which is not as high as in Qatar but higher than the global incidence. Molecular testing for five of them has revealed three previously reported pathogenic variants in the <i>CBS</i> gene, c.969G>A, p.(Trp323Ter); c.982G>A, p.(Asp328Asn); and the Qatari founder variant c.1006C>T, p.(Arg336Cys). This is the first study to review the screening of newborns in Kuwait for classic homocystinuria, starting with the detection of elevated blood methionine and providing a follow-up strategy for positive results, including plasma total homocysteine and amino acid analyses. Further, we have demonstrated an increase in the specificity of the current newborn screening test for classic homocystinuria by including the methionine to phenylalanine ratio along with the elevated methionine blood levels in first-tier testing. Here, we provide evidence that the newborn screening in Kuwait has led to the early detection of classic homocystinuria cases and enabled the affected individuals to lead active and productive lives." #Sample 1 is from: Krabbi K, Joost K, Zordania R, Talvik I, Rein R, Huijmans JG, Verheijen FV, Õunap K. The live-birth prevalence of mucopolysaccharidoses in Estonia. Genet Test Mol Biomarkers. 2012 Aug;16(8):846-9. doi: 10.1089/gtmb.2011.0307. Epub 2012 Apr 5. PMID: 22480138; PMCID: PMC3422553. #Sample 2 is from: Alsharhan H, Ahmed AA, Ali NM, Alahmad A, Albash B, Elshafie RM, Alkanderi S, Elkazzaz UM, Cyril PX, Abdelrahman RM, Elmonairy AA, Ibrahim SM, Elfeky YME, Sadik DI, Al-Enezi SD, Salloum AM, Girish Y, Al-Ali M, Ramadan DG, Alsafi R, Al-Rushood M, Bastaki L. Early Diagnosis of Classic Homocystinuria in Kuwait through Newborn Screening: A 6-Year Experience. Int J Neonatal Screen. 2021 Aug 17;7(3):56. doi: 10.3390/ijns7030056. PMID: 34449519; PMCID: PMC8395821. NER_pipeline(sample) NER_pipeline(sample2) ~~~ Or if you download [classify_abs.py](https://github.com/ncats/epi4GARD/blob/master/EpiExtract4GARD/classify_abs.py), [extract_abs.py](https://github.com/ncats/epi4GARD/blob/master/EpiExtract4GARD/extract_abs.py), and [gard-id-name-synonyms.json](https://github.com/ncats/epi4GARD/blob/master/EpiExtract4GARD/gard-id-name-synonyms.json) from GitHub then you can test with this [additional code](https://github.com/ncats/epi4GARD/blob/master/EpiExtract4GARD/Case%20Study.ipynb): ~~~ import pandas as pd import extract_abs import classify_abs pd.set_option('display.max_colwidth', None) NER_pipeline = extract_abs.init_NER_pipeline() GARD_dict, max_length = extract_abs.load_GARD_diseases() nlp, nlpSci, nlpSci2, classify_model, classify_tokenizer = classify_abs.init_classify_model() def search(term,num_results = 50): return extract_abs.search_term_extraction(term, num_results, NER_pipeline, GARD_dict, max_length,nlp, nlpSci, nlpSci2, classify_model, classify_tokenizer) a = search(7058) a b = search('Santos Mateus Leal syndrome') b c = search('Fellman syndrome') c d = search('GARD:0009941') d e = search('Homocystinuria') e ~~~ #### Limitations and bias ## Training data It was trained on [EpiSet4NER](https://huggingface.co/datasets/ncats/EpiSet4NER). See dataset documentation for details on the weakly supervised teaching methods and dataset biases and limitations. The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Abbreviation\|Description ---------\|-------------- O \|Outside of a named entity B-LOC \| Beginning of a location I-LOC \| Inside of a location B-EPI \| Beginning of an epidemiologic type (e.g. "incidence", "prevalence", "occurrence") I-EPI \| Epidemiologic type that is not the beginning token. B-STAT \| Beginning of an epidemiologic rate I-STAT \| Inside of an epidemiologic rate +More \| Description pending ### EpiSet Statistics Beyond any limitations due to the EpiSet4NER dataset, this model is limited in numeracy due to BERT-based model's use of subword embeddings, which is crucial for epidemiologic rate identification and limits the entity-level results. Recent techniques in numeracy could be used to improve the performance of the model without improving the underlying dataset. ## Training procedure This model was trained on a [AWS EC2 p3.2xlarge](https://aws.amazon.com/ec2/instance-types/), which utilized a single Tesla V100 GPU, with these hyperparameters: 4 epochs of training (AdamW weight decay = 0.05) with a batch size of 16. Maximum sequence length = 192. Model was fed one sentence at a time. <!--- Full config [here](https://wandb.ai/wzkariampuzha/huggingface/runs/353prhts/files/config.yaml). ---> <!--- THIS IS NOT THE UPDATED RESULTS ---> <!--- ## Hold-out validation results ---> <!--- metric\| entity-level result ---> <!--- -\|- ---> <!--- f1 \| 83.8 ---> <!--- precision \| 83.2 ---> <!--- recall \| 84.5 ---> <!--- ## Test results ---> <!--- \| Dataset for Model Training \| Evaluation Level \| Entity \| Precision \| Recall \| F1 \| ---> <!--- \|:--------------------------:\|:----------------:\|:------------------:\|:---------:\|:------:\|:-----:\| ---> <!--- \| EpiSet \| Entity-Level \| Overall \| 0.556 \| 0.662 \| 0.605 \| ---> <!--- \| \| \| Location \| 0.661 \| 0.696 \| 0.678 \| ---> <!--- \| \| \| Epidemiologic Type \| 0.854 \| 0.911 \| 0.882 \| ---> <!--- \| \| \| Epidemiologic Rate \| 0.143 \| 0.218 \| 0.173 \| ---> <!--- \| \| Token-Level \| Overall \| 0.811 \| 0.713 \| 0.759 \| ---> <!--- \| \| \| Location \| 0.949 \| 0.742 \| 0.833 \| ---> <!--- \| \| \| Epidemiologic Type \| 0.9 \| 0.917 \| 0.908 \| ---> <!--- \| \| \| Epidemiologic Rate \| 0.724 \| 0.636 \| 0.677 \| ---> Thanks to [@William Kariampuzha](https://github.com/wzkariampuzha) at Axle Informatics/NCATS for contributing this model.
vxvxx/t5-small-finetuned-no_paragraph-to-paragraph	vxvxx	2022-02-15T23:01:34Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: t5-small-finetuned-no_paragraph-to-paragraph 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-no_paragraph-to-paragraph This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0713 - Bleu: 0.0 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----:\|:-------:\| \| 0.767 \| 1.0 \| 576 \| 0.0713 \| 0.0 \| 19.0 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
huggingartists/led-zeppelin	huggingartists	2022-02-15T22:19:29Z	4	0	transformers	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingartists", "lyrics", "lm-head", "causal-lm", "en", "dataset:huggingartists/led-zeppelin", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en datasets: - huggingartists/led-zeppelin tags: - huggingartists - lyrics - lm-head - causal-lm widget: - text: "I am" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/e4763bba12e6411077a3e573cd290da0.433x433x1.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Led Zeppelin</div> <a href="https://genius.com/artists/led-zeppelin"> <div style="text-align: center; font-size: 14px;">@led-zeppelin</div> </a> </div> I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists). Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)! ## How does it work? To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist). ## Training data The model was trained on lyrics from Led Zeppelin. Dataset is available [here](https://huggingface.co/datasets/huggingartists/led-zeppelin). And can be used with: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/led-zeppelin") ``` [Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/cpexpb1w/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 Led Zeppelin's lyrics. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/bna2epba) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/bna2epba/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='huggingartists/led-zeppelin') generator("I am", num_return_sequences=5) ``` Or with Transformers library: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("huggingartists/led-zeppelin") model = AutoModelWithLMHead.from_pretrained("huggingartists/led-zeppelin") ``` ## 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 Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)
Sourabh714/distilbert-base-uncased-finetuned-squad	Sourabh714	2022-02-15T20:47:37Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1573 ## Model description More information needed ## Intended uses & 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.2188 \| 1.0 \| 5533 \| 1.1708 \| \| 0.9519 \| 2.0 \| 11066 \| 1.1058 \| \| 0.7576 \| 3.0 \| 16599 \| 1.1573 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
espnet/roshansh_how2_asr_raw_ft_sum_valid.acc	espnet	2022-02-15T19:51:13Z	0	0	espnet	[ "espnet", "audio", "automatic-speech-summarization", "en", "dataset:how2", "arxiv:2110.06263", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]	null	2022-03-02T23:29:05Z	--- tags: - espnet - audio - automatic-speech-summarization language: en datasets: - how2 license: cc-by-4.0 --- ## ESPnet2 ASR model ### `espnet/roshansh_how2_asr_raw_ft_sum_valid.acc` This model was trained by roshansh-cmu using how2 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout e6f42a9783a5d9eba0687c19417f933e890722d7 pip install -e . cd egs2/how2/sum1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/roshansh_how2_asr_raw_ft_sum_valid.acc ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Mon Feb 7 15:24:21 EST 2022` - python version: `3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]` - espnet version: `espnet 0.10.6a1` - pytorch version: `pytorch 1.10.1` - Git hash: `04561cdf3b6c3bc1d51edb04c93b953759ef551d` - Commit date: `Mon Feb 7 09:06:12 2022 -0500` ## asr_raw_ft_sum \|dataset\|Snt\|Wrd\|ROUGE-1\|ROUGE-2\|ROUGE-L\|METEOR\|BERTScore\| \|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_sum_asr_model_valid.acc.best/dev5_test_sum\|2127\|69795\|60.72\|44.7\|56.1\|29.36\|91.53\| ## ASR config <details><summary>expand</summary> ``` config: conf/train_asr_conformer_vid_lf.