license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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gpl-3.0 | ['object-detection', 'yolo', 'autogenerated-modelcard'] | false | Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] | 72ac8c7f9b3cd2993c2b6f054af39c7b |
apache-2.0 | [] | false | distilbert-base-en-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). | 31cf5d5645b32e0ec20b8674ebe04a0b |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). | 7b5ad54d2830d73e94f78dbf983c917a |
apache-2.0 | ['generated_from_trainer'] | false | 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: 1.7375 | 6bae5b23c9ffdc7ec8d2196b47a55b8b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4419 | 1.0 | 557 | 1.7242 | | 1.2397 | 2.0 | 1114 | 1.6714 | | 0.9066 | 3.0 | 1671 | 1.7375 | | 391ec2924f9119c12cb49f4233f54a7c |
apache-2.0 | ['generated_from_trainer'] | false | demo_sentiment_42 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.6332 - F1: 0.7114 | bd1a9aa80a5fd62155216fa1474ec03e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8.62486660723695e-06 - train_batch_size: 64 - eval_batch_size: 64 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 93fbc834e98efd39f0878e54fbc0b591 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7592 | 1.0 | 713 | 0.6509 | 0.6834 | | 0.6389 | 2.0 | 1426 | 0.6318 | 0.7011 | | 0.5647 | 3.0 | 2139 | 0.6320 | 0.7041 | | 0.5391 | 4.0 | 2852 | 0.6332 | 0.7114 | | bb02d5c46fa19d043441662733b15146 |
apache-2.0 | ['generated_from_trainer'] | false | 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: 0.2235 - Accuracy: 0.9265 - F1: 0.9268 | b7c5f2702d9fb0ca3f590dc00ff71405 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8101 | 1.0 | 250 | 0.3177 | 0.9045 | 0.9010 | | 0.2472 | 2.0 | 500 | 0.2235 | 0.9265 | 0.9268 | | 75fe32a4e997f690f6e266a207e8d9c7 |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-asqa-ob This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the [ASQA](https://huggingface.co/datasets/din0s/asqa) dataset. It achieves the following results on the evaluation set: - Loss: 1.7356 - Rougelsum: 12.0879 | 1a54465bebbfc1acdee713e151dac8af |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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: 20 - mixed_precision_training: Native AMP | 7a3a21795dfb57cd3f39a11e877654a3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:---------:| | No log | 1.0 | 355 | 1.8545 | 11.6549 | | 2.4887 | 2.0 | 710 | 1.8050 | 11.7533 | | 1.9581 | 3.0 | 1065 | 1.7843 | 11.8327 | | 1.9581 | 4.0 | 1420 | 1.7722 | 11.9442 | | 1.9252 | 5.0 | 1775 | 1.7648 | 11.9331 | | 1.8853 | 6.0 | 2130 | 1.7567 | 11.9788 | | 1.8853 | 7.0 | 2485 | 1.7519 | 12.0300 | | 1.8512 | 8.0 | 2840 | 1.7483 | 12.0225 | | 1.8328 | 9.0 | 3195 | 1.7451 | 12.0402 | | 1.8115 | 10.0 | 3550 | 1.7436 | 12.0444 | | 1.8115 | 11.0 | 3905 | 1.7419 | 12.0850 | | 1.7878 | 12.0 | 4260 | 1.7408 | 12.1047 | | 1.774 | 13.0 | 4615 | 1.7394 | 12.0839 | | 1.774 | 14.0 | 4970 | 1.7390 | 12.0910 | | 1.7787 | 15.0 | 5325 | 1.7381 | 12.0880 | | 1.7632 | 16.0 | 5680 | 1.7380 | 12.1088 | | 1.7623 | 17.0 | 6035 | 1.7370 | 12.1046 | | 1.7623 | 18.0 | 6390 | 1.7368 | 12.0997 | | 1.7508 | 19.0 | 6745 | 1.7359 | 12.0902 | | 1.7597 | 20.0 | 7100 | 1.7356 | 12.0879 | | db225b5f13557591a6a27d69124f9a92 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Slovak - Robust This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 sk dataset. It achieves the following results on the evaluation set: - Loss: 0.7397 - Wer: 43.6221 | b26aa46fa09a93f9f9438444d7b1cd0e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0232 | 14.29 | 1000 | 0.7425 | 51.8801 | | 0.0083 | 28.57 | 2000 | 0.7698 | 48.4888 | | 0.0006 | 42.86 | 3000 | 0.7640 | 47.5964 | | 0.0005 | 57.14 | 4000 | 0.7649 | 44.8953 | | 0.0002 | 71.43 | 5000 | 0.7440 | 44.3598 | | b0a736e91d76234de5097aebd5e98591 |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_r-wav2vec2_s869 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 246d6757f25ef6f1bede6164f43897a6 |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-tr 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: 0.2841 - Wer: 0.2904 | 40367e843d2e3361bca7a2dbbfba86ea |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 7 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 14 - 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 | 5fce67df2b68683cef80e3de5132730f |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.0805 | 4.03 | 1000 | 3.0333 | 1.0 | | 1.5733 | 8.06 | 2000 | 0.5545 | 0.5080 | | 0.6238 | 12.1 | 3000 | 0.3861 | 0.3977 | | 0.4535 | 16.13 | 4000 | 0.3253 | 0.3408 | | 0.3682 | 20.16 | 5000 | 0.3042 | 0.3177 | | 0.3302 | 24.19 | 6000 | 0.2950 | 0.3015 | | 0.2985 | 28.23 | 7000 | 0.2841 | 0.2904 | | 08133e818b4fb66b8e2913ed6708fcfc |
mit | ['generated_from_keras_callback'] | false | xlmrobertaenepochz This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.1485 - Train End Logits Accuracy: 0.6933 - Train Start Logits Accuracy: 0.6537 - Validation Loss: 0.9772 - Validation End Logits Accuracy: 0.7275 - Validation Start Logits Accuracy: 0.6976 - Epoch: 0 | 0bf9e6e5f7db565497cc2f09527786fd |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5599, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | 3b16bafa8194025db01145ab64229196 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.1485 | 0.6933 | 0.6537 | 0.9772 | 0.7275 | 0.6976 | 0 | | 25f44ff9ec89b50576c0c33abaf5b6d6 |
apache-2.0 | ['generated_from_trainer'] | false | 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: 1.1664 | cd73413c65f6fd4bf23e5644c51597ec |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2096 | 1.0 | 5533 | 1.1505 | | 0.952 | 2.0 | 11066 | 1.1238 | | 0.7347 | 3.0 | 16599 | 1.1664 | | 6900926e56229b8ed8c970f727b5aca8 |
