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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apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.8267 | 1.0 | 23927 | 1.6689 | 24.4634 | 11.7413 | 20.2154 | 23.0875 | 18.9993 | | 1.81 | 2.0 | 47854 | 1.6614 | 24.5589 | 11.8509 | 20.3011 | 23.1768 | 19.0 | | da8dd5c87979a2f2baff2c0e103f0ae9 |
apache-2.0 | ['generated_from_keras_callback'] | false | Imene/vit-base-patch16-224-in21k-wi2 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.9892 - Train Accuracy: 0.5568 - Train Top-3-accuracy: 0.8130 - Validation Loss: 3.0923 - Validation Accuracy: 0.4280 - Validation Top-3-accuracy: 0.7034 - Epoch: 4 | 14a2558c78b50cdb64edb0eea178dcd8 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 | b028d2c5e3badf861ede3c34610c86c4 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch | |:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:| | 3.8488 | 0.0720 | 0.1713 | 3.7116 | 0.1564 | 0.3617 | 0 | | 3.5246 | 0.2703 | 0.4898 | 3.4122 | 0.3217 | 0.5732 | 1 | | 3.2493 | 0.4150 | 0.6827 | 3.2232 | 0.3880 | 0.6633 | 2 | | 3.0840 | 0.5002 | 0.7670 | 3.1275 | 0.4255 | 0.6921 | 3 | | 2.9892 | 0.5568 | 0.8130 | 3.0923 | 0.4280 | 0.7034 | 4 | | d64837037f661d99d09e5bff4261d932 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | kpriyanshu256/whisper-large-v2-as-600-32-1e-05-bn-Assamese This model is a fine-tuned version of [kpriyanshu256/whisper-large-v2-as-600-32-1e-05-bn](https://huggingface.co/kpriyanshu256/whisper-large-v2-as-600-32-1e-05-bn) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2637 - Wer: 21.6928 | b296eb6f3d1ea1bda23d57bf64ef6aab |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 200 | e16d7547b2fbf235bb1755519029f74c |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1915 | 1.1 | 50 | 0.2129 | 26.3851 | | 0.0639 | 3.06 | 100 | 0.2305 | 23.0825 | | 0.0192 | 5.03 | 150 | 0.2391 | 22.0538 | | 0.0041 | 6.13 | 200 | 0.2637 | 21.6928 | | 2bc174498fa09ad48803d2d20f37b63a |
mit | [] | false | <h1>Transformer Encoder for Social Science (TESS)</h1> TESS is a deep neural network model intended for social science related NLP tasks. The model is developed by Haosen Ge, In Young Park, Xuancheng Qian, and Grace Zeng. We demonstrate in two validation tests that TESS outperforms BERT and RoBERTa by 16.7\% on average, especially when the number of training samples is limited (<1,000 training instances). The results display the superiority of TESS on social science text processing tasks. GitHub: [TESS](https://github.com/haosenge/TESS). <h2>Training Corpus</h2> | TEXT | SOURCE | | ------------- | ------------- | | Preferential Trade Agreements | ToTA | | Congressional Bills | Kornilova and Eidelman (2019) | |UNGA Resolutions | UN | |Firms' Annual Reports | Loughran and McDonald (2016)| | U.S. Court Opinions | Caselaw Access Project| The model is trained on 4 NVIDIA A100 GPUs for 120K steps. | f0a503799a83c760ca96108235996dec |
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: 0.7758 - Matthews Correlation: 0.5259 | ef5fe161ede83bece6e7ec8a435cc026 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.1926 | 1.0 | 535 | 0.7758 | 0.5259 | | 71497df5d13c4b213a2484248e418e23 |
apache-2.0 | ['generated_from_trainer'] | false | BERT_Mod_7_Squad This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.0928 | f6e62aba980dbb839f168739efdf7d11 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - 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: 3 | 049a7bee5a055f2055f94637e1b1cdda |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.189 | 1.0 | 4089 | 1.2196 | | 1.0312 | 2.0 | 8178 | 1.0691 | | 0.8954 | 3.0 | 12267 | 1.0928 | | 44aa0b42d6a82a42a4761d7b454b2a8a |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_unispeech-ml_s952 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (es)](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. | a30a7f811c266e28ae9ded653e5fdeea |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-work-4-16-5-oos 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 | ec9542f7c7c74067398989dc6bfd39d1 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4475 - Wer: 0.3400 | a09047c034ec6c85292a6ae8dbaf172b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.6929 | 4.0 | 500 | 2.4485 | 1.0009 | | 0.9441 | 8.0 | 1000 | 0.4848 | 0.4758 | | 0.3016 | 12.0 | 1500 | 0.4464 | 0.4016 | | 0.1715 | 16.0 | 2000 | 0.4666 | 0.3765 | | 0.1277 | 20.0 | 2500 | 0.4340 | 0.3515 | | 0.1082 | 24.0 | 3000 | 0.4544 | 0.3495 | | 0.0819 | 28.0 | 3500 | 0.4475 | 0.3400 | | 95b688f55191e4b67b4394051ceb5dc9 |