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_raw_ft_sum ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 8 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 45875 dist_launcher: null multiprocessing_distributed: true unused_parameters: true sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 100 patience: 10 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 10 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: 5000 use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: - exp/asr_raw_utt_conformer/valid.acc.ave_10best.pth:::ctc ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 60000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_vid_sum/train/speech_shape - exp/asr_stats_raw_vid_sum/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_vid_sum/valid/speech_shape - exp/asr_stats_raw_vid_sum/valid/text_shape.bpe batch_type: length valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/tr_2000h_sum_trim/wav.scp - speech - sound - - dump/raw/tr_2000h_sum_trim/text - text - text valid_data_path_and_name_and_type: - - dump/raw/cv05_sum_trim/wav.scp - speech - sound - - dump/raw/cv05_sum_trim/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.001 scheduler: reducelronplateau scheduler_conf: mode: min factor: 0.5 patience: 1 token_list: - <blank> - <unk> - '[hes]' - S - ▁THE - ▁TO - '''' - ▁AND - ▁YOU - ▁A - ▁IT - T - ▁THAT - ▁OF - ▁I - ▁IS - RE - ▁IN - ING - ▁WE - M - ▁GOING - ▁SO - ▁THIS - ▁YOUR - ▁ON - E - D - ▁BE - ▁CAN - N - Y - O - ER - ▁HAVE - ▁JUST - ▁FOR - ▁WITH - ▁DO - ED - ▁ARE - ▁WANT - ▁UP - R - LL - P - ▁ - L - B - ▁IF - C - ▁ONE - ▁S - ▁OR - A - ▁GO - ▁LIKE - ▁NOW - ▁HERE - VE - LE - U - ▁GET - ▁WHAT - ▁OUT - IN - W - ▁C - ▁LITTLE - ▁THERE - LY - ▁AS - ▁MAKE - I - ▁THEY - ▁MY - K - ▁THEN - ▁BUT - AL - G - ▁ALL - OR - ▁BACK - ▁NOT - ▁ABOUT - ▁RIGHT - ▁OUR - EN - ▁SOME - ▁DOWN - F - ▁WHEN - CH - ▁F - ▁HOW - AR - ▁WILL - ▁RE - CK - ▁G - ES - CE - ▁TAKE - ▁AT - ▁FROM - ▁WAY - TER - ▁SEE - RA - ▁USE - ▁REALLY - RI - TH - ▁TWO - ▁ME - ▁VERY - ▁E - ▁B - AT - ▁THEM - ▁DON - ▁AN - ▁BECAUSE - ▁MORE - RO - H - 'ON' - LI - ▁PUT - ▁ST - IL - ▁BIT - ▁START - ▁NEED - ▁INTO - UR - ▁TIME - ▁OVER - ▁W - ▁DE - ▁LOOK - ▁THESE - ▁LET - ▁GOOD - ▁ALSO - AN - ▁OFF - ▁HE - ▁KIND - ▁SIDE - ▁CO - ▁SURE - ▁AGAIN - ▁MA - ▁KNOW - IT - ▁WOULD - IC - ▁OTHER - LA - ▁P - ▁WHICH - '-' - IR - ▁LA - ▁HAND - EL - ▁LOT - ▁WHERE - ▁THREE - ▁PA - ION - LO - ▁KEEP - ▁SHOW - ▁THING - ▁FIRST - TE - ENT - ATE - ▁COME - AD - ▁GOT - NG - ▁NICE - ▁T - ET - ▁MO - ▁ANY - ▁ACTUALLY - ▁DIFFERENT - ▁SE - GE - ▁WORK - ▁THROUGH - ▁O - KE - V - ▁AROUND - ▁BA - PE - ▁HI - ▁BY - SH - ATION - ▁SU - ▁CA - ▁D - ▁LO - ▁HAS - ▁LI - ▁PLAY - Z - ▁ADD - ▁RO - ▁TA - AS - ▁FOUR - ▁CON - ▁THOSE - MP - NE - ▁SP - UT - ▁GIVE - ▁WELL - ▁BALL - TING - RY - X - ▁HO - INE - IVE - ▁NEXT - ▁PO - ▁STEP - ▁EVEN - TION - ▁MI - MENT - ▁CUT - ▁BO - ▁LINE - ▁MUCH - ▁THINGS - ▁TALK - UN - ▁PART - ▁WAS - ▁FA - ▁SOMETHING - PP - ANCE - ND - DI - ▁RA - AGE - ▁SAME - ▁EXPERT - ▁DOING - ▁LEFT - IST - ▁DI - ▁NO - RU - ME - TA - UL - TI - ▁VILLAGE - DE - ERS - ▁PEOPLE - ▁TURN - VER - ▁FL - ▁LEG - ▁ONCE - ▁COLOR - ▁PULL - ▁USING - VI - ▁WATER - ▁SHE - ▁TOP - ▁OKAY - ▁ANOTHER - ▁THEIR - ▁SAY - URE - ▁HA - ▁IMPORTANT - ▁PIECE - ▁FOOT - ▁TRA - ▁SC - ▁BODY - ▁SET - ▁POINT - ▁HELP - ▁TODAY - ▁BRING - ▁V - ▁END - MA - ▁CH - ▁MOST - ▁K - ▁AHEAD - ▁HER - OL - ▁SA - AM - IES - ▁THINK - ▁NAME - ▁TRY - ▁MOVE - ONE - ▁LE - ▁TOO - TO - UM - ▁PLACE - ▁COULD - ▁FIND - ▁FIVE - ▁ALWAYS - ID - TY - NT - ▁FEEL - ▁HEAD - ▁THAN - NA - ▁EX - ▁EYE - ITY - CI - OP - ▁SHOULD - ▁MIGHT - ▁HOLD - ▁CAR - AND - ▁GREAT - ▁RI - ▁BU - ▁HIGH - ▁OPEN - ▁BEFORE - US - ▁FRONT - ▁LONG - ▁TOGETHER - NI - ▁HAIR - ▁LIGHT - ▁TEN - ▁HIT - EST - OUS - ▁PRETTY - ▁TYPE - IP - CO - ▁FINGER - ▁JO - ▁UN - ▁PRO - ▁STRAIGHT - ▁BEHALF - ▁TI - ▁SIX - ▁CLEAN - ▁DIS - ▁DA - ▁POSITION - IGHT - ACT - ▁CHA - ▁PE - GG - AP - ▁MEAN - ▁COMP - FI - ▁KNEE - ▁CALLED - ▁HANDS - ▁PRE - ▁FORWARD - ▁AREA - ANT - ▁TE - ▁WA - ▁AFTER - ▁SMALL - ▁THROW - ▁EVERY - ▁SHOULDER - NC - PER - ▁MAYBE - ▁ABLE - ▁BASICALLY - ▁AM - ▁READY - ▁BOTTOM - IE - ▁HALF - FF - ▁BIG - ▁EACH - ▁PUSH - ▁EIGHT - ▁NEW - ▁DONE - ▁MAY - ▁GETTING - HO - ▁HIS - ▁HARD - ▁CLOSE - ALLY - ▁SECOND - ▁FEET - ICAL - ▁JA - ▁PAINT - ▁LEARN - ▁SOUND - HE - ▁ROLL - ▁ONLY - ▁DOESN - WA - ▁DRAW - ▁VI - ▁DID - ▁SHA - ▁CENTER - CU - ▁CLIP - ▁PI - ▁CARD - ▁INSIDE - ▁PERSON - ▁STILL - ▁MAKING - 'NO' - ▁EVERYTHING - . - ▁FUN - ARD - ▁REMEMBER - ▁AWAY - ATED - COM - ▁SEVEN - ▁BEEN - ▁MANY - ABLE - ▁DAY - ▁SIT - IZE - ▁REAL - ▁HIP - ▁BASIC - ▁KICK - ▁TU - ATING - ▁STICK - ▁FLAT - ▁WHO - END - HA - ▁EXP - ▁PICK - ▁MIX - ▁TRI - ▁BI - ▁WHOLE - ▁STRETCH - ▁BOTH - ▁PROBABLY - CA - ▁HIM - ▁STRING - ▁EDGE - ▁BASE - ▁COMING - UGH - ▁LIFT - ▁STA - ▁WORKING - ▁MU - ▁QUICK - ▁SOMETIMES - ▁HAPPEN - ▁YOURSELF - ▁TALKING - ▁DR - ▁TELL - ▁ANYTHING - ▁BRA - ▁LOOKING - ▁SLOW - ▁NE - ▁STAND - NER - ▁COMES - ▁GOES - ISE - BE - ▁USED - ▁UNDER - ▁BETWEEN - ▁HU - ▁CREATE - ▁NA - ▁USUALLY - ▁ARM - ▁DRY - ▁RUN - LING - ▁BRUSH - ▁COVER - ▁HEAR - ▁DOES - ▁STAY - ▁EN - ▁FOLD - ▁CHANGE - ▁LAST - ▁EASY - ▁US - ▁PER - ▁FACE - ▁EAR - ▁TIGHT - ▁FE - ▁PIN - ▁MAN - ▁BETTER - ▁CALL - ▁PRI - ▁BEST - ▁KI - ▁COUPLE - ▁WHILE - ▁SHAPE - ▁GAME - IV - ▁SHOT - ▁PAPER - ▁OWN - ▁ALRIGHT - ▁HAD - TIC - ▁BREATH - ▁TOOL - '2' - ▁ENOUGH - ▁COURSE - ▁SKIN - ▁SPIN - ▁VA - ▁ARMS - ▁TEA - ▁BREAK - ▁DOG - ▁1 - QUE - ▁DROP - ▁NUMBER - IG - ▁RED - ▁NOTE - ▁WEIGHT - WARD - ▁PLAYING - ▁FINISH - ▁MINUTE - ▁R - ▁PRESS - ▁EITHER - ▁CHE - ▁PU - BER - ▁FEW - ▁SIZE - ▁MADE - ▁LEAVE - ▁GA - ▁ALREADY - ▁GUY - ▁FAR - ▁HOME - ▁BAR - UP - ▁GRAB - ▁MARK - ▁WHITE - ▁PROPER - ▁CAUSE - ▁OK - ▁ART - HI - ▁SORT - ▁EXERCISE - ▁LOWER - PORT - ▁PLANT - ▁BOARD - ▁CASE - ▁YEAR - CENT - ▁DU - ▁CHECK - ▁WHATEVER - ▁OIL - ▁IDEA - ▁SIMPLE - ▁PRACTICE - ▁FAST - '0' - ▁CONTROL - ▁J - ▁KEY - ▁MIDDLE - ▁FULL - ▁GLASS - ▁OUTSIDE - ▁LOW - ▁REST - ▁STUFF - ▁ACT - ▁UNTIL - ▁BLACK - ▁POP - ▁CLICK - ▁HOLE - ▁Z - ▁COUNT - ▁POT - ▁ALLOW - ▁HAVING - ▁TRYING - ▁MUSCLE - ▁GU - ▁BOX - ▁NOTICE - ▁EXAMPLE - UND - ▁ALONG - FUL - ISH - ▁STORE - ▁LU - ▁FLOOR - ▁MOVING - ▁LARGE - ▁STOP - ▁PH - ▁WALK - '5' - ▁QU - ▁TECHNIQUE - ▁SOFT - ▁GROUND - ▁JUMP - ▁JU - ▁FILL - ▁WHY - ▁BUY - ▁GREEN - ▁WALL - ▁HEEL - NESS - ▁LEVEL - ▁UNDERNEATH - ▁PATTERN - ▁BEHIND - ▁OLD - ▁TIP - ▁COMPLETE - ▁WON - ▁TEACH - ▁FIT - ▁NECK - ▁REMOVE - ▁TRICK - ▁MOVEMENT - ▁TOWARDS - ▁PARTICULAR - ▁CHI - ▁EFFECT - J - ▁FREE - ▁ACROSS - ▁BEND - ▁SAFE - ▁SLIDE - ▁PROBLEM - ▁BLOCK - ▁PAN - ▁NATURAL - ▁TOUCH - ▁CHILD - LINE - ▁CROSS - ▁REASON - '4' - ▁POWER - ▁APPLY - ▁FOLLOW - ▁DESIGN - ▁SPACE - ▁ORDER - ▁WOOD - ▁RID - '3' - ▁COOK - ▁BEGIN - ▁WATCH - ▁STYLE - QUA - ▁PRODUCT - ▁TAKING - ▁PUTTING - ▁EXHALE - ▁THOUGH - ▁DEEP - IAN - ▁REACH - ▁FOOD - ▁ALMOST - ▁COOL - ▁SECTION - ▁SAID - ▁ANGLE - ▁MUSIC - ▁RELAX - ▁CORNER - ▁DARK - ▁CHORD - ▁ESPECIALLY - ▁SCALE - ▁WARM - ▁WITHOUT - ▁WHEEL - ▁SEGMENT - ▁TABLE - ▁BOOK - ▁PASS - ▁ELBOW - ▁ROUND - ▁INHALE - ▁SMOOTH - ▁ROOM - / - ▁NINE - ▁SHORT - ▁MEASURE - ▁LESS - ▁TWIST - ▁BALANCE - ▁PROCESS - ▁SWITCH - ▁GENERAL - ▁CLAY - ▁CERTAIN - ▁NEVER - ▁BLUE - ▁CUP - ▁HOUSE - ▁EXTRA - ▁MOTION - ▁PRESSURE - ▁FIRE - ▁SIMPLY - ▁DOUBLE - ▁TWENTY - ▁CATCH - ▁BECOME - ▁BUILD - ▁SPEED - ▁TRANS - ▁DRUM - ▁CHEST - ▁PICTURE - ▁LENGTH - ▁CONTINUE - ▁COMFORTABLE - ▁FISH - ▁PHOTO - ▁LOOSE - ▁SKI - ▁LIFE - ▁DEGREE - ▁OPTION - ▁WORD - ▁SHARP - ▁SHOOT - ▁FOUND - ▁STRONG - ▁QUITE - ▁THIRD - ▁GLUE - ▁MIND - ▁DEFINITELY - ▁EASIER - GRAPH - ▁HOOK - ▁CLEAR - ▁POSE - ▁BUTTON - ▁CHOOSE - ▁THICK - ▁SYSTEM - ▁PERFECT - ▁BEAUTIFUL - ▁SPOT - ▁GROW - ▁SIGN - ▁ELSE - ▁CONNECT - ▁SELECT - ▁PUNCH - ▁DIRECTION - ▁WRAP - ▁RELEASE - QUI - SIDE - ▁CAREFUL - ▁VIDEO - ▁INSTEAD - ▁CIRCLE - ▁WIRE - ▁NOSE - ▁AMOUNT - ▁FOCUS - ▁NORMAL - ▁MAJOR - ▁WHETHER - ▁SURFACE - ▁THUMB - ▁DRIVE - ▁SCREW - ▁POSSIBLE - ▁OBVIOUSLY - ▁COMMON - ▁REGULAR - ▁ADJUST - ▁WIDE - ▁BLADE - ▁FRET - ▁RECOMMEND - ▁BOWL - BOARD - ▁IMAGE - ▁DEPENDING - ▁PROTECT - ▁CLOTH - ▁HEALTH - ▁WRIST - ▁CLUB - ▁DRINK - ▁SINCE - ▁FRIEND - '00' - ▁RUNNING - ▁ITSELF - ▁RECORD - ▁SWING - ▁DIRECT - ▁MATERIAL - ▁YO - ▁LEAST - ▁EXACTLY - ▁BEGINNING - ▁SLIGHTLY - ▁TREAT - ▁CAMERA - ▁QUARTER - ▁WINDOW - '8' - ▁SOMEBODY - ▁BURN - ▁DEMONSTRATE - ▁DIFFERENCE - ▁COMPUTER - IBLE - ▁SHOE - ▁PERFORM - ▁SQUARE - ▁CONSIDER - ▁DRILL - ▁TEXT - ▁FILE - ▁RUB - ▁FABRIC - ▁HUNDRED - ▁GRIP - ▁CHARACTER - ▁SPECIFIC - ▁KNOT - ▁CURL - ▁STITCH - ▁BLEND - ▁FRAME - ▁THIRTY - '1' - ▁HORSE - ▁ATTACH - ▁GROUP - ▁STROKE - ▁GUITAR - ▁APART - ▁MACHINE - ▁CLASS - ▁COMB - ▁ROOT - ▁HELLO - ▁ENERGY - ▁ATTACK - ▁CORRECT - ▁EXTEND - ▁MINOR - ▁PROFESSIONAL - ▁MONEY - ▁STRIP - ▁FLAVOR - ▁EVERYBODY - ▁RULE - ▁DIFFICULT - ▁PROJECT - ▁DISCUSS - ▁FIGURE - ▁HOWEVER - ▁FINAL - ▁STRENGTH - ▁ENTIRE - ▁FIELD - ▁CONTACT - ▁SUPPORT - ▁PALM - ▁SERIES - ▁ENJOY - '6' - ▁WORLD - ▁DECIDE - ▁SPEAK - ▁SEVERAL - ▁WRITE - ▁PROGRAM - ABILITY - ▁KNIFE - ▁PLASTIC - ▁ORGAN - '7' - ▁UNDERSTAND - ▁FIFTEEN - ▁FLEX - ▁INFORMATION - ▁TWELVE - ▁DETAIL - ▁STRIKE - ▁ACTUAL - ▁SPRAY - ▁LOCAL - ▁MOUTH - ▁NIGHT - ▁VEHICLE - ▁OPPOSITE - ▁SCHOOL - '9' - ▁QUESTION - ▁SPECIAL - ▁BIGGER - ▁DEVELOP - ▁PEPPER - ▁PREFER - Q - '%' - ']' - '[' - '&' - ',' - _ - '#' - '=' - '@' - + - '*' - $ - '~' - <sos/eos> init: null input_size: null ctc_conf: ignore_nan_grad: true model_conf: ctc_weight: 0.0 lsm_weight: 0.15 length_normalized_loss: false use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram1000/bpe.model non_linguistic_symbols: data/nlsyms cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: default frontend_conf: n_fft: 512 hop_length: 256 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_vid_sum/train/feats_stats.npz preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 512 attention_heads: 8 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true pos_enc_layer_type: abs_pos selfattention_layer_type: lf_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 attention_windows: - 40 - 40 - 40 - 40 - 40 - 40 - 40 - 40 - 40 - 40 - 40 - 40 attention_dilation: - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 - 1 attention_mode: tvm decoder: transformer decoder_conf: attention_heads: 4 linear_units: 512 num_blocks: 6 dropout_rate: 0.15 positional_dropout_rate: 0.15 self_attention_dropout_rate: 0.15 src_attention_dropout_rate: 0.15 required: - output_dir - token_list version: 0.10.0 distributed: true ``` </details> Please cite the following paper if you use this recipe: ```BibTex @misc{sharma2022speech, title={Speech Summarization using Restricted Self-Attention}, author={Roshan Sharma and Shruti Palaskar and Alan W Black and Florian Metze}, year={2022}, eprint={2110.06263}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title##3={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass{cs.CL} ```