apache-2.0 | ['generated_from_trainer'] | false | MIX2_en-ja_helsinki This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-jap](https://huggingface.co/Helsinki-NLP/opus-mt-en-jap) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6703 | fccc9326eb4742e5badd94659ffb77cb |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 96 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP | e12faebeea9add9259674b78751defde |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 3.5357 | 0.02 | 4000 | 2.9519 | | 2.8601 | 0.04 | 8000 | 2.6962 | | 2.6183 | 0.06 | 12000 | 2.5156 | | 2.4731 | 0.08 | 16000 | 2.4312 | | 2.3731 | 0.1 | 20000 | 2.3575 | | 2.2964 | 0.11 | 24000 | 2.3319 | | 2.238 | 0.13 | 28000 | 2.2802 | | 2.1919 | 0.15 | 32000 | 2.2552 | | 2.1479 | 0.17 | 36000 | 2.2354 | | 2.1104 | 0.19 | 40000 | 2.2210 | | 2.0788 | 0.21 | 44000 | 2.1835 | | 2.0552 | 0.23 | 48000 | 2.1391 | | 2.0228 | 0.25 | 52000 | 2.1338 | | 2.0062 | 0.27 | 56000 | 2.1115 | | 1.9868 | 0.29 | 60000 | 2.1025 | | 1.9628 | 0.31 | 64000 | 2.1334 | | 1.9474 | 0.32 | 68000 | 2.0935 | | 1.9318 | 0.34 | 72000 | 2.1030 | | 1.9187 | 0.36 | 76000 | 2.0605 | | 1.9019 | 0.38 | 80000 | 2.0388 | | 1.8916 | 0.4 | 84000 | 2.0360 | | 1.8775 | 0.42 | 88000 | 2.0356 | | 1.8689 | 0.44 | 92000 | 2.0315 | | 1.8558 | 0.46 | 96000 | 2.0169 | | 1.8431 | 0.48 | 100000 | 2.0213 | | 1.8373 | 0.5 | 104000 | 2.0071 | | 1.8224 | 0.52 | 108000 | 2.0093 | | 1.8181 | 0.53 | 112000 | 1.9952 | | 1.8087 | 0.55 | 116000 | 1.9927 | | 1.7998 | 0.57 | 120000 | 1.9726 | | 1.7947 | 0.59 | 124000 | 1.9817 | | 1.7874 | 0.61 | 128000 | 1.9650 | | 1.7781 | 0.63 | 132000 | 1.9688 | | 1.7712 | 0.65 | 136000 | 1.9655 | | 1.7631 | 0.67 | 140000 | 1.9561 | | 1.7577 | 0.69 | 144000 | 1.9529 | | 1.7528 | 0.71 | 148000 | 1.9447 | | 1.746 | 0.73 | 152000 | 1.9700 | | 1.7386 | 0.74 | 156000 | 1.9413 | | 1.7329 | 0.76 | 160000 | 1.9329 | | 1.7285 | 0.78 | 164000 | 1.9289 | | 1.7227 | 0.8 | 168000 | 1.9337 | | 1.7186 | 0.82 | 172000 | 1.9263 | | 1.7116 | 0.84 | 176000 | 1.9407 | | 1.7072 | 0.86 | 180000 | 1.9059 | | 1.7032 | 0.88 | 184000 | 1.9380 | | 1.6932 | 0.9 | 188000 | 1.9183 | | 1.6921 | 0.92 | 192000 | 1.9131 | | 1.6875 | 0.94 | 196000 | 1.9180 | | 1.6846 | 0.96 | 200000 | 1.9040 | | 1.6797 | 0.97 | 204000 | 1.9089 | | 1.6725 | 0.99 | 208000 | 1.9024 | | 1.6589 | 1.01 | 212000 | 1.8909 | | 1.6507 | 1.03 | 216000 | 1.8837 | | 1.6441 | 1.05 | 220000 | 1.8906 | | 1.6445 | 1.07 | 224000 | 1.8914 | | 1.6394 | 1.09 | 228000 | 1.8833 | | 1.6382 | 1.11 | 232000 | 1.8837 | | 1.6376 | 1.13 | 236000 | 1.8869 | | 1.6329 | 1.15 | 240000 | 1.8829 | | 1.6294 | 1.17 | 244000 | 1.8845 | | 1.6273 | 1.18 | 248000 | 1.8888 | | 1.6243 | 1.2 | 252000 | 1.8709 | | 1.6226 | 1.22 | 256000 | 1.8418 | | 1.6177 | 1.24 | 260000 | 1.8587 | | 1.6151 | 1.26 | 264000 | 1.8526 | | 1.6111 | 1.28 | 268000 | 1.8494 | | 1.6084 | 1.3 | 272000 | 1.8781 | | 1.6043 | 1.32 | 276000 | 1.8390 | | 1.6011 | 1.34 | 280000 | 1.8603 | | 1.5999 | 1.36 | 284000 | 1.8515 | | 1.5954 | 1.38 | 288000 | 1.8356 | | 1.5936 | 1.39 | 292000 | 1.8530 | | 1.5916 | 1.41 | 296000 | 1.8475 | | 1.5886 | 1.43 | 300000 | 1.8410 | | 1.5883 | 1.45 | 304000 | 1.8153 | | 1.5828 | 1.47 | 308000 | 1.8254 | | 1.582 | 1.49 | 312000 | 1.8139 | | 1.578 | 1.51 | 316000 | 1.8366 | | 1.5723 | 1.53 | 320000 | 1.8353 | | 1.5705 | 1.55 | 324000 | 1.8230 | | 1.5691 | 1.57 | 328000 | 1.8194 | | 1.5656 | 1.59 | 332000 | 1.8069 | | 1.566 | 1.6 | 336000 | 1.8204 | | 1.5604 | 1.62 | 340000 | 1.8307 | | 1.5573 | 1.64 | 344000 | 1.8209 | | 1.5547 | 1.66 | 348000 | 1.8320 | | 1.5545 | 1.68 | 352000 | 1.8179 | | 1.5519 | 1.7 | 356000 | 1.8323 | | 1.545 | 1.72 | 360000 | 1.8005 | | 1.5483 | 1.74 | 364000 | 1.8034 | | 1.5454 | 1.76 | 368000 | 1.7997 | | 1.5393 | 1.78 | 372000 | 1.8078 | | 1.5381 | 1.8 | 376000 | 1.8204 | | 1.5347 | 1.81 | 380000 | 1.8071 | | 1.5327 | 1.83 | 384000 | 1.7997 | | 1.529 | 1.85 | 388000 | 1.8012 | | 1.5287 | 1.87 | 392000 | 1.8028 | | 1.5273 | 1.89 | 396000 | 1.8103 | | 1.5194 | 1.91 | 400000 | 1.8008 | | 1.5197 | 1.93 | 404000 | 1.8004 | | 1.5218 | 1.95 | 408000 | 1.8024 | | 1.514 | 1.97 | 412000 | 1.7852 | | 1.5146 | 1.99 | 416000 | 1.7908 | | 1.5045 | 2.01 | 420000 | 1.7864 | | 1.4876 | 2.02 | 424000 | 1.7813 | | 1.4846 | 2.04 | 428000 | 1.7822 | | 1.4865 | 2.06 | 432000 | 1.7737 | | 1.4857 | 2.08 | 436000 | 1.7668 | | 1.4825 | 2.1 | 440000 | 1.7681 | | 1.4828 | 2.12 | 444000 | 1.7685 | | 1.4821 | 2.14 | 448000 | 1.7636 | | 1.4778 | 2.16 | 452000 | 1.7778 | | 1.4803 | 2.18 | 456000 | 1.7834 | | 1.4766 | 2.2 | 460000 | 1.7801 | | 1.4741 | 2.22 | 464000 | 1.7601 | | 1.4705 | 2.23 | 468000 | 1.7665 | | 1.4739 | 2.25 | 472000 | 1.7604 | | 1.4694 | 2.27 | 476000 | 1.7803 | | 1.4665 | 2.29 | 480000 | 1.7835 | | 1.4668 | 2.31 | 484000 | 1.7670 | | 1.4605 | 2.33 | 488000 | 1.7629 | | 1.4626 | 2.35 | 492000 | 1.7612 | | 1.4627 | 2.37 | 496000 | 1.7612 | | 1.4569 | 2.39 | 500000 | 1.7557 | | 1.455 | 2.41 | 504000 | 1.7599 | | 1.4547 | 2.43 | 508000 | 1.7569 | | 1.453 | 2.44 | 512000 | 1.7589 | | 1.4515 | 2.46 | 516000 | 1.7679 | | 1.4501 | 2.48 | 520000 | 1.7574 | | 1.4446 | 2.5 | 524000 | 1.7526 | | 1.4456 | 2.52 | 528000 | 1.7506 | | 1.4445 | 2.54 | 532000 | 1.7484 | | 1.4428 | 2.56 | 536000 | 1.7447 | | 1.439 | 2.58 | 540000 | 1.7468 | | 1.441 | 2.6 | 544000 | 1.7609 | | 1.4358 | 2.62 | 548000 | 1.7498 | | 1.4318 | 2.64 | 552000 | 1.7592 | | 1.4276 | 2.65 | 556000 | 1.7452 | | 1.4317 | 2.67 | 560000 | 1.7500 | | 1.4277 | 2.69 | 564000 | 1.7392 | | 1.4259 | 2.71 | 568000 | 1.7351 | | 1.4239 | 2.73 | 572000 | 1.7385 | | 1.4191 | 2.75 | 576000 | 1.7487 | | 1.4204 | 2.77 | 580000 | 1.7392 | | 1.4176 | 2.79 | 584000 | 1.7372 | | 1.4147 | 2.81 | 588000 | 1.7347 | | 1.4154 | 2.83 | 592000 | 1.7085 | | 1.4134 | 2.85 | 596000 | 1.7103 | | 1.4091 | 2.87 | 600000 | 1.7124 | | 1.4091 | 2.88 | 604000 | 1.7369 | | 1.406 | 2.9 | 608000 | 1.7142 | | 1.4028 | 2.92 | 612000 | 1.7376 | | 1.4019 | 2.94 | 616000 | 1.7201 | | 1.4018 | 2.96 | 620000 | 1.7230 | | 1.3959 | 2.98 | 624000 | 1.7206 | | 1.3985 | 3.0 | 628000 | 1.7183 | | 1.3681 | 3.02 | 632000 | 1.7283 | | 1.3668 | 3.04 | 636000 | 1.7330 | | 1.3687 | 3.06 | 640000 | 1.7187 | | 1.3681 | 3.08 | 644000 | 1.7163 | | 1.3687 | 3.09 | 648000 | 1.7249 | | 1.364 | 3.11 | 652000 | 1.7283 | | 1.364 | 3.13 | 656000 | 1.7091 | | 1.3652 | 3.15 | 660000 | 1.7030 | | 1.3623 | 3.17 | 664000 | 1.7058 | | 1.3604 | 3.19 | 668000 | 1.7101 | | 1.3598 | 3.21 | 672000 | 1.7104 | | 1.3577 | 3.23 | 676000 | 1.7028 | | 1.3574 | 3.25 | 680000 | 1.7023 | | 1.3546 | 3.27 | 684000 | 1.7197 | | 1.3549 | 3.29 | 688000 | 1.7045 | | 1.3534 | 3.3 | 692000 | 1.6990 | | 1.3511 | 3.32 | 696000 | 1.6971 | | 1.3504 | 3.34 | 700000 | 1.6894 | | 1.346 | 3.36 | 704000 | 1.6820 | | 1.3467 | 3.38 | 708000 | 1.6920 | | 1.3461 | 3.4 | 712000 | 1.6897 | | 1.3425 | 3.42 | 716000 | 1.6962 | | 1.34 | 3.44 | 720000 | 1.6864 | | 1.3408 | 3.46 | 724000 | 1.6860 | | 1.3387 | 3.48 | 728000 | 1.6924 | | 1.3377 | 3.5 | 732000 | 1.6919 | | 1.3378 | 3.51 | 736000 | 1.6858 | | 1.334 | 3.53 | 740000 | 1.6816 | | 1.3347 | 3.55 | 744000 | 1.6867 | | 1.3307 | 3.57 | 748000 | 1.6859 | | 1.3316 | 3.59 | 752000 | 1.6896 | | 1.3257 | 3.61 | 756000 | 1.6824 | | 1.3222 | 3.63 | 760000 | 1.6819 | | 1.3247 | 3.65 | 764000 | 1.6809 | | 1.3207 | 3.67 | 768000 | 1.6775 | | 1.3227 | 3.69 | 772000 | 1.6807 | | 1.3203 | 3.71 | 776000 | 1.6750 | | 1.3203 | 3.72 | 780000 | 1.6758 | | 1.316 | 3.74 | 784000 | 1.6787 | | 1.3147 | 3.76 | 788000 | 1.6747 | | 1.3146 | 3.78 | 792000 | 1.6718 | | 1.3137 | 3.8 | 796000 | 1.6744 | | 1.3143 | 3.82 | 800000 | 1.6733 | | 1.3123 | 3.84 | 804000 | 1.6754 | | 1.3069 | 3.86 | 808000 | 1.6734 | | 1.3122 | 3.88 | 812000 | 1.6742 | | 1.3074 | 3.9 | 816000 | 1.6742 | | 1.3006 | 3.92 | 820000 | 1.6709 | | 1.308 | 3.93 | 824000 | 1.6714 | | 1.3063 | 3.95 | 828000 | 1.6727 | | 1.3036 | 3.97 | 832000 | 1.6711 | | 1.3048 | 3.99 | 836000 | 1.6703 | | 4cb68df889d2f01c7b634edfea2753ad |