apache-2.0 | ['image-classification', 'other-image-classification', 'generated_from_trainer'] | false | vit-base-beans-demo-v3 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0645 - Accuracy: 0.9850 | 5ec21cace4c97bc7f8151b9528cbb82a |
apache-2.0 | ['image-classification', 'other-image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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: 2 - mixed_precision_training: Native AMP | 4bdd42952666aeb840535cd9cb112b83 |
apache-2.0 | ['image-classification', 'other-image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0397 | 1.54 | 100 | 0.0645 | 0.9850 | | cb30534c76058ea63b67b346c1493883 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr8e06-wd0.01-bs32 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2753 - Rmse: 0.5246 - Mse: 0.2753 - Mae: 0.4184 | f76ca7fb19b2b8d6a76ea2a57cc20cf4 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-06 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | c606c5e39431487b6d506691e6c02bc9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2769 | 1.0 | 623 | 0.2774 | 0.5266 | 0.2774 | 0.4296 | | 0.2745 | 2.0 | 1246 | 0.2739 | 0.5233 | 0.2739 | 0.4145 | | 0.2733 | 3.0 | 1869 | 0.2752 | 0.5246 | 0.2752 | 0.4215 | | 0.2722 | 4.0 | 2492 | 0.2744 | 0.5238 | 0.2744 | 0.4058 | | 0.2714 | 5.0 | 3115 | 0.2758 | 0.5251 | 0.2758 | 0.4232 | | 0.2705 | 6.0 | 3738 | 0.2753 | 0.5246 | 0.2753 | 0.4184 | | ba4744ad9a4cef5694adfd666714331e |
apache-2.0 | ['generated_from_trainer'] | false | fnet-large-finetuned-cola-copy4 This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6500 - Matthews Correlation: 0.0 | a8385138f5dc69f662d7a188b3b0bd73 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - num_epochs: 3.0 | e4df249c288dd6213f87c03c22fb1339 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6345 | 1.0 | 2138 | 0.6611 | 0.0 | | 0.6359 | 2.0 | 4276 | 0.6840 | 0.0 | | 0.6331 | 3.0 | 6414 | 0.6500 | 0.0 | | 4c89c3a66f124eb6eb13592beba5b0bc |
mit | ['generated_from_keras_callback'] | false | nandysoham16/Canadian_Armed_Forces-clustered This model is a fine-tuned version of [nandysoham16/0-clustered_aug](https://huggingface.co/nandysoham16/0-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5493 - Train End Logits Accuracy: 0.8611 - Train Start Logits Accuracy: 0.7812 - Validation Loss: 0.3839 - Validation End Logits Accuracy: 1.0 - Validation Start Logits Accuracy: 0.8000 - Epoch: 0 | 64c9e93e4980ec11556d47ec7717bd79 |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.5493 | 0.8611 | 0.7812 | 0.3839 | 1.0 | 0.8000 | 0 | | 6440015a96b22264aea9300b083e4175 |
apache-2.0 | ['automatic-speech-recognition', 'th'] | false | exp_w2v2t_th_vp-100k_s403 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition on Thai using the train split of [Common Voice 7.0](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. | f42adcd96878f831423457a49175251b |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08 - lr_scheduler: cosine - lr_warmup_steps: 500 - ema_inv_gamma: 1.0 - ema_inv_gamma: 0.75 - ema_inv_gamma: 0.9999 - mixed_precision: fp16 | 2b1a14d4f01c6b2e9fe2304ad31ad232 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xls-r-300m-cv8-es 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: - eval_loss: 0.2115 - eval_wer: 0.1931 - eval_runtime: 859.964 - eval_samples_per_second: 17.954 - eval_steps_per_second: 2.244 - epoch: 6.97 - step: 50000 | 88069ca21498a301d56084092b170254 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1368 - F1: 0.8599 | f30530e6c4f84745b2a41bdaa05e77f7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2618 | 1.0 | 525 | 0.1748 | 0.8134 | | 0.1274 | 2.0 | 1050 | 0.1398 | 0.8461 | | 0.0817 | 3.0 | 1575 | 0.1368 | 0.8599 | | ba1a5f8dcc82f9741d37c23dda8f60f5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 249 | 3.1538 | | No log | 2.0 | 498 | 2.6796 | | 4.0415 | 3.0 | 747 | 2.5939 | | 79997273a6811e9afc880adc2026e82b |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_vp-100k_age_teens-8_sixties-2_s284 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 (es)](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. | fad571f4ae41d105f81482b23378e2ea |
other | ['generated_from_trainer'] | false | dalio-all-io-125m-3-epoch This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the AlekseyKorshuk/dalio-all-io dataset. It achieves the following results on the evaluation set: - Loss: 2.7656 - Accuracy: 0.0497 | 92e45e04472eafce445ba9b3494739ea |
other | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 16 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 3.0 | 2123c4089e43e3f14c94b439aa827030 |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.1406 | 0.03 | 1 | 3.0762 | 0.0451 | | 3.074 | 0.07 | 2 | 3.0762 | 0.0451 | | 3.0557 | 0.1 | 3 | 3.0762 | 0.0451 | | 3.2166 | 0.14 | 4 | 3.0176 | 0.0457 | | 3.0989 | 0.17 | 5 | 2.9922 | 0.0460 | | 3.0732 | 0.21 | 6 | 2.9746 | 0.0464 | | 3.0867 | 0.24 | 7 | 2.9629 | 0.0463 | | 2.979 | 0.28 | 8 | 2.9512 | 0.0467 | | 3.1838 | 0.31 | 9 | 2.9414 | 0.0467 | | 2.9399 | 0.34 | 10 | 2.9336 | 0.0467 | | 2.926 | 0.38 | 11 | 2.9258 | 0.0471 | | 3.2144 | 0.41 | 12 | 2.9199 | 0.0473 | | 2.978 | 0.45 | 13 | 2.9141 | 0.0474 | | 3.0076 | 0.48 | 14 | 2.9082 | 0.0476 | | 2.9897 | 0.52 | 15 | 2.9023 | 0.0477 | | 2.8831 | 0.55 | 16 | 2.8945 | 0.0479 | | 2.9749 | 0.59 | 17 | 2.8867 | 0.0479 | | 2.9431 | 0.62 | 18 | 2.8828 | 0.0478 | | 3.0498 | 0.66 | 19 | 2.8770 | 0.0479 | | 2.9409 | 0.69 | 20 | 2.8711 | 0.0479 | | 2.96 | 0.72 | 21 | 2.8672 | 0.0480 | | 3.0767 | 0.76 | 22 | 2.8633 | 0.0478 | | 2.772 | 0.79 | 23 | 2.8594 | 0.0479 | | 3.0574 | 0.83 | 24 | 2.8535 | 0.0480 | | 2.8137 | 0.86 | 25 | 2.8496 | 0.0480 | | 2.8872 | 0.9 | 26 | 2.8438 | 0.0483 | | 3.0085 | 0.93 | 27 | 2.8398 | 0.0484 | | 2.9165 | 0.97 | 28 | 2.8359 | 0.0485 | | 2.8525 | 1.0 | 29 | 2.8340 | 0.0486 | | 2.7759 | 1.03 | 30 | 2.8301 | 0.0485 | | 2.7312 | 1.07 | 31 | 2.8281 | 0.0485 | | 2.6641 | 1.1 | 32 | 2.8262 | 0.0487 | | 2.7896 | 1.14 | 33 | 2.8242 | 0.0486 | | 2.7878 | 1.17 | 34 | 2.8223 | 0.0487 | | 2.4028 | 1.21 | 35 | 2.8203 | 0.0487 | | 2.5618 | 1.24 | 36 | 2.8184 | 0.0488 | | 2.6697 | 1.28 | 37 | 2.8164 | 0.0488 | | 2.6333 | 1.31 | 38 | 2.8145 | 0.0487 | | 2.4897 | 1.34 | 39 | 2.8125 | 0.0486 | | 2.4908 | 1.38 | 40 | 2.8105 | 0.0487 | | 2.6926 | 1.41 | 41 | 2.8086 | 0.0488 | | 2.6602 | 1.45 | 42 | 2.8066 | 0.0489 | | 2.8054 | 1.48 | 43 | 2.8047 | 0.0489 | | 2.5532 | 1.52 | 44 | 2.8047 | 0.0490 | | 2.4756 | 1.55 | 45 | 2.8027 | 0.0491 | | 2.6123 | 1.59 | 46 | 2.8008 | 0.0491 | | 2.5117 | 1.62 | 47 | 2.7988 | 0.0490 | | 2.5552 | 1.66 | 48 | 2.7969 | 0.0490 | | 2.5122 | 1.69 | 49 | 2.7949 | 0.0490 | | 2.5593 | 1.72 | 50 | 2.7930 | 0.0491 | | 2.5759 | 1.76 | 51 | 2.7910 | 0.0491 | | 2.5535 | 1.79 | 52 | 2.7891 | 0.0493 | | 2.6531 | 1.83 | 53 | 2.7871 | 0.0494 | | 2.5701 | 1.86 | 54 | 2.7852 | 0.0495 | | 2.6621 | 1.9 | 55 | 2.7832 | 0.0497 | | 2.532 | 1.93 | 56 | 2.7812 | 0.0496 | | 2.5928 | 1.97 | 57 | 2.7793 | 0.0497 | | 2.5486 | 2.0 | 58 | 2.7754 | 0.0497 | | 2.5009 | 2.03 | 59 | 2.7734 | 0.0497 | | 2.4346 | 2.07 | 60 | 2.7734 | 0.0498 | | 2.3259 | 2.1 | 61 | 2.7715 | 0.0497 | | 2.3569 | 2.14 | 62 | 2.7695 | 0.0498 | | 2.5898 | 2.17 | 63 | 2.7695 | 0.0498 | | 2.3657 | 2.21 | 64 | 2.7676 | 0.0498 | | 2.4875 | 2.24 | 65 | 2.7676 | 0.0498 | | 2.4392 | 2.28 | 66 | 2.7676 | 0.0497 | | 2.3595 | 2.31 | 67 | 2.7656 | 0.0497 | | 2.4757 | 2.34 | 68 | 2.7656 | 0.0498 | | 2.4617 | 2.38 | 69 | 2.7656 | 0.0498 | | 2.3376 | 2.41 | 70 | 2.7656 | 0.0499 | | 2.3129 | 2.45 | 71 | 2.7656 | 0.0498 | | 2.5703 | 2.48 | 72 | 2.7656 | 0.0498 | | 2.3491 | 2.52 | 73 | 2.7656 | 0.0498 | | 2.3484 | 2.55 | 74 | 2.7656 | 0.0498 | | 2.3782 | 2.59 | 75 | 2.7656 | 0.0497 | | 2.4033 | 2.62 | 76 | 2.7656 | 0.0498 | | 2.3821 | 2.66 | 77 | 2.7656 | 0.0498 | | 2.39 | 2.69 | 78 | 2.7656 | 0.0498 | | 2.3984 | 2.72 | 79 | 2.7656 | 0.0497 | | 2.3936 | 2.76 | 80 | 2.7656 | 0.0498 | | 2.4414 | 2.79 | 81 | 2.7656 | 0.0497 | | 2.4727 | 2.83 | 82 | 2.7656 | 0.0497 | | 2.3192 | 2.86 | 83 | 2.7656 | 0.0497 | | 2.4365 | 2.9 | 84 | 2.7656 | 0.0497 | | 2.5042 | 2.93 | 85 | 2.7656 | 0.0497 | | 2.4746 | 2.97 | 86 | 2.7656 | 0.0497 | | 2.5383 | 3.0 | 87 | 2.7656 | 0.0497 | | 392bd61a588bc7457871c263a8c383c9 |
apache-2.0 | ['generated_from_trainer'] | false | DistilBERT-POWO_MGH_Growth_Form_Finetuned 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: 0.2182 | 4799505a524eba63c4479f15e652083a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.2379 | 1.0 | 2054 | 0.2241 | | 0.2098 | 2.0 | 4108 | 0.2173 | | 0.2168 | 3.0 | 6162 | 0.2182 | | 367394a705e54e18c54f216d9a50d58d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_rte_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.4234 - Accuracy: 0.4729 | 1ffe2a581d9ad8bd6f6ba17679fd9acd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4604 | 1.0 | 10 | 0.4429 | 0.4729 | | 0.4358 | 2.0 | 20 | 0.4328 | 0.4729 | | 0.4282 | 3.0 | 30 | 0.4290 | 0.4729 | | 0.4246 | 4.0 | 40 | 0.4269 | 0.4729 | | 0.4227 | 5.0 | 50 | 0.4252 | 0.4729 | | 0.4204 | 6.0 | 60 | 0.4243 | 0.4729 | | 0.4191 | 7.0 | 70 | 0.4238 | 0.4729 | | 0.4185 | 8.0 | 80 | 0.4235 | 0.4729 | | 0.4175 | 9.0 | 90 | 0.4234 | 0.4729 | | 0.4164 | 10.0 | 100 | 0.4235 | 0.4729 | | 0.418 | 11.0 | 110 | 0.4236 | 0.4729 | | 0.4169 | 12.0 | 120 | 0.4236 | 0.4729 | | 0.4173 | 13.0 | 130 | 0.4238 | 0.4729 | | 0.4168 | 14.0 | 140 | 0.4239 | 0.4729 | | 81cf1e3297c301369604d3d0bf43ca78 |