premrawat/en_model_ner_skills	premrawat	2022-02-15T19:50:15Z	6	4	spacy	[ "spacy", "token-classification", "en", "model-index", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - spacy - token-classification language: - en model-index: - name: en_model_ner_skills results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.3125 - name: NER Recall type: recall value: 0.243902439 - name: NER F Score type: f_score value: 0.2739726027 --- \| Feature \| Description \| \| --- \| --- \| \| Name \| `en_model_ner_skills` \| \| Version \| `0.0.2` \| \| spaCy \| `>=3.2.1,<3.3.0` \| \| Default Pipeline \| `tok2vec`, `ner` \| \| Components \| `tok2vec`, `ner` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| n/a \| \| License \| n/a \| \| Author \| [n/a]() \| ### Label Scheme <details> <summary>View label scheme (1 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `ner` \| `SKILL` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `ENTS_F` \| 27.40 \| \| `ENTS_P` \| 31.25 \| \| `ENTS_R` \| 24.39 \| \| `TOK2VEC_LOSS` \| 129837.25 \| \| `NER_LOSS` \| 1056832.41 \|
AI-Nordics/bert-large-swedish-cased	AI-Nordics	2022-02-15T16:52:53Z	162	11	transformers	[ "transformers", "pytorch", "megatron-bert", "fill-mask", "sv", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- language: sv --- # A Swedish Bert model ## Model description This model follows the Bert Large model architecture as implemented in [Megatron-LM framework](https://github.com/NVIDIA/Megatron-LM). It was trained with a batch size of 512 in 600k steps. The model contains following parameters: <figure> \| Hyperparameter \| Value \| \|----------------------\|------------\| \| \$n_{parameters}\$ \| 340M \| \| \$n_{layers}\$ \| 24 \| \| \$n_{heads}\$ \| 16 \| \| \$n_{ctx}\$ \| 1024 \| \| \$n_{vocab}\$ \| 30592 \| ## Training data The model is pretrained on a Swedish text corpus of around 85 GB from a variety of sources as shown below. <figure> \| Dataset \| Genre \| Size(GB)\| \|----------------------\|------\|------\| \| Anföranden \| Politics \|0.9\| \|DCEP\|Politics\|0.6\| \|DGT\|Politics\|0.7\| \|Fass\|Medical\|0.6\| \|Författningar\|Legal\|0.1\| \|Web data\|Misc\|45.0\| \|JRC\|Legal\|0.4\| \|Litteraturbanken\|Books\|0.3O\| \|SCAR\|Misc\|28.0\| \|SOU\|Politics\|5.3\| \|Subtitles\|Drama\|1.3\| \|Wikipedia\|Facts\|1.8\| ## Intended uses & limitations The raw model can be used for the usual tasks of masked language modeling or next sentence prediction. It is also often fine-tuned on a downstream task to improve its performance in a specific domain/task. <br> <br> ## How to use ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AI-Nordics/bert-large-swedish-cased") model = AutoModelForMaskedLM.from_pretrained("AI-Nordics/bert-large-swedish-cased")
AKulk/wav2vec2-base-timit-epochs15	AKulk	2022-02-15T14:26:13Z	21	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-epochs15 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-epochs15 This model is a fine-tuned version of [AKulk/wav2vec2-base-timit-epochs10](https://huggingface.co/AKulk/wav2vec2-base-timit-epochs10) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 80 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
xujiacheng127/anchi-bert	xujiacheng127	2022-02-15T12:01:06Z	0	2	null	[ "pytorch", "region:us" ]	null	2022-03-02T23:29:05Z	import json import requests headers = {"Authorization": f"Bearer {API_TOKEN}"} API_URL = "https://api-inference.huggingface.co/models/bert-base-uncased" def query(payload): data = json.dumps(payload) response = requests.request("POST", API_URL, headers=headers, data=data) return json.loads(response.content.decode("utf-8")) data = query({"inputs": "The answer to the universe is [MASK]."})
CLAck/en-km	CLAck	2022-02-15T11:26:53Z	39	3	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "translation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:04Z	--- tags: - translation --- This model translate from English to Khmer. It is the pure fine-tuned version of MarianMT model en-zh. This is the result after 30 epochs of pure fine-tuning of khmer language. ### Example ``` %%capture !pip install transformers transformers[sentencepiece] from transformers import AutoModelForSeq2SeqLM, AutoTokenizer # Download the pretrained model for English-Vietnamese available on the hub model = AutoModelForSeq2SeqLM.from_pretrained("CLAck/en-km") tokenizer = AutoTokenizer.from_pretrained("CLAck/en-km") # Download a tokenizer that can tokenize English since the model Tokenizer doesn't know anymore how to do it # We used the one coming from the initial model # This tokenizer is used to tokenize the input sentence tokenizer_en = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-zh') # These special tokens are needed to reproduce the original tokenizer tokenizer_en.add_tokens(["<2zh>", "<2khm>"], special_tokens=True) sentence = "The cat is on the table" # This token is needed to identify the target language input_sentence = "<2khm> " + sentence translated = model.generate(**tokenizer_en(input_sentence, return_tensors="pt", padding=True)) output_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in translated] ```
CLAck/indo-mixed	CLAck	2022-02-15T11:25:18Z	18	1	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "translation", "en", "id", "dataset:ALT", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:04Z	--- language: - en - id tags: - translation license: apache-2.0 datasets: - ALT metrics: - sacrebleu --- This model is pretrained on Chinese and Indonesian languages, and fine-tuned on Indonesian language. ### Example ``` %%capture !pip install transformers transformers[sentencepiece] from transformers import AutoModelForSeq2SeqLM, AutoTokenizer # Download the pretrained model for English-Vietnamese available on the hub model = AutoModelForSeq2SeqLM.from_pretrained("CLAck/indo-mixed") tokenizer = AutoTokenizer.from_pretrained("CLAck/indo-mixed") # Download a tokenizer that can tokenize English since the model Tokenizer doesn't know anymore how to do it # We used the one coming from the initial model # This tokenizer is used to tokenize the input sentence tokenizer_en = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-zh') # These special tokens are needed to reproduce the original tokenizer tokenizer_en.add_tokens(["<2zh>", "<2indo>"], special_tokens=True) sentence = "The cat is on the table" # This token is needed to identify the target language input_sentence = "<2indo> " + sentence translated = model.generate(**tokenizer_en(input_sentence, return_tensors="pt", padding=True)) output_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in translated] ``` ### Training results MIXED \| Epoch \| Bleu \| \|:-----:\|:-------:\| \| 1.0 \| 24.2579 \| \| 2.0 \| 30.6287 \| \| 3.0 \| 34.4417 \| \| 4.0 \| 36.2577 \| \| 5.0 \| 37.3488 \| FINETUNING \| Epoch \| Bleu \| \|:-----:\|:-------:\| \| 6.0 \| 34.1676 \| \| 7.0 \| 35.2320 \| \| 8.0 \| 36.7110 \| \| 9.0 \| 37.3195 \| \| 10.0 \| 37.9461 \|
CLAck/indo-pure	CLAck	2022-02-15T11:24:33Z	28	0	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "translation", "en", "id", "dataset:ALT", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:04Z	--- language: - en - id tags: - translation license: apache-2.0 datasets: - ALT metrics: - sacrebleu --- Pure fine-tuning version of MarianMT en-zh on Indonesian Language ### Example ``` %%capture !pip install transformers transformers[sentencepiece] from transformers import AutoModelForSeq2SeqLM, AutoTokenizer # Download the pretrained model for English-Vietnamese available on the hub model = AutoModelForSeq2SeqLM.from_pretrained("CLAck/indo-pure") tokenizer = AutoTokenizer.from_pretrained("CLAck/indo-pure") # Download a tokenizer that can tokenize English since the model Tokenizer doesn't know anymore how to do it # We used the one coming from the initial model # This tokenizer is used to tokenize the input sentence tokenizer_en = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-zh') # These special tokens are needed to reproduce the original tokenizer tokenizer_en.add_tokens(["<2zh>", "<2indo>"], special_tokens=True) sentence = "The cat is on the table" # This token is needed to identify the target language input_sentence = "<2indo> " + sentence translated = model.generate(**tokenizer_en(input_sentence, return_tensors="pt", padding=True)) output_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in translated] ``` ### Training results \| Epoch \| Bleu \| \|:-----:\|:-------:\| \| 1.0 \| 15.9336 \| \| 2.0 \| 28.0175 \| \| 3.0 \| 31.6603 \| \| 4.0 \| 33.9151 \| \| 5.0 \| 35.0472 \| \| 6.0 \| 35.8469 \| \| 7.0 \| 36.1180 \| \| 8.0 \| 36.6018 \| \| 9.0 \| 37.1973 \| \| 10.0 \| 37.2738 \|
jfarray/Model_dccuchile_bert-base-spanish-wwm-uncased_100_Epochs	jfarray	2022-02-14T22:15:16Z	2	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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) ``` ## 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 11 with parameters: ``` {'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 100, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 110, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
jfarray/Model_dccuchile_bert-base-spanish-wwm-uncased_50_Epochs	jfarray	2022-02-14T21:41:05Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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) ``` ## 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 11 with parameters: ``` {'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 50, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 55, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
jfarray/Model_dccuchile_bert-base-spanish-wwm-uncased_10_Epochs	jfarray	2022-02-14T21:06:23Z	2	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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) ``` ## 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 11 with parameters: ``` {'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 11, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
jfarray/Model_dccuchile_bert-base-spanish-wwm-uncased_5_Epochs	jfarray	2022-02-14T20:57:30Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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) ``` ## 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 11 with parameters: ``` {'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 6, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
smmzhu/DialoGPT-small-SZ	smmzhu	2022-02-14T20:25:36Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- tags: - conversational ---
jfarray/Model_bert-base-multilingual-uncased_50_Epochs	jfarray	2022-02-14T19:44:38Z	5	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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) ``` ## 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 11 with parameters: ``` {'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 50, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 55, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