mit | [] | false | GPT-2 Tokenizer with unmerged digits A fork of the GPT-2 tokenizer, which **removes multi-digit tokens**: ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('cyrilzhang/gpt2-numfix') tokenizer('123.45') | 4700840e3f7e7f6bcf65e6788686d045 |
mit | [] | false | '123 pigeon' ``` - This is for my investigations into the arithmetic capabilities of large language models. There is no model here, only a tokenizer. - [PaLM](https://arxiv.org/abs/2204.02311) does this. I think it's very reasonable. - Many models (illustriously, [GPT-3](https://arxiv.org/abs/2005.14165)) don't do this, because they use the GPT-2 tokenizer. | 1378133885e19e3a4657d278913230ec |
mit | ['generated_from_trainer'] | false | xlnet-base-cased_fold_3_binary_v1 This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8649 - F1: 0.8044 | 9287d8db261afc18bb387cc457e1b62a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.4483 | 0.8000 | | 0.4228 | 2.0 | 578 | 0.4264 | 0.8040 | | 0.4228 | 3.0 | 867 | 0.5341 | 0.8056 | | 0.2409 | 4.0 | 1156 | 0.9077 | 0.8103 | | 0.2409 | 5.0 | 1445 | 1.1069 | 0.7889 | | 0.1386 | 6.0 | 1734 | 1.0288 | 0.8093 | | 0.0817 | 7.0 | 2023 | 1.2477 | 0.8049 | | 0.0817 | 8.0 | 2312 | 1.5915 | 0.7872 | | 0.0465 | 9.0 | 2601 | 1.5323 | 0.8035 | | 0.0465 | 10.0 | 2890 | 1.4351 | 0.7989 | | 0.0376 | 11.0 | 3179 | 1.4639 | 0.7916 | | 0.0376 | 12.0 | 3468 | 1.6027 | 0.7956 | | 0.0234 | 13.0 | 3757 | 1.7860 | 0.7931 | | 0.0109 | 14.0 | 4046 | 1.8567 | 0.7934 | | 0.0109 | 15.0 | 4335 | 1.8294 | 0.8053 | | 0.0115 | 16.0 | 4624 | 1.7799 | 0.7971 | | 0.0115 | 17.0 | 4913 | 1.5935 | 0.8000 | | 0.0142 | 18.0 | 5202 | 1.8136 | 0.8066 | | 0.0142 | 19.0 | 5491 | 1.7718 | 0.8063 | | 0.0124 | 20.0 | 5780 | 1.8581 | 0.8053 | | 0.0083 | 21.0 | 6069 | 1.8523 | 0.8056 | | 0.0083 | 22.0 | 6358 | 1.8408 | 0.8035 | | 0.0045 | 23.0 | 6647 | 1.8347 | 0.8040 | | 0.0045 | 24.0 | 6936 | 1.8683 | 0.8067 | | 0.0005 | 25.0 | 7225 | 1.8649 | 0.8044 | | 5653c73a95561d38d3a6212c4d4251e8 |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio', 'vocoder'] | false | Details ``` batch_size: 64 discriminator_params: follow_official_norm: true period_discriminator_params: bias: true channels: 32 downsample_scales: - 3 - 3 - 3 - 3 - 1 in_channels: 1 kernel_sizes: - 5 - 3 max_downsample_channels: 1024 nonlinear_activation: LeakyReLU nonlinear_activation_params: negative_slope: 0.1 out_channels: 1 use_spectral_norm: false use_weight_norm: true periods: - 2 - 3 - 5 - 7 - 11 ``` | a4eb986c57022e6274af8c54cac659e5 |
apache-2.0 | ['translation'] | false | opus-mt-nso-es * source languages: nso * target languages: es * OPUS readme: [nso-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nso-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/nso-es/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-es/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-es/opus-2020-01-16.eval.txt) | 5ea646ad667df0b2294590ef0bc2d161 |
apache-2.0 | ['generated_from_trainer'] | false | binary-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3009 - Accuracy: 0.8968 | c96d9fe01cf434078413070be83fae76 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.175 | 1.0 | 4210 | 0.3009 | 0.8968 | | 7e4359e3791dae2d066b975125bc943d |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT'] | false | <p align="center"> <img src="https://github.com/iPieter/RobBERT/raw/master/res/robbert_2022_logo_with_name.png" alt="RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use" width="75%"> </p> | d4a8e7694f8cffb1f0570d033846269d |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT'] | false | RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use. RobBERT-2022 is the latest release of the [Dutch RobBERT model](https://pieter.ai/robbert/). It further pretrained the original [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) model on the 2022 version of the OSCAR version. Thanks to this more recent dataset, this [DTAI-KULeuven/robbert-2022-dutch-base](https://huggingface.co/DTAI-KULeuven/robbert-2022-dutch-base) model shows increased performance on several tasks related to recent events, e.g. COVID-19-related tasks. We also found that for some tasks that do not contain more recent information than 2019, the original [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) RobBERT model can still outperform this newer one. The original RobBERT model was released in January 2020. Dutch has evolved a lot since then, for example the COVID-19 pandemic introduced a wide range of new words that were suddenly used daily. Also, many other world facts that the original model considered true have also changed. To account for this and other changes in usage, we release a new Dutch BERT model trained on data from 2022: RobBERT 2022. More in-depth information about RobBERT-2022 can be found in our [blog post](https://pieter.ai/robbert-2022/), [our paper](http://arxiv.org/abs/2211.08192), [the original RobBERT paper](https://arxiv.org/abs/2001.06286) and [the RobBERT Github repository](https://github.com/iPieter/RobBERT). | b6aaea0555665b85f21756ce0c8c5ceb |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT'] | false | How to use RobBERT-2022 and RobBERT both use the [RoBERTa](https://arxiv.org/abs/1907.11692) architecture and pre-training but with a Dutch tokenizer and training data. RoBERTa is the robustly optimized English BERT model, making it even more powerful than the original BERT model. Given this same architecture, RobBERT