apache-2.0 | ['generated_from_keras_callback'] | false | kasrahabib/20_propogated This model is a fine-tuned version of [kasrahabib/XXX08_02_23__-bucket-finetunned](https://huggingface.co/kasrahabib/XXX08_02_23__-bucket-finetunned) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0504 - Validation Loss: 0.1528 - Epoch: 9 | 2d697bbaf075f62f2c90b5bdd5730fa0 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7660, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | c2ca07384747c955bd8e991fc6786f06 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.2492 | 0.1740 | 0 | | 0.1527 | 0.1501 | 1 | | 0.1092 | 0.1582 | 2 | | 0.0879 | 0.1568 | 3 | | 0.0774 | 0.1577 | 4 | | 0.0689 | 0.1513 | 5 | | 0.0597 | 0.1598 | 6 | | 0.0600 | 0.1536 | 7 | | 0.0526 | 0.1519 | 8 | | 0.0504 | 0.1528 | 9 | | 524302f678007e485f4b5b9e4f940477 |
apache-2.0 | ['generated_from_keras_callback'] | false | alk/t5-small-finetuned-cnn_dailymail-en-es This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.9163 - Validation Loss: 1.7610 - Epoch: 3 | 00ccb1de4fa0d940dc16e38a9ed899ef |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 71776, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 | 0f7fe2394c5c2a0a5ac44eea7160c534 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.9945 | 1.7837 | 0 | | 1.9478 | 1.7694 | 1 | | 1.9278 | 1.7646 | 2 | | 1.9163 | 1.7610 | 3 | | b3158b30aeb63261577c354c92a8b366 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-mnli-target-glue-qnli This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-mnli](https://huggingface.co/muhtasham/tiny-mlm-glue-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4695 - Accuracy: 0.7814 | 3d7ddd1f7b7b593343683a11c9ccc074 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6034 | 0.15 | 500 | 0.5431 | 0.7335 | | 0.5403 | 0.31 | 1000 | 0.5253 | 0.7459 | | 0.5174 | 0.46 | 1500 | 0.4953 | 0.7659 | | 0.5137 | 0.61 | 2000 | 0.5259 | 0.7483 | | 0.511 | 0.76 | 2500 | 0.4814 | 0.7750 | | 0.5032 | 0.92 | 3000 | 0.4670 | 0.7847 | | 0.4901 | 1.07 | 3500 | 0.4525 | 0.7904 | | 0.4798 | 1.22 | 4000 | 0.4679 | 0.7836 | | 0.4667 | 1.37 | 4500 | 0.4752 | 0.7798 | | 0.4736 | 1.53 | 5000 | 0.4695 | 0.7814 | | 3ec6dc6ed8c46a47ae00f870a76bb272 |
unlicense | [] | false | 권장사항 (Recommend) * 가장 추천되는 모델은 BAD 0.3입니다. * BA 0.1이 가장 반실사에 가깝고, BAD 0.5는 매우 실사스럽고 드림 특유의 뭉개짐이 많습니다. * 권장 프롬프트 : detailed face, restore face * 권장 네거티브 : (worst quality, low quality:1.4), (loli, child, infant, baby:1.3), accessories * Most recommended model is BAD 0.3. * BA 0.1 likes semi-realistic, BAD 0.5 is very realistic but it has many errors. * Prompts recommended : detailed face, restore face * Negative recommended : (worst quality, low quality:1.4), (loli, child, infant, baby:1.3), accessories | 707df9969cfd935cb97a38bc2e0e7db4 |
mit | ['generated_from_trainer'] | false | model_dir This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0380 - Pearson: 0.9399 | e320e99e776f63c2ebf10767dade3cf0 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 128 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP | 018977d71652801ccd65b2fb9990d80b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 1.0 | 12 | 0.2773 | 0.7230 | | No log | 2.0 | 24 | 0.1120 | 0.7812 | | No log | 3.0 | 36 | 0.1090 | 0.8638 | | No log | 4.0 | 48 | 0.0613 | 0.9163 | | No log | 5.0 | 60 | 0.0447 | 0.9409 | | No log | 6.0 | 72 | 0.0356 | 0.9402 | | No log | 7.0 | 84 | 0.0368 | 0.9359 | | No log | 8.0 | 96 | 0.0408 | 0.9295 | | No log | 9.0 | 108 | 0.0397 | 0.9382 | | No log | 10.0 | 120 | 0.0380 | 0.9399 | | cd70313dd926cc62136494f2e2f2c681 |
mit | ['text-classification', 'generated_from_trainer'] | false | xnli_xlm_r_base_only_en_automodel_single_gpu This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xnli dataset. It achieves the following results on the evaluation set: - Loss: 1.0986 - Accuracy: 0.3333 | 42fad2cba2e12be077cf4abbcd47488f |