huggingtweets/magicrealismbot	huggingtweets	2022-02-14T18:15:59Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/668872745329885184/67TNOs2A_400x400.png')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Magic Realism Bot</div> <div style="text-align: center; font-size: 14px;">@magicrealismbot</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 Magic Realism Bot. \| Data \| Magic Realism Bot \| \| --- \| --- \| \| Tweets downloaded \| 3250 \| \| Retweets \| 0 \| \| Short tweets \| 0 \| \| Tweets kept \| 3250 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1nx0qvg7/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 @magicrealismbot's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/9vq0074d) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/9vq0074d/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/magicrealismbot') 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)
akshaychaudhary/distilbert-base-uncased-finetuned-cloud2-ner	akshaychaudhary	2022-02-14T17:33:18Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-cloud2-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cloud2-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8866 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.8453 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---:\|:--------:\| \| No log \| 1.0 \| 162 \| 0.7804 \| 0.0 \| 0.0 \| 0.0 \| 0.8447 \| \| No log \| 2.0 \| 324 \| 0.8303 \| 0.0 \| 0.0 \| 0.0 \| 0.8465 \| \| No log \| 3.0 \| 486 \| 0.8866 \| 0.0 \| 0.0 \| 0.0 \| 0.8453 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
NewT5SharedHeadsSharedKeyValues/t5-efficient-large-sh	NewT5SharedHeadsSharedKeyValues	2022-02-14T16:22:44Z	6	0	transformers	[ "transformers", "t5", "text2text-generation", "t5-new-failed", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- tags: - t5-new-failed --- # Test Hf T5: -110.35000801086426 MTF T5: -57.58127975463867
NewT5SharedHeadsSharedKeyValues/t5-efficient-base-sh	NewT5SharedHeadsSharedKeyValues	2022-02-14T16:22:41Z	4	0	transformers	[ "transformers", "t5", "text2text-generation", "t5-new-failed", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- tags: - t5-new-failed --- # Test Hf T5: -95.86687088012695 MTF T5: -67.8558578491211
NYTK/translation-marianmt-en-hu	NYTK	2022-02-14T13:31:08Z	44	1	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "translation", "en", "hu", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:04Z	--- language: - en - hu tags: - translation license: gpl-3.0 metrics: - sacrebleu - chrf widget: - text: "This may not make much sense to you, sir, but I'd like to ask your permission to date your daughter." example_title: "Translation: English-Hungarian" --- # Marian Translation model For further models, scripts and details, see [our repository](https://github.com/nytud/machine-translation) or [our demo site](https://juniper.nytud.hu/demo/nlp). There is a description of the REST API of our service. This model has been traind with a [MarianNMT](https://github.com/marian-nmt/marian-dev) v1.10.23; commit: 42f0b8b7 transformer-big typed environment. This repository contains our translation model (en-hu) which were published in MSZNY 2022 conference. - Source language: English - Target language: Hungarian - Pretrained on subcorpora from OPUS - Segments: 56.837.602 ## Limitations ## Results \| Model \| BLEU \| chrF-3 \| \| ------------- \| ------------- \| ------------- \| \| Google en-hu \| 25.30 \| 54.08 \| \| Marian-big-enhu \| 37.30 \| 61.61 \| ## Citation If you use this model, please cite the following paper: ``` @inproceedings {laki-yang-mt, title = {{Jobban fordítunk magyarra, mint a Google!}}, booktitle = {XVIII. Magyar Számítógépes Nyelvészeti Konferencia}, year = {2022}, publisher = {Szegedi Tudományegyetem, Informatikai Intézet}, address = {Szeged, Magyarország}, author = {Laki, László and Yang, Zijian Győző}, pages = {357--372} } ```
akshaychaudhary/distilbert-base-uncased-finetuned-cloud1-ner	akshaychaudhary	2022-02-14T13:30:57Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-cloud1-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cloud1-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0074 - Precision: 0.9714 - Recall: 0.9855 - F1: 0.9784 - Accuracy: 0.9972 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 166 \| 0.0160 \| 0.9653 \| 0.9420 \| 0.9535 \| 0.9945 \| \| No log \| 2.0 \| 332 \| 0.0089 \| 0.9623 \| 0.9855 \| 0.9737 \| 0.9965 \| \| No log \| 3.0 \| 498 \| 0.0074 \| 0.9714 \| 0.9855 \| 0.9784 \| 0.9972 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
sshasnain/wav2vec2-xls-r-300m-bangla-command-synthetic	sshasnain	2022-02-14T08:39:07Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-xls-r-300m-bangla-command-synthetic results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xls-r-300m-bangla-command-synthetic This model is a fine-tuned version of [sshasnain/wav2vec2-xls-r-300m-bangla-command](https://huggingface.co/sshasnain/wav2vec2-xls-r-300m-bangla-command) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0254 - eval_wer: 0.4311 - eval_runtime: 2.5036 - eval_samples_per_second: 76.689 - eval_steps_per_second: 9.586 - epoch: 35.71 - step: 1000 ## Model description More information needed ## Intended uses & 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: 100 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
DeltaHub/lora_t5-base_mrpc	DeltaHub	2022-02-14T06:32:18Z	4	0	transformers	[ "transformers", "pytorch", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:04Z	Need to work with OpenDelta ``` from transformers import AutoModelForSeq2SeqLM t5 = AutoModelForSeq2SeqLM.from_pretrained("t5-base") from opendelta import AutoDeltaModel delta = AutoDeltaModel.from_finetuned("DeltaHub/lora_t5-base_mrpc", backbone_model=t5) delta.log() ```
jatinshah/distilbert-base-uncased-finetuned-imdb	jatinshah	2022-02-14T04:17:56Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-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-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4726 ## Model description More information needed ## Intended uses & 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: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.7091 \| 1.0 \| 157 \| 2.4999 \| \| 2.5768 \| 2.0 \| 314 \| 2.4239 \| \| 2.5371 \| 3.0 \| 471 \| 2.4366 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0a0+0aef44c - Datasets 1.18.3 - Tokenizers 0.11.0
stellaathena/test-med	stellaathena	2022-02-14T02:28:29Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2022-03-02T23:29:05Z	--- license: apache-2.0 ---
jfarray/Model_bert-base-multilingual-uncased_10_Epochs	jfarray	2022-02-13T23:21:43Z	5	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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) ``` ## 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 11 with parameters: ``` {'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 11, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
groar/gpt-neo-1.3B-finetuned-escape2	groar	2022-02-13T20:59:30Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt_neo", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: gpt-neo-1.3B-finetuned-escape2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt-neo-1.3B-finetuned-escape2 This model is a fine-tuned version of [EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B) 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: 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: 10 ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
jfarray/Model_all-distilroberta-v1_50_Epochs	jfarray	2022-02-13T20:18:37Z	9	0	sentence-transformers	[ "sentence-transformers", "pytorch", "roberta", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {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) ``` ## 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 11 with parameters: ``` {'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 50, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 55, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
huggingartists/egor-letov	huggingartists	2022-02-13T20:16:48Z	8	0	transformers	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingartists", "lyrics", "lm-head", "causal-lm", "en", "dataset:huggingartists/egor-letov", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en datasets: - huggingartists/egor-letov tags: - huggingartists - lyrics - lm-head - causal-lm widget: - text: "I am" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/faa3dae99bf1fe365927608fd55c745a.330x330x1.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Егор Летов (Egor Letov)</div> <a href="https://genius.com/artists/egor-letov"> <div style="text-align: center; font-size: 14px;">@egor-letov</div> </a> </div> I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists). Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)! ## How does it work? To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist). ## Training data The model was trained on lyrics from Егор Летов (Egor Letov). Dataset is available [here](https://huggingface.co/datasets/huggingartists/egor-letov). And can be used with: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/egor-letov") ``` [Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1omrcegx/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 Егор Летов (Egor Letov)'s lyrics. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3lk60u9h) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3lk60u9h/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='huggingartists/egor-letov') generator("I am", num_return_sequences=5) ``` Or with Transformers library: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("huggingartists/egor-letov") model = AutoModelWithLMHead.from_pretrained("huggingartists/egor-letov") ``` ## 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 Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)
jfarray/Model_all-distilroberta-v1_30_Epochs	jfarray	2022-02-13T20:00:26Z	9	0	sentence-transformers	[ "sentence-transformers", "pytorch", "roberta", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {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) ``` ## 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 11 with parameters: ``` {'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 30, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 33, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