can easily be finetuned and inferenced using [code to finetune RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html) models and most code used for BERT models, e.g. as provided by [HuggingFace Transformers](https://huggingface.co/transformers/) library. By default, RobBERT-2022 has the masked language model head used in training. This can be used as a zero-shot way to fill masks in sentences. It can be tested out for free on [RobBERT's Hosted infererence API of Huggingface](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=De+hoofdstad+van+Belgi%C3%AB+is+%3Cmask%3E.). You can also create a new prediction head for your own task by using any of HuggingFace's [RoBERTa-runners](https://huggingface.co/transformers/v2.7.0/examples.html | ec2cc161c759897e95fb1f0c34439141 |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT'] | false | language-model-training), [their fine-tuning notebooks](https://huggingface.co/transformers/v4.1.1/notebooks.html) by changing the model name to `DTAI-KULeuven/robbert-2022-dutch-base`. ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DTAI-KULeuven/robbert-2022-dutch-base") model = AutoModelForSequenceClassification.from_pretrained("DTAI-KULeuven/robbert-2022-dutch-base") ``` You can then use most of [HuggingFace's BERT-based notebooks](https://huggingface.co/transformers/v4.1.1/notebooks.html) for finetuning RobBERT-2022 on your type of Dutch language dataset. | 462ca645e65fad856d2f8387e132dbd8 |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT'] | false | Comparison of Available Dutch BERT models There is a wide variety of Dutch BERT-based models available for fine-tuning on your tasks. Here's a quick summary to find the one that suits your need: - [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base): The RobBERT model has for years been the best performing BERT-like model for most language tasks. It is trained on a large Dutch webcrawled dataset (OSCAR) and uses the superior [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta) architecture, which robustly optimized the original [BERT model](https://huggingface.co/docs/transformers/model_doc/bert). - [DTAI-KULeuven/robbertje-1-gb-merged](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-mergedRobBERTje): The RobBERTje model is a distilled version of RobBERT and about half the size and four times faster to perform inference on. This can help deploy more scalable language models for your language task - [DTAI-KULeuven/robbert-2022-dutch-base](https://huggingface.co/DTAI-KULeuven/robbert-2022-dutch-base): The RobBERT-2022 is a further pre-trained RobBERT model on the OSCAR2022 dataset. It is helpful for tasks that rely on words and/or information about more recent events. There's also the [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) "BERTje" model. This model uses the outdated basic BERT model, and is trained on a smaller corpus of clean Dutch texts. Thanks to RobBERT's more recent architecture as well as its larger and more real-world-like training corpus, most researchers and practitioners seem to achieve higher performance on their language tasks with the RobBERT model. | 0f1fb186faee58c8ef83bc23b4749117 |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT'] | false | Our Performance Evaluation Results All experiments are described in more detail in our [paper](https://arxiv.org/abs/2001.06286), with the code in [our GitHub repository](https://github.com/iPieter/RobBERT). | f2b2940a77ec745446925857ca7757ba |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT'] | false | Sentiment analysis Predicting whether a review is positive or negative using the [Dutch Book Reviews Dataset](https://github.com/benjaminvdb/110kDBRD). | Model | Accuracy [%] | |-------------------|--------------------------| | ULMFiT | 93.8 | | BERTje | 93.0 | | RobBERT v2 | 94.4 | | RobBERT 2022 | **95.1** | | 91a5e78b072f0aa86489d538811eb0a1 |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT'] | false | Die/Dat (coreference resolution) We measured how well the models are able to do coreference resolution by predicting whether "die" or "dat" should be filled into a sentence. For this, we used the [EuroParl corpus](https://www.statmt.org/europarl/). | 2a5ec4031fe4ff235ca9e162fed2b82d |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT'] | false | Finetuning on whole dataset | Model | Accuracy [%] | F1 [%] | |-------------------|--------------------------|--------------| | [Baseline](https://arxiv.org/abs/2001.02943) (LSTM) | | 75.03 | | mBERT | 98.285 | 98.033 | | BERTje | 98.268 | 98.014 | | RobBERT v2 | **99.232** | **99.121** | | RobBERT 2022 | 97.8 | | | d33c908e72a0c9a54347600267d49638 |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT'] | false | Finetuning on 10K examples We also measured the performance using only 10K training examples. This experiment clearly illustrates that RobBERT outperforms other models when there is little data available. | Model | Accuracy [%] | F1 [%] | |-------------------|--------------------------|--------------| | mBERT | 92.157 | 90.898 | | BERTje | 93.096 | 91.279 | | RobBERT v2 | **97.816** | **97.514** | | 0bfe4ef2b446e78fe3c569ec91c3ae00 |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT'] | false | Using zero-shot word masking task Since BERT models are pre-trained using the word masking task, we can use this to predict whether "die" or "dat" is more likely. This experiment shows that RobBERT has internalised more information about Dutch than other models. | Model | Accuracy [%] | |-------------------|--------------------------| | ZeroR | 66.70 | | mBERT | 90.21 | | BERTje | 94.94 | | RobBERT v2 | **98.75** | | 540f054ed67c017bf37230612f730b52 |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT'] | false | Part-of-Speech Tagging. Using the [Lassy UD dataset](https://universaldependencies.org/treebanks/nl_lassysmall/index.html). | Model | Accuracy [%] | |-------------------|--------------------------| | Frog | 91.7 | | mBERT | **96.5** | | BERTje | 96.3 | | RobBERT v2 | 96.4 | | RobBERT 2022 | 96.1 | | b3faa7eaead3946850f69bb4a9bb471f |