mit | ['text-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.1064 | 0.04 | 1000 | 1.1003 | 0.3333 | | 1.1042 | 0.08 | 2000 | 1.1006 | 0.3333 | | 1.1049 | 0.12 | 3000 | 1.0992 | 0.3333 | | 1.1037 | 0.16 | 4000 | 1.1019 | 0.3333 | | 1.1037 | 0.2 | 5000 | 1.0986 | 0.3333 | | 1.1028 | 0.24 | 6000 | 1.1014 | 0.3333 | | 1.1044 | 0.29 | 7000 | 1.1059 | 0.3333 | | 1.102 | 0.33 | 8000 | 1.1000 | 0.3333 | | 1.1022 | 0.37 | 9000 | 1.1012 | 0.3333 | | 1.1019 | 0.41 | 10000 | 1.0995 | 0.3333 | | 1.1018 | 0.45 | 11000 | 1.0990 | 0.3333 | | 1.103 | 0.49 | 12000 | 1.1018 | 0.3333 | | 1.1016 | 0.53 | 13000 | 1.0989 | 0.3333 | | 1.1021 | 0.57 | 14000 | 1.0995 | 0.3333 | | 1.1012 | 0.61 | 15000 | 1.1026 | 0.3333 | | 1.1012 | 0.65 | 16000 | 1.1000 | 0.3333 | | 1.1018 | 0.69 | 17000 | 1.0992 | 0.3333 | | 1.1004 | 0.73 | 18000 | 1.0996 | 0.3333 | | 1.101 | 0.77 | 19000 | 1.0987 | 0.3333 | | 1.1011 | 0.81 | 20000 | 1.1001 | 0.3333 | | 1.1006 | 0.86 | 21000 | 1.0991 | 0.3333 | | 1.1006 | 0.9 | 22000 | 1.1028 | 0.3333 | | 1.1003 | 0.94 | 23000 | 1.0988 | 0.3333 | | 1.1006 | 0.98 | 24000 | 1.0987 | 0.3333 | | 1.1008 | 1.02 | 25000 | 1.0995 | 0.3333 | | 1.1011 | 1.06 | 26000 | 1.0987 | 0.3333 | | 1.1003 | 1.1 | 27000 | 1.0987 | 0.3333 | | 1.1002 | 1.14 | 28000 | 1.1020 | 0.3333 | | 1.1 | 1.18 | 29000 | 1.0988 | 0.3333 | | 1.1002 | 1.22 | 30000 | 1.0995 | 0.3333 | | 1.1001 | 1.26 | 31000 | 1.0989 | 0.3333 | | 1.1001 | 1.3 | 32000 | 1.0986 | 0.3333 | | 1.0999 | 1.34 | 33000 | 1.0989 | 0.3333 | | 1.1004 | 1.39 | 34000 | 1.0987 | 0.3333 | | 1.0993 | 1.43 | 35000 | 1.0989 | 0.3333 | | 1.1003 | 1.47 | 36000 | 1.0989 | 0.3333 | | 1.0999 | 1.51 | 37000 | 1.0991 | 0.3333 | | 1.0999 | 1.55 | 38000 | 1.0993 | 0.3333 | | 1.0994 | 1.59 | 39000 | 1.0993 | 0.3333 | | 1.0994 | 1.63 | 40000 | 1.0989 | 0.3333 | | 1.0999 | 1.67 | 41000 | 1.0988 | 0.3333 | | 1.0995 | 1.71 | 42000 | 1.0996 | 0.3333 | | 1.1003 | 1.75 | 43000 | 1.0987 | 0.3333 | | 1.0996 | 1.79 | 44000 | 1.0987 | 0.3333 | | 1.0996 | 1.83 | 45000 | 1.0990 | 0.3333 | | 1.0994 | 1.87 | 46000 | 1.0990 | 0.3333 | | 1.0992 | 1.91 | 47000 | 1.1000 | 0.3333 | | 1.0992 | 1.96 | 48000 | 1.0989 | 0.3333 | | 1.0991 | 2.0 | 49000 | 1.0991 | 0.3333 | | 1.099 | 2.04 | 50000 | 1.0987 | 0.3333 | | 1.0992 | 2.08 | 51000 | 1.0987 | 0.3333 | | 1.0995 | 2.12 | 52000 | 1.0988 | 0.3333 | | 1.0994 | 2.16 | 53000 | 1.0989 | 0.3333 | | 1.0994 | 2.2 | 54000 | 1.0989 | 0.3333 | | 1.0993 | 2.24 | 55000 | 1.0988 | 0.3333 | | 1.0988 | 2.28 | 56000 | 1.0986 | 0.3333 | | 1.0995 | 2.32 | 57000 | 1.0986 | 0.3333 | | 1.0991 | 2.36 | 58000 | 1.0988 | 0.3333 | | 1.0989 | 2.4 | 59000 | 1.0987 | 0.3333 | | 1.0991 | 2.44 | 60000 | 1.0990 | 0.3333 | | 1.0992 | 2.49 | 61000 | 1.0989 | 0.3333 | | 1.0992 | 2.53 | 62000 | 1.0987 | 0.3333 | | 1.0989 | 2.57 | 63000 | 1.0986 | 0.3333 | | 1.099 | 2.61 | 64000 | 1.0987 | 0.3333 | | 1.0991 | 2.65 | 65000 | 1.0986 | 0.3333 | | 1.0991 | 2.69 | 66000 | 1.0986 | 0.3333 | | 1.0991 | 2.73 | 67000 | 1.0987 | 0.3333 | | 1.0986 | 2.77 | 68000 | 1.0987 | 0.3333 | | 1.0992 | 2.81 | 69000 | 1.0986 | 0.3333 | | 1.0989 | 2.85 | 70000 | 1.0986 | 0.3333 | | 1.099 | 2.89 | 71000 | 1.0987 | 0.3333 | | 1.0989 | 2.93 | 72000 | 1.0986 | 0.3333 | | 1.0989 | 2.97 | 73000 | 1.0986 | 0.3333 | | 71f1ef3cd8d40d1ad20c27be98aac3c5 |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-pointer-mtop This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the mtop dataset. It achieves the following results on the evaluation set: - Loss: 0.1131 - Exact Match: 0.7199 | a92ff815801909156dce760f83223b9c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | |:-------------:|:-----:|:----:|:---------------:|:-----------:| | 1.7749 | 6.65 | 200 | 0.5892 | 0.0031 | | 0.6021 | 13.33 | 400 | 0.5160 | 0.0139 | | 0.6044 | 19.98 | 600 | 0.4080 | 0.0532 | | 0.3302 | 26.65 | 800 | 0.1865 | 0.3620 | | 0.1483 | 33.33 | 1000 | 0.1267 | 0.5105 | | 0.0768 | 39.98 | 1200 | 0.1131 | 0.5298 | | 0.0525 | 46.65 | 1400 | 0.1219 | 0.5414 | | 0.0801 | 53.33 | 1600 | 0.1186 | 0.5275 | | 0.0331 | 59.98 | 1800 | 0.1306 | 0.5423 | | 0.0254 | 66.65 | 2000 | 0.1396 | 0.5396 | | 0.0168 | 73.33 | 2200 | 0.1560 | 0.5436 | | 0.0129 | 79.98 | 2400 | 0.1659 | 0.5494 | | 0.0105 | 86.65 | 2600 | 0.1699 | 0.5423 | | 0.0088 | 93.33 | 2800 | 0.1742 | 0.5472 | | 0.0077 | 99.98 | 3000 | 0.1775 | 0.5468 | | 860c13935783790fe2306b08ba819a7f |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-brainTumorData This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. | a200f15068179c7c9c7489c76f51a830 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 | e2909760e6a9b88b111fbca1e8176d4a |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'xlsr-fine-tuning-week'] | false | wav2vec2-xls-r-300m-west-slavic-cv8 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 8 dataset of five similar languages with similar scripts: Czech, Slovak, Polish, Slovenian and Upper Sorbian. Training and validation sets were concatenated and shuffled. Evaluation set used for training was concatenated from the respective test sets and shuffled while limiting each language to at most 2000 samples. During training, cca WER 70 was achieved on this set. | 6c06ca4553df9b852c278599f813d62e |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'xlsr-fine-tuning-week'] | false | Evaluation script ``` python eval.py --model_id comodoro/wav2vec2-xls-r-300m-west-slavic-cv8 --dataset mozilla-foundation/common_voice_8_0 --split test --config {lang} ``` | 24e449de2f055134d9db6a6092f7cd90 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'xlsr-fine-tuning-week'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 50 - mixed_precision_training: Native AMP | b9131518fbd50e62ad03bf6ad52baf5f |