jfarray/Model_all-distilroberta-v1_5_Epochs	jfarray	2022-02-13T19:40:19Z	10	0	sentence-transformers	[ "sentence-transformers", "pytorch", "roberta", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {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) ``` ## 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 11 with parameters: ``` {'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 6, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
castorini/dkrr-dpr-tqa-retriever	castorini	2022-02-13T17:57:26Z	15	0	transformers	[ "transformers", "pytorch", "bert", "arxiv:2012.04584", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	This model is converted from the original DKRR [repo](https://github.com/facebookresearch/FiD) and ported into Pyserini: ``` @misc{izacard2020distilling, title={Distilling Knowledge from Reader to Retriever for Question Answering}, author={Gautier Izacard and Edouard Grave}, year={2020}, eprint={2012.04584}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
cscottp27/distilbert-base-uncased-finetuned-emotion	cscottp27	2022-02-13T13:19:16Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.923 - name: F1 type: f1 value: 0.9232542847906783 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2175 - Accuracy: 0.923 - F1: 0.9233 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8352 \| 1.0 \| 250 \| 0.3079 \| 0.91 \| 0.9086 \| \| 0.247 \| 2.0 \| 500 \| 0.2175 \| 0.923 \| 0.9233 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
srosy/distilbert-base-uncased-finetuned-emotion	srosy	2022-02-13T09:39:07Z	4	1	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.939 - name: F1 type: f1 value: 0.9391566069722169 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1582 - Accuracy: 0.939 - F1: 0.9392 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.4977 \| 1.0 \| 1000 \| 0.1919 \| 0.9255 \| 0.9253 \| \| 0.1545 \| 2.0 \| 2000 \| 0.1582 \| 0.939 \| 0.9392 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.8.1+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
timtarusov/distilbert-base-uncased-finetuned-emotion	timtarusov	2022-02-13T08:48:03Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.921 - name: F1 type: f1 value: 0.9211076096482195 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2274 - Accuracy: 0.921 - F1: 0.9211 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8308 \| 1.0 \| 250 \| 0.3319 \| 0.8955 \| 0.8897 \| \| 0.2516 \| 2.0 \| 500 \| 0.2274 \| 0.921 \| 0.9211 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
mujeensung/roberta-base_mnli_bc	mujeensung	2022-02-13T05:13:00Z	23	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- language: - en license: mit tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: roberta-base_mnli_bc results: - task: name: Text Classification type: text-classification dataset: name: GLUE MNLI type: glue args: mnli metrics: - name: Accuracy type: accuracy value: 0.9583768461882739 --- <!-- 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_mnli_bc This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.2125 - Accuracy: 0.9584 ## Model description More information needed ## Intended uses & 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.2015 \| 1.0 \| 16363 \| 0.1820 \| 0.9470 \| \| 0.1463 \| 2.0 \| 32726 \| 0.1909 \| 0.9559 \| \| 0.0768 \| 3.0 \| 49089 \| 0.2117 \| 0.9585 \| ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.1+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_50_Epochs	jfarray	2022-02-12T21:16:09Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- 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 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 11 with parameters: ``` {'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 50, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 55, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_30_Epochs	jfarray	2022-02-12T21:00:41Z	8	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- 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 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 11 with parameters: ``` {'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 30, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 33, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_10_Epochs	jfarray	2022-02-12T20:47:55Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- 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 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 11 with parameters: ``` {'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 11, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_5_Epochs	jfarray	2022-02-12T20:37:59Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- 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 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 11 with parameters: ``` {'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 6, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
jfarray/Model_distiluse-base-multilingual-cased-v1_100_Epochs	jfarray	2022-02-12T19:45:48Z	137	0	sentence-transformers	[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 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) ``` ## 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 11 with parameters: ``` {'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 100, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 110, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
jgammack/multi-qa-MTL-distilbert-base-uncased-40k	jgammack	2022-02-12T14:14:47Z	144	0	sentence-transformers	[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # jgammack/multi-qa-MTL-distilbert-base-uncased-40k 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('jgammack/multi-qa-MTL-distilbert-base-uncased-40k') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('jgammack/multi-qa-MTL-distilbert-base-uncased-40k') model = AutoModel.from_pretrained('jgammack/multi-qa-MTL-distilbert-base-uncased-40k') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=jgammack/multi-qa-MTL-distilbert-base-uncased-40k) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
jfarray/Model_distiluse-base-multilingual-cased-v1_5_Epochs	jfarray	2022-02-12T13:43:01Z	131	0	sentence-transformers	[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 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) ``` ## 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 11 with parameters: ``` {'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 6, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
huggingartists/death-grips	huggingartists	2022-02-12T08:56:17Z	4	1	transformers	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingartists", "lyrics", "lm-head", "causal-lm", "en", "dataset:huggingartists/death-grips", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en datasets: - huggingartists/death-grips tags: - huggingartists - lyrics - lm-head - causal-lm widget: - text: "I am" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/de4ca387303c4b46007ca1072c2e57d0.600x600x1.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Death Grips</div> <a href="https://genius.com/artists/death-grips"> <div style="text-align: center; font-size: 14px;">@death-grips</div> </a> </div> I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists). Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)! ## How does it work? To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist). ## Training data The model was trained on lyrics from Death Grips. Dataset is available [here](https://huggingface.co/datasets/huggingartists/death-grips). And can be used with: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/death-grips") ``` [Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2hmeenl7/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 Death Grips's lyrics. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/226ak5bw) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/226ak5bw/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='huggingartists/death-grips') generator("I am", num_return_sequences=5) ``` Or with Transformers library: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("huggingartists/death-grips") model = AutoModelWithLMHead.from_pretrained("huggingartists/death-grips") ``` ## 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 Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)
HHousen/household-rooms	HHousen	2022-02-12T06:21:05Z	77	5	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-02T23:29:04Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: household-rooms results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.8482142686843872 --- # household-rooms Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### bathroom ![bathroom](images/bathroom.jpg) #### bedroom ![bedroom](images/bedroom.jpg) #### dining room ![dining room](images/dining_room.jpg) #### kitchen ![kitchen](images/kitchen.jpg) #### living room ![living room](images/living_room.jpg)
thyagosme/bert-base-uncased-finetuned-swag	thyagosme	2022-02-12T02:13:46Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "multiple-choice", "generated_from_trainer", "dataset:swag", "license:apache-2.0", "endpoints_compatible", "region:us" ]	multiple-choice	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - swag metrics: - accuracy model-index: - name: bert-base-uncased-finetuned-swag 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-finetuned-swag This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset. It achieves the following results on the evaluation set: - Loss: 1.0438 - Accuracy: 0.7915 ## Model description More information needed ## Intended uses & 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.7708 \| 1.0 \| 4597 \| 0.6025 \| 0.7659 \| \| 0.4015 \| 2.0 \| 9194 \| 0.6287 \| 0.7841 \| \| 0.1501 \| 3.0 \| 13791 \| 1.0438 \| 0.7915 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
jgammack/multi-qa-distilbert-base-uncased	jgammack	2022-02-11T23:40:41Z	141	0	sentence-transformers	[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # jgammack/multi-qa-distilbert-base-uncased 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('jgammack/multi-qa-distilbert-base-uncased') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('jgammack/multi-qa-distilbert-base-uncased') model = AutoModel.from_pretrained('jgammack/multi-qa-distilbert-base-uncased') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=jgammack/multi-qa-distilbert-base-uncased) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