mit | ['Dutch', 'Flemish', 'RoBERTa', 'RobBERT'] | false | Credits and citation This project is created by [Pieter Delobelle](https://people.cs.kuleuven.be/~pieter.delobelle), [Thomas Winters](https://thomaswinters.be) and [Bettina Berendt](https://people.cs.kuleuven.be/~bettina.berendt/). If you would like to cite our paper or model, you can use the following BibTeX: ``` @inproceedings{delobelle2022robbert2022, doi = {10.48550/ARXIV.2211.08192}, url = {https://arxiv.org/abs/2211.08192}, author = {Delobelle, Pieter and Winters, Thomas and Berendt, Bettina}, keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use}, venue = {arXiv}, year = {2022}, } @inproceedings{delobelle2020robbert, title = "{R}ob{BERT}: a {D}utch {R}o{BERT}a-based {L}anguage {M}odel", author = "Delobelle, Pieter and Winters, Thomas and Berendt, Bettina", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.findings-emnlp.292", doi = "10.18653/v1/2020.findings-emnlp.292", pages = "3255--3265" } ``` | 45d69f53c56687e5aa121af53acf8410 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | nagisa Dreambooth model trained by birdaz with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept: | 15988e9c6bd251d373da52e69d528ac1 |
cc-by-sa-4.0 | ['japanese', 'question-answering', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [deberta-large-japanese-aozora](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-aozora) and [UD_Japanese-GSDLUW](https://github.com/UniversalDependencies/UD_Japanese-GSDLUW). Use [MASK] inside `context` to avoid ambiguity when specifying a multiple-used word as `question`. | f922f21ad8d0fd41d07fe105666bba29 |
cc-by-sa-4.0 | ['japanese', 'question-answering', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForQuestionAnswering,QuestionAnsweringPipeline tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-large-japanese-aozora-ud-head") model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/deberta-large-japanese-aozora-ud-head") qap=QuestionAnsweringPipeline(tokenizer=tokenizer,model=model,align_to_words=False) print(qap(question="国語",context="全学年にわたって小学校の国語の教科書に挿し絵>が用いられている")) ``` or (with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/)) ```py class TransformersUD(object): def __init__(self,bert): import os from transformers import (AutoTokenizer,AutoModelForQuestionAnswering, AutoModelForTokenClassification,AutoConfig,TokenClassificationPipeline) self.tokenizer=AutoTokenizer.from_pretrained(bert) self.model=AutoModelForQuestionAnswering.from_pretrained(bert) x=AutoModelForTokenClassification.from_pretrained if os.path.isdir(bert): d,t=x(os.path.join(bert,"deprel")),x(os.path.join(bert,"tagger")) else: from transformers.utils import cached_file c=AutoConfig.from_pretrained(cached_file(bert,"deprel/config.json")) d=x(cached_file(bert,"deprel/pytorch_model.bin"),config=c) s=AutoConfig.from_pretrained(cached_file(bert,"tagger/config.json")) t=x(cached_file(bert,"tagger/pytorch_model.bin"),config=s) self.deprel=TokenClassificationPipeline(model=d,tokenizer=self.tokenizer, aggregation_strategy="simple") self.tagger=TokenClassificationPipeline(model=t,tokenizer=self.tokenizer) def __call__(self,text): import numpy,torch,ufal.chu_liu_edmonds w=[(t["start"],t["end"],t["entity_group"]) for t in self.deprel(text)] z,n={t["start"]:t["entity"].split("|") for t in self.tagger(text)},len(w) r,m=[text[s:e] for s,e,p in w],numpy.full((n+1,n+1),numpy.nan) v,c=self.tokenizer(r,add_special_tokens=False)["input_ids"],[] for i,t in enumerate(v): q=[self.tokenizer.cls_token_id]+t+[self.tokenizer.sep_token_id] c.append([q]+v[0:i]+[[self.tokenizer.mask_token_id]]+v[i+1:]+[[q[-1]]]) b=[[len(sum(x[0:j+1],[])) for j in range(len(x))] for x in c] with torch.no_grad(): d=self.model(input_ids=torch.tensor([sum(x,[]) for x in c]), token_type_ids=torch.tensor([[0]*x[0]+[1]*(x[-1]-x[0]) for x in b])) s,e=d.start_logits.tolist(),d.end_logits.tolist() for i in range(n): for j in range(n): m[i+1,0 if i==j else j+1]=s[i][b[i][j]]+e[i][b[i][j+1]-1] h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] if [0 for i in h if i==0]!=[0]: i=([p for s,e,p in w]+["root"]).index("root") j=i+1 if i<n else numpy.nanargmax(m[:,0]) m[0:j,0]=m[j+1:,0]=numpy.nan h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] u=" | ffc100d564434a585cce6fec07141b38 |
cc-by-sa-4.0 | ['japanese', 'question-answering', 'dependency-parsing'] | false | text = "+text.replace("\n"," ")+"\n" for i,(s,e,p) in enumerate(w,1): p="root" if h[i]==0 else "dep" if p=="root" else p u+="\t".join([str(i),r[i-1],"_",z[s][0][2:],"_","|".join(z[s][1:]), str(h[i]),p,"_","_" if i<n and e<w[i][0] else "SpaceAfter=No"])+"\n" return u+"\n" nlp=TransformersUD("KoichiYasuoka/deberta-large-japanese-aozora-ud-head") print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている")) ``` | c5d28f2f8586e7d3c79f5c6050bcba4a |
apache-2.0 | ['translation'] | false | opus-mt-fi-yap * source languages: fi * target languages: yap * OPUS readme: [fi-yap](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-yap/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-yap/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-yap/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-yap/opus-2020-01-08.eval.txt) | 75e5bb2454046d67221c2439e35f2cf0 |
apache-2.0 | ['generated_from_trainer'] | false | Graphcore/roberta-base-squad2 Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore). Through HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project. | c8f8630c76c51f3be3b083b696a2c56d |
apache-2.0 | ['generated_from_trainer'] | false | Model description RoBERTa is based on BERT pretraining approach and improves on it by carefully evaluating a number of design decisions of BERT pretraining which it found to cause the model to be undertrained. It suggested a way to improve the performance by training the model longer, with bigger batches over more data, removing the next sentence prediction objectives, training on longer sequences and dynamically changing the mask pattern applied to the training data. As a result, it achieved state-of-the-art results on GLUE, RACE and SQuAD. Paper link : [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/pdf/1907.11692.pdf) | 6a2b5e322bb61f5a6e81a147d19ab08d |