openrail | ['generated_from_trainer'] | false | santacoder-finetuned-the-stack-bash-3 This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan | e5169b4fbbffbb18fc98d561e8087e72 |
openrail | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 5000 - mixed_precision_training: Native AMP | 121febce6e08eb2c6b6625f63b3d73f7 |
openrail | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0 | 0.1 | 500 | nan | | 0.0 | 0.2 | 1000 | nan | | 0.0 | 0.3 | 1500 | nan | | 0.0 | 0.4 | 2000 | nan | | 0.0 | 0.5 | 2500 | nan | | 0.0 | 0.6 | 3000 | nan | | 0.0 | 0.7 | 3500 | nan | | 0.0 | 0.8 | 4000 | nan | | 0.0 | 0.9 | 4500 | nan | | 0.0 | 1.0 | 5000 | nan | | 801739f64c131f78a91fa313a949a0d9 |
apache-2.0 | [] | false | Türkçe Multi-label Intent Classification RoBERTa Depremzedelerin ihtiyaçlarını karşılamak için etiketlenmiş eğitilmiş multi-label RoBERTa modeli. Aşağıda değerlendirme sonuçları var. **Evaluation** - 'eval_loss': 0.18568251545368838, - 'eval_runtime': 2.7693, - 'eval_samples_per_second': 254.935, - 'eval_steps_per_second': 8.305, - 'epoch': 3.0 **Classification Report** ``` precision recall f1-score support Alakasiz 0.95 0.87 0.91 781 Barinma 0.86 0.52 0.65 234 Elektronik 0.00 0.00 0.00 171 Giysi 0.89 0.25 0.39 122 Kurtarma 0.86 0.78 0.82 472 Lojistik 0.00 0.00 0.00 123 Saglik 0.78 0.05 0.09 148 Su 0.92 0.11 0.20 96 Yagma 0.00 0.00 0.00 19 Yemek 0.94 0.42 0.58 158 micro avg 0.91 0.55 0.69 2324 macro avg 0.62 0.30 0.36 2324 weighted avg 0.78 0.55 0.61 2324 samples avg 0.69 0.63 0.65 2324 ``` | 0ac762aef0a2d35254454e1bb23122e7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-health_facts This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the health_fact dataset. It achieves the following results on the evaluation set: - Loss: 1.1227 - Accuracy: 0.6285 - F1: 0.6545 | 7af581da52bb157fcd82a5dbadca6d68 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.1367 | 1.0 | 154 | 0.9423 | 0.5560 | 0.6060 | | 0.9444 | 2.0 | 308 | 0.9267 | 0.5733 | 0.6170 | | 0.8248 | 3.0 | 462 | 0.9483 | 0.5832 | 0.6256 | | 0.7213 | 4.0 | 616 | 1.0119 | 0.5815 | 0.6219 | | 0.608 | 5.0 | 770 | 1.1227 | 0.6285 | 0.6545 | | c62d9e766aa35b05e84ad3c223f54995 |
cc-by-sa-4.0 | ['text-classification', 'hate-speech'] | false | roberta-base-frenk-hate Text classification model based on [`roberta-base`](https://huggingface.co/roberta-base) and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the English subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable). | f55582d30a0287be3ed9fa6748b0aa34 |
cc-by-sa-4.0 | ['text-classification', 'hate-speech'] | false | Fine-tuning hyperparameters Fine-tuning was performed with `simpletransformers`. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are: ```python model_args = { "num_train_epochs": 6, "learning_rate": 3e-6, "train_batch_size": 69} ``` | 50e3f1b8b9b092618f18184172aeebec |
cc-by-sa-4.0 | ['text-classification', 'hate-speech'] | false | Performance The same pipeline was run with two other transformer models and `fasttext` for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed. | model | average accuracy | average macro F1| |---|---|---| |roberta-base-frenk-hate|0.7915|0.7785| |xlm-roberta-large |0.7904|0.77876| |xlm-roberta-base |0.7577|0.7402| |fasttext|0.725 |0.707 | From recorded accuracies and macro F1 scores p-values were also calculated: Comparison with `xlm-roberta-base`: | test | accuracy p-value | macro F1 p-value| | --- | --- | --- | |Wilcoxon|0.00781|0.00781| |Mann Whithney U-test|0.00108|0.00108| |Student t-test | 1.35e-08 | 1.05e-07| Comparison with `xlm-roberta-large` yielded inconclusive results. `roberta-base` has average accuracy 0.7915, while `xlm-roberta-large` has average accuracy of 0.7904. If macro F1 scores were to be compared, `roberta-base` actually has lower average than `xlm-roberta-large`: 0.77852 vs 0.77876 respectively. The same statistical tests were performed with the premise that `roberta-base` has greater metrics, and the results are given below. | test | accuracy p-value | macro F1 p-value| | --- | --- | --- | |Wilcoxon|0.188|0.406| |Mann Whithey|0.375|0.649| |Student t-test | 0.681| 0.934| With reversed premise (i.e., that `xlm-roberta-large` has greater statistics) the Wilcoxon p-value for macro F1 scores for this case reaches 0.656, Mann-Whithey p-value is 0.399, and of course the Student p-value stays the same. It was therefore concluded that performance of the two models are not statistically significantly different from one another. | 877eeaaa1d0c46ff80146c97595ab011 |