huggingtweets/sauce__world	huggingtweets	2022-02-11T22:14:53Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/sauce__world/1644617665459/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1488960307305218049/nAFuBERK_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">poolboy sauce world</div> <div style="text-align: center; font-size: 14px;">@sauce__world</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 poolboy sauce world. \| Data \| poolboy sauce world \| \| --- \| --- \| \| Tweets downloaded \| 3192 \| \| Retweets \| 323 \| \| Short tweets \| 513 \| \| Tweets kept \| 2356 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/20dtxww4/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 @sauce__world's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/vh9fgsnx) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/vh9fgsnx/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/sauce__world') 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)
BigSalmon/InformalToFormalLincoln21	BigSalmon	2022-02-11T21:24:42Z	11	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:04Z	Informal to Formal: Wordy to Concise: Fill Missing Phrase: ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln21") model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln21") ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2 (The model for this space changes over time) ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time) ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ```` ``` infill: increasing the number of sidewalks in suburban areas will [MASK]. Translated into the Style of Abraham Lincoln: increasing the number of sidewalks in suburban areas will ( ( enhance / maximize ) community cohesion / facilitate ( communal ties / the formation of neighborhood camaraderie ) / forge neighborly relations / lend themselves to the advancement of neighborly ties / plant the seeds of community building / flower anew the bonds of friendship / invite the budding of neighborhood rapport / enrich neighborhood life ). infill: corn fields [MASK], [MASK] visibly as one ventures beyond chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), ( manifesting themselves ) visibly as one ventures beyond chicago. infill: the [MASK] the SAT will soon be [MASK]. [MASK] an examination undertaken on one's laptop. [MASK] will allow students to retrieve test results promptly. Translated into the Style of Abraham Lincoln: the ( conventional form of ) the SAT will soon be ( consigned to history ). ( replacing it will be ) an examination undertaken on one's laptop. ( so doing ) will allow students to retrieve test results promptly. infill: ``` ``` * wordy: chancing upon a linux user is a rare occurrence in the present day. Translate into Concise Text: present-day linux users are rare. * wordy: an interest in classical music is becoming more and more less popular. Translate into Concise Text: classical music appreciation is dwindling. Translate into Concise Text: waning interest in classic music persists. Translate into Concise Text: interest in classic music is fading. * wordy: the ice cream was only one dollar, but it was not a good value for the size. Translate into Concise Text: the one dollar ice cream was overpriced for its size. Translate into Concise Text: overpriced, the one dollar ice cream was small. * wordy: ```
microsoft/codebert-base	microsoft	2022-02-11T19:59:44Z	574,944	236	transformers	[ "transformers", "pytorch", "tf", "jax", "rust", "roberta", "feature-extraction", "arxiv:2002.08155", "endpoints_compatible", "region:us" ]	feature-extraction	2022-03-02T23:29:05Z	## CodeBERT-base Pretrained weights for [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://arxiv.org/abs/2002.08155). ### Training Data The model is trained on bi-modal data (documents & code) of [CodeSearchNet](https://github.com/github/CodeSearchNet) ### Training Objective This model is initialized with Roberta-base and trained with MLM+RTD objective (cf. the paper). ### Usage Please see [the official repository](https://github.com/microsoft/CodeBERT) for scripts that support "code search" and "code-to-document generation". ### Reference 1. [CodeBERT trained with Masked LM objective](https://huggingface.co/microsoft/codebert-base-mlm) (suitable for code completion) 2. 🤗 [Hugging Face's CodeBERTa](https://huggingface.co/huggingface/CodeBERTa-small-v1) (small size, 6 layers) ### Citation ```bibtex @misc{feng2020codebert, title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages}, author={Zhangyin Feng and Daya Guo and Duyu Tang and Nan Duan and Xiaocheng Feng and Ming Gong and Linjun Shou and Bing Qin and Ting Liu and Daxin Jiang and Ming Zhou}, year={2020}, eprint={2002.08155}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
jgammack/multi-qa-SAE-distilbert-base-uncased	jgammack	2022-02-11T19:45:37Z	138	0	sentence-transformers	[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # jgammack/multi-qa-SAE-distilbert-base 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('jgammack/multi-qa-SAE-distilbert-base') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('jgammack/multi-qa-SAE-distilbert-base') model = AutoModel.from_pretrained('jgammack/multi-qa-SAE-distilbert-base') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=jgammack/multi-qa-SAE-distilbert-base) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
mbateman/distilbert-base-uncased-finetuned-squad-d5716d28	mbateman	2022-02-11T09:26:12Z	4	0	transformers	[ "transformers", "pytorch", "distilbert", "fill-mask", "question-answering", "en", "dataset:squad", "arxiv:1910.01108", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- language: - en thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg tags: - question-answering license: apache-2.0 datasets: - squad metrics: - squad --- # DistilBERT with a second step of distillation ## Model description This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation. In this version, the following pre-trained models were used: * Student: `distilbert-base-uncased` * Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1` ## Training data This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows: ```python from datasets import load_dataset squad = load_dataset('squad') ``` ## Training procedure ## Eval results \| \| Exact Match \| F1 \| \|------------------\|-------------\|------\| \| DistilBERT paper \| 79.1 \| 86.9 \| \| Ours \| 78.4 \| 86.5 \| The scores were calculated using the `squad` metric from `datasets`. ### BibTeX entry and citation info ```bibtex @misc{sanh2020distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, year={2020}, eprint={1910.01108}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
shahukareem/wav2vec2-xls-r-1b-dv	shahukareem	2022-02-11T08:15:25Z	5	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "dv", "robust-speech-event", "model_for_talk", "dataset:mozilla-foundation/common_voice_8_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_8_0 - generated_from_trainer - dv - robust-speech-event - model_for_talk datasets: - mozilla-foundation/common_voice_8_0 model-index: - name: wav2vec2-xls-r-1b-dv results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8 type: mozilla-foundation/common_voice_8_0 args: dv metrics: - name: Test WER type: wer value: 21.32 - name: Test CER type: cer value: 3.43 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xls-r-1b-dv This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.1702 - Wer: 0.2123 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.5e-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: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 3.8412 \| 0.66 \| 400 \| 0.7160 \| 0.7913 \| \| 0.6832 \| 1.33 \| 800 \| 0.3401 \| 0.5268 \| \| 0.4624 \| 1.99 \| 1200 \| 0.2671 \| 0.4683 \| \| 0.3832 \| 2.65 \| 1600 \| 0.2395 \| 0.4410 \| \| 0.3443 \| 3.32 \| 2000 \| 0.2410 \| 0.4296 \| \| 0.324 \| 3.98 \| 2400 \| 0.2302 \| 0.4143 \| \| 0.2934 \| 4.64 \| 2800 \| 0.2402 \| 0.4136 \| \| 0.2773 \| 5.31 \| 3200 \| 0.2134 \| 0.4088 \| \| 0.2638 \| 5.97 \| 3600 \| 0.2072 \| 0.4037 \| \| 0.2479 \| 6.63 \| 4000 \| 0.2036 \| 0.3876 \| \| 0.2424 \| 7.3 \| 4400 \| 0.2037 \| 0.3767 \| \| 0.2249 \| 7.96 \| 4800 \| 0.1959 \| 0.3802 \| \| 0.2169 \| 8.62 \| 5200 \| 0.1943 \| 0.3813 \| \| 0.2109 \| 9.29 \| 5600 \| 0.1944 \| 0.3691 \| \| 0.1991 \| 9.95 \| 6000 \| 0.1870 \| 0.3589 \| \| 0.1917 \| 10.61 \| 6400 \| 0.1834 \| 0.3485 \| \| 0.1862 \| 11.28 \| 6800 \| 0.1857 \| 0.3486 \| \| 0.1744 \| 11.94 \| 7200 \| 0.1812 \| 0.3330 \| \| 0.171 \| 12.6 \| 7600 \| 0.1797 \| 0.3436 \| \| 0.1599 \| 13.27 \| 8000 \| 0.1839 \| 0.3319 \| \| 0.1597 \| 13.93 \| 8400 \| 0.1737 \| 0.3385 \| \| 0.1494 \| 14.59 \| 8800 \| 0.1807 \| 0.3239 \| \| 0.1444 \| 15.26 \| 9200 \| 0.1750 \| 0.3155 \| \| 0.1382 \| 15.92 \| 9600 \| 0.1705 \| 0.3084 \| \| 0.1299 \| 16.58 \| 10000 \| 0.1777 \| 0.2999 \| \| 0.1306 \| 17.25 \| 10400 \| 0.1765 \| 0.3056 \| \| 0.1239 \| 17.91 \| 10800 \| 0.1676 \| 0.2864 \| \| 0.1149 \| 18.57 \| 11200 \| 0.1774 \| 0.2861 \| \| 0.1134 \| 19.24 \| 11600 \| 0.1654 \| 0.2699 \| \| 0.1101 \| 19.9 \| 12000 \| 0.1621 \| 0.2651 \| \| 0.1038 \| 20.56 \| 12400 \| 0.1686 \| 0.2610 \| \| 0.1038 \| 21.23 \| 12800 \| 0.1722 \| 0.2559 \| \| 0.0988 \| 21.89 \| 13200 \| 0.1708 \| 0.2486 \| \| 0.0949 \| 22.55 \| 13600 \| 0.1696 \| 0.2453 \| \| 0.0913 \| 23.22 \| 14000 \| 0.1677 \| 0.2424 \| \| 0.0879 \| 23.88 \| 14400 \| 0.1640 \| 0.2359 \| \| 0.0888 \| 24.54 \| 14800 \| 0.1697 \| 0.2347 \| \| 0.0826 \| 25.21 \| 15200 \| 0.1709 \| 0.2314 \| \| 0.0819 \| 25.87 \| 15600 \| 0.1679 \| 0.2256 \| \| 0.0793 \| 26.53 \| 16000 \| 0.1701 \| 0.2214 \| \| 0.0773 \| 27.2 \| 16400 \| 0.1682 \| 0.2176 \| \| 0.0783 \| 27.86 \| 16800 \| 0.1685 \| 0.2165 \| \| 0.074 \| 28.52 \| 17200 \| 0.1688 \| 0.2155 \| \| 0.0753 \| 29.19 \| 17600 \| 0.1695 \| 0.2110 \| \| 0.0699 \| 29.85 \| 18000 \| 0.1702 \| 0.2123 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0