apache-2.0 | ['generated_from_trainer'] | false | Training procedure Trained on 16 Graphcore Mk2 IPUs using [optimum-graphcore](https://github.com/huggingface/optimum-graphcore). Command line: ``` python examples/question-answering/run_qa.py \ --ipu_config_name Graphcore/roberta-base-ipu \ --model_name_or_path roberta-base \ --dataset_name squad_v2 \ --version_2_with_negative \ --do_train \ --do_eval \ --num_train_epochs 3 \ --per_device_train_batch_size 4 \ --per_device_eval_batch_size 2 \ --pod_type pod16 \ --learning_rate 7e-5 \ --max_seq_length 384 \ --doc_stride 128 \ --seed 1984 \ --lr_scheduler_type linear \ --loss_scaling 64 \ --weight_decay 0.01 \ --warmup_ratio 0.2 \ --logging_steps 1 \ --save_steps -1 \ --dataloader_num_workers 64 \ --output_dir roberta-base-squad2 \ --overwrite_output_dir \ --push_to_hub ``` | b67e0e0c82a7a8e9b1c1d18c98d8242e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 1984 - distributed_type: IPU - total_train_batch_size: 256 - total_eval_batch_size: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 3.0 - training precision: Mixed Precision | 890ea70d814b6b7658f8568502ba7a7b |
apache-2.0 | ['generated_from_trainer'] | false | Training results ``` ***** train metrics ***** epoch = 3.0 train_loss = 0.9982 train_runtime = 0:04:44.21 train_samples = 131823 train_samples_per_second = 1391.43 train_steps_per_second = 5.425 ***** eval metrics ***** epoch = 3.0 eval_HasAns_exact = 78.1208 eval_HasAns_f1 = 84.6569 eval_HasAns_total = 5928 eval_NoAns_exact = 82.0353 eval_NoAns_f1 = 82.0353 eval_NoAns_total = 5945 eval_best_exact = 80.0809 eval_best_exact_thresh = 0.0 eval_best_f1 = 83.3442 eval_best_f1_thresh = 0.0 eval_exact = 80.0809 eval_f1 = 83.3442 eval_samples = 12165 eval_total = 11873 ``` | 9b2e6a4b0aa02afc0d193e192c96128f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Punjabi Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Punjabi using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | 52c1f1cb57130eb00b4d2f583c09ccbf |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "pa-IN", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("danurahul/wav2vec2-large-xlsr-pa-IN") model = Wav2Vec2ForCTC.from_pretrained("danurahul/wav2vec2-large-xlsr-pa-IN") resampler = torchaudio.transforms.Resample(48_000, 16_000) | d3346c0e56af61f15747f00451e569ad |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Punjabi test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "pa-IN", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("danurahul/wav2vec2-large-xlsr-pa-IN") model = Wav2Vec2ForCTC.from_pretrained("danurahul/wav2vec2-large-xlsr-pa-IN") model.to("cuda") chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\?\\\\\\\\\\\\\\\\.\\\\\\\\\\\\\\\\!\\\\\\\\\\\\\\\\-\\\\\\\\\\\\\\\\;\\\\\\\\\\\\\\\\:\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\“\\\\\\\\\\\\\\\\%\\\\\\\\\\\\\\\\‘\\\\\\\\\\\\\\\\”\\\\\\\\\\\\\\\\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) | 7146fdb2915afd8a80eb12ac669e8f12 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 100 % | 26aaec9b971fb0e81912406142380404 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the bird concept trained by Someman on the Someman/danphe dataset. This is a Stable Diffusion model fine-tuned on the bird concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of bird danphe** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! | 30a3311d3cdea8e8d117f4cf99a33cbf |
apache-2.0 | ['tapas', 'TapasModel'] | false | TAPAS small model This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_inter_masklm_small_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training. It uses relative position embeddings by default (i.e. resetting the position index at every cell of the table). The other (non-default) version which can be used is the one with absolute position embeddings: - `revision="no_reset"`, which corresponds to `tapas_inter_masklm_small` Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by the Hugging Face team and contributors. | 199dcb498f0991d8cf382cfb501fbed9 |
openrail | [] | false | This model is an outcome of an experiment of training from scratch https://huggingface.co/facebook/opt-1.3b for just 8B tokens in fp16, fp32 and bf16 which would allow comparing the resulting models when they are used to train a multimodal model. But, of course, it can be used for any other purpose, just be aware that these models are very undertrained. Most language models are trained for about 300B tokens, this one was just 8B. The 3 repositories are: - https://huggingface.co/HuggingFaceM4/opt-1.3b-fp16-8b-samples - https://huggingface.co/HuggingFaceM4/opt-1.3b-fp32-8b-samples - https://huggingface.co/HuggingFaceM4/opt-1.3b-bf16-8b-samples | 1831873fc57d8208a59f06a75e2ce3aa |
openrail | [] | false | The training get transformers: ``` git clone https://github.com/huggingface/transformers cd transformers ``` Prepare an initialized opt-1.3 model: ``` cat << EOT > prep-bf16.py from transformers import AutoConfig, AutoModel, AutoTokenizer import torch mname = "facebook/opt-1.3b" config = AutoConfig.from_pretrained(mname) model = AutoModel.from_config(config, torch_dtype=torch.float16) tokenizer = AutoTokenizer.from_pretrained(mname) path = "opt-1.3b-bf16" model.save_pretrained(path) tokenizer.save_pretrained(path) EOT ``` Run: ``` python prep-bf16.py ``` Train from scratch on a single 8x 80GB A100 node on `realnewslike` subset of https://huggingface.co/datasets/c4: ``` git clone https://github.com/huggingface/transformers cd transformers PYTHONPATH="src" python -m torch.distributed.run \ --nproc_per_node=8 \ --nnode=1 \ --node_rank=0 \ --master_addr=127.0.0.1 \ --master_port=9901 \ examples/pytorch/language-modeling/run_clm.py \ --bf16 \ --tf32 1 \ --seed 42 \ --dataset_name c4 \ --dataset_config_name realnewslike \ --model_name_or_path opt-1.3b-bf16 \ --per_device_train_batch_size 6 \ --per_device_eval_batch_size 6 \ --gradient_accumulation_steps 2 \ --do_train \ --logging_steps 5 \ --save_steps 1000 \ --eval_steps 1000 \ --weight_decay 0.1 \ --num_train_epochs 1 \ --adam_beta1 0.9 \ --adam_beta2 0.95 \ --learning_rate 0.0002 \ --lr_scheduler_type linear \ --warmup_steps 1000 \ --report_to tensorboard \ --output_dir saved \ --logging_dir tb \ --log_level warning \ --preprocessing_num_workers 32 ``` The training took about 40h. | e0402fcf7ecdd86f065b7f4176b49dde |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-common-voice-fa-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: 3.0558 - Wer: 1.0 | 3c1fcf7477d44a31e554aa9011947172 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 - mixed_precision_training: Native AMP | 10b9db8326a102fa712d7193a6e610ba |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 5.1626 | 0.3 | 100 | 4.0692 | 1.0 | | 5.1776 | 0.6 | 200 | 3.6640 | 1.0 | | 3.6628 | 0.9 | 300 | 3.3832 | 1.0 | | 3.2022 | 1.2 | 400 | 3.3492 | 1.0 | | 3.1714 | 1.5 | 500 | 3.3215 | 1.0 | | 3.0689 | 1.8 | 600 | 3.0806 | 1.0 | | 3.1478 | 2.1 | 700 | 3.0624 | 1.0 | | 3.1818 | 2.4 | 800 | 3.0777 | 1.0 | | 3.159 | 2.7 | 900 | 3.0558 | 1.0 | | 29836f3e24462686c02433f83eba46ad |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_vp-100k_age_teens-10_sixties-0_s362 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | dbd331a6b8877a80f6f8649a8c213f1f |