cc-by-sa-4.0 | ['text-classification', 'hate-speech'] | false | Use examples ```python from simpletransformers.classification import ClassificationModel model_args = { "num_train_epochs": 6, "learning_rate": 3e-6, "train_batch_size": 69} model = ClassificationModel( "roberta", "5roop/roberta-base-frenk-hate", use_cuda=True, args=model_args ) predictions, logit_output = model.predict(["Build the wall", "Build the wall of trust"] ) predictions | 8a229779f1d3b382e1c14e4e8cceec39 |
cc-by-sa-4.0 | ['text-classification', 'hate-speech'] | false | Citation If you use the model, please cite the following paper on which the original model is based: ``` @article{DBLP:journals/corr/abs-1907-11692, author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov}, title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach}, journal = {CoRR}, volume = {abs/1907.11692}, year = {2019}, url = {http://arxiv.org/abs/1907.11692}, archivePrefix = {arXiv}, eprint = {1907.11692}, timestamp = {Thu, 01 Aug 2019 08:59:33 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` and the dataset used for fine-tuning: ``` @misc{ljubešić2019frenk, title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English}, author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec}, year={2019}, eprint={1906.02045}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/1906.02045} } ``` | 4da0f0a2820ec068773b19b98d4af87b |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2526 | fcaf140b45db13a8bac1bc0d22516a79 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - 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 - num_epochs: 16 - mixed_precision_training: Native AMP | 667db5c171ffad19769567b74760accf |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.1071 | 1.0 | 291 | 1.6964 | | 1.6421 | 2.0 | 582 | 1.4279 | | 1.4853 | 3.0 | 873 | 1.3924 | | 1.4014 | 4.0 | 1164 | 1.3701 | | 1.3388 | 5.0 | 1455 | 1.1944 | | 1.283 | 6.0 | 1746 | 1.2795 | | 1.2394 | 7.0 | 2037 | 1.2671 | | 1.2014 | 8.0 | 2328 | 1.2084 | | 1.1668 | 9.0 | 2619 | 1.1783 | | 1.14 | 10.0 | 2910 | 1.2076 | | 1.1277 | 11.0 | 3201 | 1.2081 | | 1.1053 | 12.0 | 3492 | 1.1628 | | 1.0819 | 13.0 | 3783 | 1.2544 | | 1.0763 | 14.0 | 4074 | 1.1695 | | 1.0634 | 15.0 | 4365 | 1.1157 | | 1.0637 | 16.0 | 4656 | 1.2526 | | ea73aab5b1a193c180ce7fe7b0234098 |
mit | ['huggan', 'gan', 'image-to-image', 'huggingnft', 'nft', 'image', 'images'] | false | Model description CycleGAN for unpaired image-to-image translation. Given two image domains A and B, the following components are trained end2end to translate between such domains: - A generator A to B, named G_AB conditioned on an image from A - A generator B to A, named G_BA conditioned on an image from B - A domain classifier D_A, associated with G_AB - A domain classifier D_B, associated with G_BA At inference time, G_AB or G_BA are relevant to translate images, respectively A to B or B to A. In the general setting, this technique provides style transfer functionalities between the selected image domains A and B. This allows to obtain a generated translation by G_AB, of an image from domain A that resembles the distribution of the images from domain B, and viceversa for the generator G_BA. Under these framework, these aspects have been used to perform style transfer between NFT collections. A collection is selected as domain A, another one as domain B and the CycleGAN provides forward and backward translation between A and B. This has showed to allows high quality translation even in absence of paired sample-ground-truth data. In particular, the model performs well with stationary backgrounds (no drastic texture changes in the appearance of backgrounds) as it is capable of recognizing the attributes of each of the elements of an NFT collections. An attribute can be a variation in type of dressed fashion items such as sunglasses, earrings, clothes and also face or body attributes with respect to a common template model of the given NFT collection). | 0b9083546cdba493bbff09e4f07e4ead |