espnet/Wangyou_Zhang_chime4_enh_train_enh_beamformer_mvdr_raw	espnet	2022-02-11T06:24:00Z	67	1	espnet	[ "espnet", "audio", "audio-to-audio", "dataset:chime4", "arxiv:1804.00015", "arxiv:2011.03706", "license:cc-by-4.0", "region:us" ]	audio-to-audio	2022-03-02T23:29:05Z	--- tags: - espnet - audio - audio-to-audio language: datasets: - chime4 license: cc-by-4.0 --- ## ESPnet2 ENH model ### `espnet/Wangyou_Zhang_chime4_enh_train_enh_beamformer_mvdr_raw` This model was trained by Wangyou Zhang using chime4 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet pip install -e . cd egs2/chime4/enh1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/Wangyou_Zhang_chime4_enh_train_enh_beamformer_mvdr_raw ``` ## ENH config <details><summary>expand</summary> ``` config: conf/tuning/train_enh_beamformer_mvdr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/enh_train_enh_beamformer_mvdr_raw ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 2 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 35841 dist_launcher: null multiprocessing_distributed: true cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 70 patience: 4 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - si_snr - max - - valid - loss - min keep_nbest_models: 1 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null unused_parameters: false use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null pretrain_path: null init_param: [] freeze_param: [] num_iters_per_epoch: null batch_size: 8 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/enh_stats_16k/train/speech_mix_shape - exp/enh_stats_16k/train/speech_ref1_shape - exp/enh_stats_16k/train/noise_ref1_shape valid_shape_file: - exp/enh_stats_16k/valid/speech_mix_shape - exp/enh_stats_16k/valid/speech_ref1_shape - exp/enh_stats_16k/valid/noise_ref1_shape batch_type: folded valid_batch_type: null fold_length: - 80000 - 80000 - 80000 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/tr05_simu_isolated_6ch_track/wav.scp - speech_mix - sound - - dump/raw/tr05_simu_isolated_6ch_track/spk1.scp - speech_ref1 - sound - - dump/raw/tr05_simu_isolated_6ch_track/noise1.scp - noise_ref1 - sound valid_data_path_and_name_and_type: - - dump/raw/dt05_simu_isolated_6ch_track/wav.scp - speech_mix - sound - - dump/raw/dt05_simu_isolated_6ch_track/spk1.scp - speech_ref1 - sound - - dump/raw/dt05_simu_isolated_6ch_track/noise1.scp - noise_ref1 - sound allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.001 eps: 1.0e-08 weight_decay: 0 scheduler: reducelronplateau scheduler_conf: mode: min factor: 0.5 patience: 1 init: xavier_uniform model_conf: loss_type: mask_mse mask_type: PSM^2 use_preprocessor: false encoder: stft encoder_conf: n_fft: 512 hop_length: 128 separator: wpe_beamformer separator_conf: num_spk: 1 loss_type: mask_mse use_wpe: false wnet_type: blstmp wlayers: 3 wunits: 300 wprojs: 320 wdropout_rate: 0.0 taps: 5 delay: 3 use_dnn_mask_for_wpe: true use_beamformer: true bnet_type: blstmp blayers: 3 bunits: 512 bprojs: 512 badim: 320 ref_channel: 3 use_noise_mask: true beamformer_type: mvdr_souden bdropout_rate: 0.0 decoder: stft decoder_conf: n_fft: 512 hop_length: 128 required: - output_dir version: 0.9.7 distributed: true ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{li2021espnetse, title={{ESPnet-SE}: End-to-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration}, author={Li, Chenda and Shi, Jing and Zhang, Wangyou and Subramanian, Aswin Shanmugam and Chang, Xuankai and Kamo, Naoyuki and Hira, Moto and Hayashi, Tomoki and Boeddeker, Christoph and Chen, Zhuo and Watanabe, Shinji}, booktitle={Proc. IEEE Spoken Language Technology Workshop (SLT)}, pages={785--792}, year={2021}, } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } @inproceedings{li2021espnetse, title={{ESPnet-SE}: End-to-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration}, author={Li, Chenda and Shi, Jing and Zhang, Wangyou and Subramanian, Aswin Shanmugam and Chang, Xuankai and Kamo, Naoyuki and Hira, Moto and Hayashi, Tomoki and Boeddeker, Christoph and Chen, Zhuo and Watanabe, Shinji}, year={2020}, eprint={2011.03706}, archivePrefix={arXiv}, primaryClass={eess.AS} } ```
infinitejoy/wav2vec2-large-xls-r-300m-indonesian	infinitejoy	2022-02-11T05:56:28Z	3	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "id", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - id license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-indonesian results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-indonesian This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - ID dataset. It achieves the following results on the evaluation set: - Loss: 0.2759 - Wer: 0.3256 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 4000 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 3.0387 \| 4.72 \| 1000 \| 3.0892 \| 1.0 \| \| 1.7911 \| 9.43 \| 2000 \| 0.8451 \| 0.6702 \| \| 1.2826 \| 14.15 \| 3000 \| 0.4211 \| 0.4166 \| \| 1.1802 \| 18.87 \| 4000 \| 0.3508 \| 0.4690 \| \| 1.1065 \| 23.58 \| 5000 \| 0.3319 \| 0.4662 \| \| 1.0921 \| 28.3 \| 6000 \| 0.3056 \| 0.3880 \| \| 1.0366 \| 33.02 \| 7000 \| 0.2997 \| 0.3665 \| \| 0.9988 \| 37.74 \| 8000 \| 0.2972 \| 0.3653 \| \| 0.9864 \| 42.45 \| 9000 \| 0.2697 \| 0.3371 \| \| 0.9558 \| 47.17 \| 10000 \| 0.2739 \| 0.3141 \| \| 0.9094 \| 51.89 \| 11000 \| 0.2657 \| 0.3533 \| \| 0.9034 \| 56.6 \| 12000 \| 0.2699 \| 0.3397 \| \| 0.8907 \| 61.32 \| 13000 \| 0.2765 \| 0.3470 \| \| 0.8631 \| 66.04 \| 14000 \| 0.2774 \| 0.3346 \| \| 0.8389 \| 70.75 \| 15000 \| 0.2743 \| 0.3365 \| \| 0.8214 \| 75.47 \| 16000 \| 0.2778 \| 0.3201 \| \| 0.8195 \| 80.19 \| 17000 \| 0.2725 \| 0.3286 \| \| 0.7994 \| 84.91 \| 18000 \| 0.2782 \| 0.3315 \| \| 0.7816 \| 89.62 \| 19000 \| 0.2775 \| 0.3363 \| \| 0.7816 \| 94.34 \| 20000 \| 0.2731 \| 0.3278 \| \| 0.7635 \| 99.06 \| 21000 \| 0.2767 \| 0.3259 \| ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0
lgris/WavLM-large-CORAA-pt	lgris	2022-02-10T23:21:45Z	12	0	transformers	[ "transformers", "pytorch", "wavlm", "automatic-speech-recognition", "generated_from_trainer", "pt", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - pt license: apache-2.0 tags: - generated_from_trainer - pt model-index: - name: WavLM-large-CORAA-pt results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # WavLM-large-CORAA-pt This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on [CORAA dataset](https://github.com/nilc-nlp/CORAA). It achieves the following results on the evaluation set: - Loss: 0.6144 - Wer: 0.3840 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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: 1000 - training_steps: 40000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| No log \| 0.04 \| 1000 \| 1.9230 \| 0.9960 \| \| 5.153 \| 0.08 \| 2000 \| 1.3733 \| 0.8444 \| \| 5.153 \| 0.13 \| 3000 \| 1.1992 \| 0.7362 \| \| 1.367 \| 0.17 \| 4000 \| 1.1289 \| 0.6957 \| \| 1.367 \| 0.21 \| 5000 \| 1.0357 \| 0.6470 \| \| 1.1824 \| 0.25 \| 6000 \| 1.0216 \| 0.6201 \| \| 1.1824 \| 0.29 \| 7000 \| 0.9338 \| 0.6036 \| \| 1.097 \| 0.33 \| 8000 \| 0.9149 \| 0.5760 \| \| 1.097 \| 0.38 \| 9000 \| 0.8885 \| 0.5541 \| \| 1.0254 \| 0.42 \| 10000 \| 0.8678 \| 0.5366 \| \| 1.0254 \| 0.46 \| 11000 \| 0.8349 \| 0.5323 \| \| 0.9782 \| 0.5 \| 12000 \| 0.8230 \| 0.5155 \| \| 0.9782 \| 0.54 \| 13000 \| 0.8245 \| 0.5049 \| \| 0.9448 \| 0.59 \| 14000 \| 0.7802 \| 0.4990 \| \| 0.9448 \| 0.63 \| 15000 \| 0.7650 \| 0.4900 \| \| 0.9092 \| 0.67 \| 16000 \| 0.7665 \| 0.4796 \| \| 0.9092 \| 0.71 \| 17000 \| 0.7568 \| 0.4795 \| \| 0.8764 \| 0.75 \| 18000 \| 0.7403 \| 0.4615 \| \| 0.8764 \| 0.8 \| 19000 \| 0.7219 \| 0.4644 \| \| 0.8498 \| 0.84 \| 20000 \| 0.7180 \| 0.4502 \| \| 0.8498 \| 0.88 \| 21000 \| 0.7017 \| 0.4436 \| \| 0.8278 \| 0.92 \| 22000 \| 0.6992 \| 0.4395 \| \| 0.8278 \| 0.96 \| 23000 \| 0.7021 \| 0.4329 \| \| 0.8077 \| 1.0 \| 24000 \| 0.6892 \| 0.4265 \| \| 0.8077 \| 1.05 \| 25000 \| 0.6940 \| 0.4248 \| \| 0.7486 \| 1.09 \| 26000 \| 0.6767 \| 0.4202 \| \| 0.7486 \| 1.13 \| 27000 \| 0.6734 \| 0.4150 \| \| 0.7459 \| 1.17 \| 28000 \| 0.6650 \| 0.4152 \| \| 0.7459 \| 1.21 \| 29000 \| 0.6559 \| 0.4078 \| \| 0.7304 \| 1.26 \| 30000 \| 0.6536 \| 0.4088 \| \| 0.7304 \| 1.3 \| 31000 \| 0.6537 \| 0.4025 \| \| 0.7183 \| 1.34 \| 32000 \| 0.6462 \| 0.4008 \| \| 0.7183 \| 1.38 \| 33000 \| 0.6381 \| 0.3973 \| \| 0.7059 \| 1.42 \| 34000 \| 0.6266 \| 0.3930 \| \| 0.7059 \| 1.46 \| 35000 \| 0.6280 \| 0.3921 \| \| 0.6983 \| 1.51 \| 36000 \| 0.6248 \| 0.3897 \| \| 0.6983 \| 1.55 \| 37000 \| 0.6275 \| 0.3872 \| \| 0.6892 \| 1.59 \| 38000 \| 0.6199 \| 0.3852 \| \| 0.6892 \| 1.63 \| 39000 \| 0.6180 \| 0.3842 \| \| 0.691 \| 1.67 \| 40000 \| 0.6144 \| 0.3840 \| ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0
lgris/wavlm-large-CORAA-pt-cv7	lgris	2022-02-10T23:16:09Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wavlm", "automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "pt", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - pt license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_7_0 - generated_from_trainer - pt datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: wavlm-large-CORAA-pt-cv7 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. --> # wavlm-large-CORAA-pt-cv7 This model is a fine-tuned version of [lgris/WavLM-large-CORAA-pt](https://huggingface.co/lgris/WavLM-large-CORAA-pt) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.2546 - Wer: 0.2261 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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: 100 - training_steps: 5000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.6029 \| 0.13 \| 100 \| 0.3679 \| 0.3347 \| \| 0.5297 \| 0.26 \| 200 \| 0.3516 \| 0.3227 \| \| 0.5134 \| 0.39 \| 300 \| 0.3327 \| 0.3167 \| \| 0.4941 \| 0.52 \| 400 \| 0.3281 \| 0.3122 \| \| 0.4816 \| 0.65 \| 500 \| 0.3154 \| 0.3102 \| \| 0.4649 \| 0.78 \| 600 \| 0.3199 \| 0.3058 \| \| 0.461 \| 0.91 \| 700 \| 0.3047 \| 0.2974 \| \| 0.4613 \| 1.04 \| 800 \| 0.3006 \| 0.2900 \| \| 0.4198 \| 1.17 \| 900 \| 0.2951 \| 0.2891 \| \| 0.3864 \| 1.3 \| 1000 \| 0.2989 \| 0.2862 \| \| 0.3963 \| 1.43 \| 1100 \| 0.2932 \| 0.2830 \| \| 0.3953 \| 1.56 \| 1200 \| 0.2936 \| 0.2829 \| \| 0.3962 \| 1.69 \| 1300 \| 0.2952 \| 0.2773 \| \| 0.3811 \| 1.82 \| 1400 \| 0.2915 \| 0.2748 \| \| 0.3736 \| 1.95 \| 1500 \| 0.2839 \| 0.2684 \| \| 0.3507 \| 2.08 \| 1600 \| 0.2914 \| 0.2678 \| \| 0.3277 \| 2.21 \| 1700 \| 0.2895 \| 0.2652 \| \| 0.3344 \| 2.34 \| 1800 \| 0.2843 \| 0.2673 \| \| 0.335 \| 2.47 \| 1900 \| 0.2821 \| 0.2635 \| \| 0.3559 \| 2.6 \| 2000 \| 0.2830 \| 0.2599 \| \| 0.3254 \| 2.73 \| 2100 \| 0.2711 \| 0.2577 \| \| 0.3263 \| 2.86 \| 2200 \| 0.2685 \| 0.2546 \| \| 0.3266 \| 2.99 \| 2300 \| 0.2679 \| 0.2521 \| \| 0.3066 \| 3.12 \| 2400 \| 0.2727 \| 0.2526 \| \| 0.2998 \| 3.25 \| 2500 \| 0.2648 \| 0.2537 \| \| 0.2961 \| 3.38 \| 2600 \| 0.2630 \| 0.2519 \| \| 0.3046 \| 3.51 \| 2700 \| 0.2684 \| 0.2506 \| \| 0.3006 \| 3.64 \| 2800 \| 0.2604 \| 0.2492 \| \| 0.2992 \| 3.77 \| 2900 \| 0.2682 \| 0.2508 \| \| 0.2775 \| 3.9 \| 3000 \| 0.2732 \| 0.2440 \| \| 0.2903 \| 4.03 \| 3100 \| 0.2659 \| 0.2427 \| \| 0.2535 \| 4.16 \| 3200 \| 0.2650 \| 0.2433 \| \| 0.2714 \| 4.29 \| 3300 \| 0.2588 \| 0.2394 \| \| 0.2636 \| 4.42 \| 3400 \| 0.2652 \| 0.2434 \| \| 0.2647 \| 4.55 \| 3500 \| 0.2624 \| 0.2371 \| \| 0.2796 \| 4.67 \| 3600 \| 0.2611 \| 0.2373 \| \| 0.2644 \| 4.8 \| 3700 \| 0.2604 \| 0.2341 \| \| 0.2657 \| 4.93 \| 3800 \| 0.2567 \| 0.2331 \| \| 0.2423 \| 5.06 \| 3900 \| 0.2594 \| 0.2322 \| \| 0.2556 \| 5.19 \| 4000 \| 0.2587 \| 0.2323 \| \| 0.2327 \| 5.32 \| 4100 \| 0.2639 \| 0.2299 \| \| 0.2613 \| 5.45 \| 4200 \| 0.2569 \| 0.2310 \| \| 0.2382 \| 5.58 \| 4300 \| 0.2585 \| 0.2298 \| \| 0.2404 \| 5.71 \| 4400 \| 0.2543 \| 0.2287 \| \| 0.2368 \| 5.84 \| 4500 \| 0.2553 \| 0.2286 \| \| 0.2514 \| 5.97 \| 4600 \| 0.2517 \| 0.2279 \| \| 0.2415 \| 6.1 \| 4700 \| 0.2524 \| 0.2270 \| \| 0.2338 \| 6.23 \| 4800 \| 0.2540 \| 0.2265 \| \| 0.219 \| 6.36 \| 4900 \| 0.2549 \| 0.2263 \| \| 0.2428 \| 6.49 \| 5000 \| 0.2546 \| 0.2261 \| ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0