apache-2.0 | ['translation'] | false | war-eng * source group: Waray (Philippines) * target group: English * OPUS readme: [war-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/war-eng/README.md) * model: transformer-align * source language(s): war * target language(s): eng * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/war-eng/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/war-eng/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/war-eng/opus-2020-06-16.eval.txt) | 773a40d35c63424935f81d117d3f9f48 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: war-eng - source_languages: war - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/war-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['war', 'en'] - src_constituents: {'war'} - tgt_constituents: {'eng'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/war-eng/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/war-eng/opus-2020-06-16.test.txt - src_alpha3: war - tgt_alpha3: eng - short_pair: war-en - chrF2_score: 0.308 - bleu: 12.3 - brevity_penalty: 1.0 - ref_len: 11345.0 - src_name: Waray (Philippines) - tgt_name: English - train_date: 2020-06-16 - src_alpha2: war - tgt_alpha2: en - prefer_old: False - long_pair: war-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 16d586a073cc44dfc7ede0f5f735eafc |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 1000 | dec928cd7ab7d96303a29f968591f188 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | finetuned on dark, moody, "victorian" imagery (ノ◕ヮ◕)ノ*:・゚✧ [<img src="https://colab.research.google.com/assets/colab-badge.svg">](https://colab.research.google.com/drive/13E3i6_Z1BWd3e6f71-TNd5bk8eGqaeZf?usp=sharing)  v1 was trained on SD 1.4, v2 on SD 1.5. check the pdf for examples with different prompts & settings. comparisons.zip has steps vs cfg scale x/y plots for euler_a and lms. use the tokens "darkvictorian artstyle" in your prompt to use the style. | ad99b5f68d413d8313c83bbc8c2dc75b |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | random samples:  <a href='https://ko-fi.com/S6S6FUYKY' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi3.png?v=3' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> | 467f6cbd564f8d699638ebcb86cce12a |
mit | ['roberta', 'cloze', 'distractor', 'generation'] | false | Model description This model is a Candidate Set Generator in **"CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model", Findings of EMNLP 2022**. Its input are stem and answer, and output is candidate set of distractors. It is fine-tuned by [**CLOTH**](https://www.cs.cmu.edu/~glai1/data/cloth/) dataset based on [**roberta-base**](https://huggingface.co/roberta-base) model. For more details, you can see our **paper** or [**GitHub**](https://github.com/AndyChiangSH/CDGP). | 6001628d9c18fd5b6b834f47e91169d7 |
mit | ['roberta', 'cloze', 'distractor', 'generation'] | false | How to use? 1. Download the model by hugging face transformers. ```python from transformers import RobertaTokenizer, RobertaForMaskedLM, pipeline tokenizer = RobertaTokenizer.from_pretrained("AndyChiang/cdgp-csg-roberta-cloth") csg_model = RobertaForMaskedLM.from_pretrained("AndyChiang/cdgp-csg-roberta-cloth") ``` 2. Create a unmasker. ```python unmasker = pipeline("fill-mask", tokenizer=tokenizer, model=csg_model, top_k=10) ``` 3. Use the unmasker to generate the candidate set of distractors. ```python sent = "I feel <mask> now. </s> happy" cs = unmasker(sent) print(cs) ``` | 5e0093f5f6ca51405546ce4924453f6f |
mit | ['roberta', 'cloze', 'distractor', 'generation'] | false | Dataset This model is fine-tuned by [CLOTH](https://www.cs.cmu.edu/~glai1/data/cloth/) dataset, which is a collection of nearly 100,000 cloze questions from middle school and high school English exams. The detail of CLOTH dataset is shown below. | Number of questions | Train | Valid | Test | | ------------------- | ----- | ----- | ----- | | Middle school | 22056 | 3273 | 3198 | | High school | 54794 | 7794 | 8318 | | Total | 76850 | 11067 | 11516 | You can also use the [dataset](https://huggingface.co/datasets/AndyChiang/cloth) we have already cleaned. | 27447a4689e8ab894ca43daad10bba00 |
mit | ['roberta', 'cloze', 'distractor', 'generation'] | false | Training hyperparameters The following hyperparameters were used during training: - Pre-train language model: [roberta-base](https://huggingface.co/roberta-base) - Optimizer: adam - Learning rate: 0.0001 - Max length of input: 64 - Batch size: 64 - Epoch: 1 - Device: NVIDIA® Tesla T4 in Google Colab | 81a6636bb1e592e24085e0fa74540450 |
mit | ['roberta', 'cloze', 'distractor', 'generation'] | false | Testing The evaluations of this model as a Candidate Set Generator in CDGP is as follows: | P@1 | F1@3 | F1@10 | MRR | NDCG@10 | | ----- | ---- | ----- | ----- | ------- | | 10.50 | 9.83 | 10.25 | 20.42 | 28.17 | | 8fa5b126bd1d9fea83fa94403abd431a |
mit | ['roberta', 'cloze', 'distractor', 'generation'] | false | Candidate Set Generator | Models | CLOTH | DGen | | ----------- | ----------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- | | **BERT** | [cdgp-csg-bert-cloth](https://huggingface.co/AndyChiang/cdgp-csg-bert-cloth) | [cdgp-csg-bert-dgen](https://huggingface.co/AndyChiang/cdgp-csg-bert-dgen) | | **SciBERT** | [cdgp-csg-scibert-cloth](https://huggingface.co/AndyChiang/cdgp-csg-scibert-cloth) | [cdgp-csg-scibert-dgen](https://huggingface.co/AndyChiang/cdgp-csg-scibert-dgen) | | **RoBERTa** | [*cdgp-csg-roberta-cloth*](https://huggingface.co/AndyChiang/cdgp-csg-roberta-cloth) | [cdgp-csg-roberta-dgen](https://huggingface.co/AndyChiang/cdgp-csg-roberta-dgen) | | **BART** | [cdgp-csg-bart-cloth](https://huggingface.co/AndyChiang/cdgp-csg-bart-cloth) | [cdgp-csg-bart-dgen](https://huggingface.co/AndyChiang/cdgp-csg-bart-dgen) | | 68a2bb5e927a635b2d5f6d522ef2d4bb |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7501 - Matthews Correlation: 0.5309 | da3df4e12933496fe7225ebe0791d313 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5286 | 1.0 | 535 | 0.5067 | 0.4301 | | 0.3469 | 2.0 | 1070 | 0.5216 | 0.4802 | | 0.2343 | 3.0 | 1605 | 0.6431 | 0.5002 | | 0.1753 | 4.0 | 2140 | 0.7501 | 0.5309 | | 0.1251 | 5.0 | 2675 | 0.8695 | 0.5222 | | 31ed2e1976ffe40223172082e046730d |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-work-7-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3586 - Accuracy: 0.3689 | a7936eb2e12ec066f4a1dff5433eb54b |
apache-2.0 | ['translation'] | false | opus-mt-fr-tll * source languages: fr * target languages: tll * OPUS readme: [fr-tll](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-tll/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-tll/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-tll/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-tll/opus-2020-01-16.eval.txt) | 2d867905307360bc7943fa156f999bd8 |
apache-2.0 | ['generated_from_trainer'] | false | Article_100v5_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article100v5_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.5958 - Precision: 0.0241 - Recall: 0.0005 - F1: 0.0010 - Accuracy: 0.7822 | 9f567795eee5a1b619dcfa4cfe93edf0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 13 | 0.7298 | 0.0 | 0.0 | 0.0 | 0.7816 | | No log | 2.0 | 26 | 0.6272 | 0.0 | 0.0 | 0.0 | 0.7816 | | No log | 3.0 | 39 | 0.5958 | 0.0241 | 0.0005 | 0.0010 | 0.7822 | | b82e36ccd6fa330402a90342fd3511fc |
cc-by-sa-4.0 | ['japanese', 'pos', 'dependency-parsing'] | false | Model Description This is a RoBERTa model pretrained on 青空文庫 texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [roberta-large-japanese-aozora](https://huggingface.co/KoichiYasuoka/roberta-large-japanese-aozora) and [UD_Japanese-GSDLUW](https://github.com/UniversalDependencies/UD_Japanese-GSDLUW). | 416efba49eeacbf5adbf31df8ba6eba3 |
cc-by-sa-4.0 | ['japanese', 'pos', 'dependency-parsing'] | false | text = "+text+"\n" v=[(s,e) for s,e in w["offset_mapping"] if s<e] for i,(s,e) in enumerate(v,1): q=self.model.config.id2label[p[i,h[i]]].split("|") u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n" return u+"\n" nlp=UDgoeswith("KoichiYasuoka/roberta-large-japanese-aozora-ud-goeswith") print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている")) ``` with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/). Or without ufal.chu-liu-edmonds: ``` from transformers import pipeline nlp=pipeline("universal-dependencies","KoichiYasuoka/roberta-large-japanese-aozora-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple") print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている")) ``` | b038e7972fc1ce5bcb4ca506e4eeec1f |
mit | ['generated_from_trainer'] | false | deberta-v3-xsmall-indonesia-squadv2 This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4182 | 178b0c4705c535bfd6f1bfd34a508aff |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 9ba705a91374d04f47861d076950352e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.6078 | 1.0 | 13505 | 1.5331 | | 1.4216 | 2.0 | 27010 | 1.4344 | | 1.2017 | 3.0 | 40515 | 1.4182 | | 340dcf3efeba8b698fd75f2b60fcdc22 |
mit | ['generated_from_trainer'] | false | Evaluation Results ``` {'exact': 55.34646711872568, 'f1': 67.22757187614371, 'total': 24923, 'HasAns_exact': 55.34646711872568, 'HasAns_f1': 67.22757187614371, 'HasAns_total': 24923, 'best_exact': 55.34646711872568, 'best_exact_thresh': 0.0, 'best_f1': 67.22757187614371, 'best_f1_thresh': 0.0} ``` | 81567a228c8c0d7a0e2ad8a2df10ab7c |
mit | ['generated_from_trainer'] | false | Simple Usage ``` from transformers import pipeline qa_pipeline = pipeline( "question-answering", model="asaduas/deberta-v3-xsmall-indonesia-squadv2", tokenizer="asaduas/deberta-v3-xsmall-indonesia-squadv2" ) qa_pipeline( { 'context': "Pada tahun 1512 juga Afonso de Albuquerque mengirim Antonio Albreu dan Franscisco Serrao untuk memimpin armadanya mencari jalan ke tempat asal rempah-rempah di Maluku. Sepanjang perjalanan, mereka singgah di Madura, Bali, dan Lombok. Dengan menggunakan nakhoda-nakhoda Jawa, armada itu tiba di Kepulauan Banda, terus menuju Aibku Utara sampai tiba di Ternate.", 'question': "Siapa yang dikirim oleh Afonso de Albuquerque Pada tahun 1512?" } ) ``` | dadd6be11721ee7fbffb0ccf771252c3 |
apache-2.0 | ['generated_from_trainer'] | false | t5_large_epoch_1_comve_triple This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.5605 | 8e68a51fb6333f927aa4c0f135ae821a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 48 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 | 2d060386918cec5b1f6b923b5dce0af8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 4 | 4.1923 | | No log | 2.0 | 8 | 3.5605 | | c19ca338d87a19a546bbd85f5c833d82 |
apache-2.0 | ['translation'] | false | zho-eng * source group: Chinese * target group: English * OPUS readme: [zho-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-eng/README.md) * model: transformer * source language(s): cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant gan lzh lzh_Hans nan wuu yue yue_Hans yue_Hant * target language(s): eng * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-07-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.zip) * test set translations: [opus-2020-07-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.test.txt) * test set scores: [opus-2020-07-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.eval.txt) | ad4399ea103fc24dfe7faae60fe26175 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: zho-eng - source_languages: zho - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['zh', 'en'] - src_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'} - tgt_constituents: {'eng'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.test.txt - src_alpha3: zho - tgt_alpha3: eng - short_pair: zh-en - chrF2_score: 0.5479999999999999 - bleu: 36.1 - brevity_penalty: 0.948 - ref_len: 82826.0 - src_name: Chinese - tgt_name: English - train_date: 2020-07-17 - src_alpha2: zh - tgt_alpha2: en - prefer_old: False - long_pair: zho-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | a4e059df382367d1caf89254bccfa891 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Avatar Dreambooth model trained by yugkha3 with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)! To get started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars). Sample pictures of this concept: | 39ba72c43c76dcd57ce4c56877c9486e |
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