emre/wav2vec2-xls-r-300m-Turkish-Tr-med	emre	2022-02-10T22:56:56Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "robust-speech-event", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer - robust-speech-event datasets: - common_voice model-index: - name: wav2vec2-xls-r-300m-Turkish-Tr-med results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xls-r-300m-Turkish-Tr-med This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4727 - Wer: 0.4677 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 60 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 4.8093 \| 4.21 \| 400 \| 2.7831 \| 1.0 \| \| 0.9881 \| 8.42 \| 800 \| 0.5088 \| 0.6681 \| \| 0.3519 \| 12.63 \| 1200 \| 0.4496 \| 0.6007 \| \| 0.2436 \| 16.84 \| 1600 \| 0.4993 \| 0.5654 \| \| 0.1874 \| 21.05 \| 2000 \| 0.4793 \| 0.5530 \| \| 0.1561 \| 25.26 \| 2400 \| 0.5187 \| 0.5589 \| \| 0.1336 \| 29.47 \| 2800 \| 0.5135 \| 0.5311 \| \| 0.1163 \| 33.68 \| 3200 \| 0.4960 \| 0.5143 \| \| 0.1056 \| 37.89 \| 3600 \| 0.4795 \| 0.5045 \| \| 0.0959 \| 42.11 \| 4000 \| 0.4883 \| 0.4987 \| \| 0.0819 \| 46.32 \| 4400 \| 0.4799 \| 0.4903 \| \| 0.0756 \| 50.53 \| 4800 \| 0.4822 \| 0.4831 \| \| 0.0692 \| 54.74 \| 5200 \| 0.4621 \| 0.4762 \| \| 0.062 \| 58.95 \| 5600 \| 0.4727 \| 0.4677 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
ibombonato/vit-age-classifier	ibombonato	2022-02-10T22:06:51Z	76	6	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-02T23:29:05Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: vit-age-classifier results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.8364999890327454 --- # vit-age-classifier Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
satyaalmasian/temporal_tagger_German_GELECTRA	satyaalmasian	2022-02-10T15:23:51Z	61	1	transformers	[ "transformers", "pytorch", "electra", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	# BERT based temporal tagged Token classifier for temporal tagging of plain text using German Gelectra model. # Model description GELECTRA is a transformer (ELECTRA) model pretrained on a large corpus of German data in a self-supervised fashion. We use GELECTRA for token classification to tag the tokens in text with classes (tags are from english timex3 format): ``` O -- outside of a tag I-TIME -- inside tag of time B-TIME -- beginning tag of time I-DATE -- inside tag of date B-DATE -- beginning tag of date I-DURATION -- inside tag of duration B-DURATION -- beginning tag of duration I-SET -- inside tag of the set B-SET -- beginning tag of the set ``` # Intended uses & limitations This model is best used accompanied with code from the [repository](https://github.com/satya77/Transformer_Temporal_Tagger). Especially for inference, the direct output might be noisy and hard to decipher, in the repository we provide alignment functions and voting strategies for the final output. The repo examples the english models, the german model can be used the same way. # How to use you can load the model as follows: ``` tokenizer = AutoTokenizer.from_pretrained("satyaalmasian/temporal_tagger_German_GELECTRA", use_fast=False) model = BertForTokenClassification.from_pretrained("satyaalmasian/temporal_tagger_German_GELECTRA") ``` for inference use: ``` processed_text = tokenizer(input_text, return_tensors="pt") result = model(**processed_text) classification= result[0] ``` for an example with post-processing, refer to the [repository](https://github.com/satya77/Transformer_Temporal_Tagger). We provide a function `merge_tokens` to decipher the output. to further fine-tune, use the `Trainer` from hugginface. An example of a similar fine-tuning can be found [here](https://github.com/satya77/Transformer_Temporal_Tagger/blob/master/run_token_classifier.py). # Training data For pre-training we use a large corpus of automatically annotated news articles with heideltime. We use 2 data sources for fine-tunning. : [Tempeval-3](https://www.cs.york.ac.uk/semeval-2013/task1/index.php%3Fid=data.html),automatically translated to gemran, [KRAUTS dataset](https://github.com/JannikStroetgen/KRAUTS). # Training procedure The model is trained from publicly available checkpoints on huggingface (`deepset/gelectra-large`), with a batch size of 192. We use a learning rate of 1e-07 with an Adam optimizer and linear weight decay for pretraining. For fine-tuning we use a batch size of 16. We use a learning rate of 5e-05 with an Adam optimizer and linear weight decay. We fine-tune with 3 different random seeds, this version of the model is the only seed=7. For training, we use 2 NVIDIA A100 GPUs with 40GB of memory.
ajaiswal1008/wav2vec2-large-xls-r-300m-hi-colab_new	ajaiswal1008	2022-02-10T15:11:14Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-hi-colab_new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-hi-colab_new This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
junnyu/roformer_base_wwm_cluecorpussmall	junnyu	2022-02-10T12:26:39Z	6	2	transformers	[ "transformers", "pytorch", "roformer", "fill-mask", "tf2.0", "paddlepaddle", "zh", "arxiv:2104.09864", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: zh tags: - roformer - pytorch - tf2.0 - paddlepaddle widget: - text: "今天[MASK]很好，我想去公园玩！" --- ## 介绍 Pretrained model on 13G Chinese corpus(clue corpus small). Masked language modeling(MLM) and sentence order prediction(SOP) are used as training task. 在13g的clue corpus small数据集上进行的预训练，使用了`Whole Mask LM` 和 `SOP` 任务训练逻辑参考了这里。https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/ernie-1.0 ## 训练细节： - paddlepaddle+paddlenlp - V100 x 4 - batch size 256 - max_seq_len 512 - max_lr 0.0001 - min_lr 0.00001 - weight_decay 0.01 - grad_clip 1.0 - 总共训练的句子```12830w + 25615w + 25614.5w + 25646.5w + 25617w = 27648w``` - 约等于512 batch size, 100w步条件下的54% 最终loss： ```python [2022-02-05 16:05:59,067] [ INFO] - global step 170100, loss: 2.651634932, lm_loss: 2.603405, sop_loss: 0.048229, speed: 1.06 steps/s, ips: 271.68 seqs/s, learning rate: 6.66465e-05, loss_scaling: 137438.96875, num_good_steps: 356, num_bad_steps: 0 [2022-02-05 16:07:28,227] [ INFO] - global step 170200, loss: 2.822231531, lm_loss: 2.662831, sop_loss: 0.159401, speed: 1.12 steps/s, ips: 287.13 seqs/s, learning rate: 6.66263e-05, loss_scaling: 137438.96875, num_good_steps: 59, num_bad_steps: 0 [2022-02-05 16:08:57,346] [ INFO] - global step 170300, loss: 2.710968971, lm_loss: 2.673646, sop_loss: 0.037323, speed: 1.12 steps/s, ips: 287.26 seqs/s, learning rate: 6.66061e-05, loss_scaling: 137438.96875, num_good_steps: 159, num_bad_steps: 0 [2022-02-05 16:10:26,698] [ INFO] - global step 170400, loss: 2.867662907, lm_loss: 2.619032, sop_loss: 0.248631, speed: 1.12 steps/s, ips: 286.51 seqs/s, learning rate: 6.65859e-05, loss_scaling: 137438.96875, num_good_steps: 259, num_bad_steps: 0 [2022-02-05 16:11:55,714] [ INFO] - global step 170500, loss: 3.158756495, lm_loss: 2.953678, sop_loss: 0.205079, speed: 1.12 steps/s, ips: 287.59 seqs/s, learning rate: 6.65657e-05, loss_scaling: 137438.96875, num_good_steps: 359, num_bad_steps: 0 [2022-02-05 16:13:24,869] [ INFO] - global step 170600, loss: 2.860815048, lm_loss: 2.754750, sop_loss: 0.106064, speed: 1.12 steps/s, ips: 287.14 seqs/s, learning rate: 6.65455e-05, loss_scaling: 137438.96875, num_good_steps: 33, num_bad_steps: 0 ``` ### tf版本 https://github.com/ZhuiyiTechnology/roformer ### pytorch版本+tf2.0版本 https://github.com/JunnYu/RoFormer_pytorch ## pytorch使用 ```python import torch from transformers import RoFormerForMaskedLM, BertTokenizer text = "今天[MASK]很好，我[MASK]去公园玩。" tokenizer = BertTokenizer.from_pretrained("junnyu/roformer_base_wwm_cluecorpussmall") pt_model = RoFormerForMaskedLM.from_pretrained("junnyu/roformer_base_wwm_cluecorpussmall") pt_inputs = tokenizer(text, return_tensors="pt") with torch.no_grad(): pt_outputs = pt_model(*pt_inputs).logits[0] pt_outputs_sentence = "pytorch: " for i, id in enumerate(tokenizer.encode(text)): if id == tokenizer.mask_token_id: tokens = tokenizer.convert_ids_to_tokens(pt_outputs[i].topk(k=5)[1]) pt_outputs_sentence += "[" + "\|\|".join(tokens) + "]" else: pt_outputs_sentence += "".join( tokenizer.convert_ids_to_tokens([id], skip_special_tokens=True)) print(pt_outputs_sentence) # pytorch: 今天[天\|\|人\|\|气\|\|阳\|\|雨]很好，我[想\|\|就\|\|要\|\|也\|\|还]去公园玩。 ``` ## 引用 Bibtex： ```tex @misc{su2021roformer, title={RoFormer: Enhanced Transformer with Rotary Position Embedding}, author={Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu}, year={2021}, eprint={2104.09864}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
SetFit/deberta-v3-large__sst2__train-8-8	SetFit	2022-02-10T09:59:57Z	5	0	transformers	[ "transformers", "pytorch", "deberta-v2", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: deberta-v3-large__sst2__train-8-8 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. --> # deberta-v3-large__sst2__train-8-8 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7414 - Accuracy: 0.5623 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6597 \| 1.0 \| 3 \| 0.7716 \| 0.25 \| \| 0.6376 \| 2.0 \| 6 \| 0.7802 \| 0.25 \| \| 0.5857 \| 3.0 \| 9 \| 0.6625 \| 0.75 \| \| 0.4024 \| 4.0 \| 12 \| 0.5195 \| 0.75 \| \| 0.2635 \| 5.0 \| 15 \| 0.4222 \| 1.0 \| \| 0.1714 \| 6.0 \| 18 \| 0.4410 \| 0.5 \| \| 0.1267 \| 7.0 \| 21 \| 0.7773 \| 0.75 \| \| 0.0582 \| 8.0 \| 24 \| 0.9070 \| 0.75 \| \| 0.0374 \| 9.0 \| 27 \| 0.9539 \| 0.75 \| \| 0.0204 \| 10.0 \| 30 \| 1.0507 \| 0.75 \| \| 0.012 \| 11.0 \| 33 \| 1.2802 \| 0.5 \| \| 0.0086 \| 12.0 \| 36 \| 1.4272 \| 0.5 \| \| 0.0049 \| 13.0 \| 39 \| 1.4803 \| 0.5 \| \| 0.0039 \| 14.0 \| 42 \| 1.4912 \| 0.5 \| \| 0.0031 \| 15.0 \| 45 \| 1.5231 \| 0.5 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2 - Tokenizers 0.10.3

Subsets and Splits