mit | ['huggan', 'gan', 'image-to-image', 'huggingnft', 'nft', 'image', 'images'] | false | How to use ```python import torch from PIL import Image from huggan.pytorch.cyclegan.modeling_cyclegan import GeneratorResNet from torchvision import transforms as T from torchvision.transforms import Compose, Resize, ToTensor, Normalize from torchvision.utils import make_grid from huggingface_hub import hf_hub_download, file_download from accelerate import Accelerator import json def load_lightweight_model(model_name): file_path = file_download.hf_hub_download( repo_id=model_name, filename="config.json" ) config = json.loads(open(file_path).read()) organization_name, name = model_name.split("/") model = Trainer(**config, organization_name=organization_name, name=name) model.load(use_cpu=True) model.accelerator = Accelerator() return model def get_concat_h(im1, im2): dst = Image.new('RGB', (im1.width + im2.width, im1.height)) dst.paste(im1, (0, 0)) dst.paste(im2, (im1.width, 0)) return dst n_channels = 3 image_size = 256 input_shape = (image_size, image_size) transform = Compose([ T.ToPILImage(), T.Resize(input_shape), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) | 8bb1345669cc1fdf223448a038d89add |
mit | ['huggan', 'gan', 'image-to-image', 'huggingnft', 'nft', 'image', 'images'] | false | B = Translator(GAN(A)) translator = GeneratorResNet.from_pretrained(f'huggingnft/{model_name}', input_shape=(n_channels, image_size, image_size), num_residual_blocks=9) | 0e9cb3865deeff21c274befee2efd383 |
mit | ['huggan', 'gan', 'image-to-image', 'huggingnft', 'nft', 'image', 'images'] | false | load the GAN generator of source images that will be translated by the translation model model = load_lightweight_model(f"huggingnft/{model_name.split('__2__')[0]}") collectionA = model.generate_app( num=timestamped_filename(), nrow=nrows, checkpoint=-1, types="default" )[1] | d335e54973b24d8af2a219580fbf619f |
mit | ['huggan', 'gan', 'image-to-image', 'huggingnft', 'nft', 'image', 'images'] | false | translate the resized collectionA to collectionB collectionB = translator(input) out_transform = T.ToPILImage() results = [] for collA_image, collB_image in zip(input, collectionB): results.append( get_concat_h(out_transform(make_grid(collA_image, nrow=1, normalize=True)), out_transform(make_grid(collB_image, nrow=1, normalize=True))) ) ``` | 62455ca5cf4cb164d7a9bb9fc32891e3 |
mit | ['huggan', 'gan', 'image-to-image', 'huggingnft', 'nft', 'image', 'images'] | false | Limitations and bias Translation between collections provides exceptional output images in the case of NFT collections that portray subjects in the same way. If the backgrounds vary too much within either of the collections, performance degrades or many more training iterations re required to achieve acceptable results. | 1a03dc2c371fd6dc88cee7ccbb07ef55 |
mit | ['huggan', 'gan', 'image-to-image', 'huggingnft', 'nft', 'image', 'images'] | false | Training data The CycleGAN model is trained on an unpaired dataset of samples from two selected NFT collections: colle tionA and collectionB. To this end, two collections are loaded by means of the function load_dataset in the huggingface library, as follows. A list of all available collections is available at [huggingNFT](https://huggingface.co/huggingnft) ```python from datasets import load_dataset collectionA = load_dataset("huggingnft/COLLECTION_A") collectionB = load_dataset("huggingnft/COLLECTION_B") ``` | b68ef883ac3598060b27d8ef5a5f2e51 |
mit | ['huggan', 'gan', 'image-to-image', 'huggingnft', 'nft', 'image', 'images'] | false | Preprocessing The following transformations are applied to each input sample of collectionA and collectionB. The input size is fixed to RGB images of height, width = 256, 256 ```python n_channels = 3 image_size = 256 input_shape = (image_size, image_size) transform = Compose([ T.ToPILImage(), T.Resize(input_shape), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) ``` | 166e522aabc6270cd85311b8780ede6e |
mit | ['huggan', 'gan', 'image-to-image', 'huggingnft', 'nft', 'image', 'images'] | false | Hyperparameters The following configuration has been kept fixed for all translation models: - learning rate 0.0002 - number of epochs 200 - learning rate decay activation at epoch 80 - number of residual blocks of the cyclegan 9 - cycle loss weight 10.0 - identity loss weight 5.0 - optimizer ADAM with beta1 0.5 and beta2 0.999 - batch size 8 - NO mixed precision training | fba9295a8ca367438bb686ce48cb2f34 |
mit | ['huggan', 'gan', 'image-to-image', 'huggingnft', 'nft', 'image', 'images'] | false | Training reports [Cryptopunks to boreapeyachtclub](https://wandb.ai/chris1nexus/experiments--experiments_cyclegan_punk_to_apes_HQ--0/reports/CycleGAN-training-report--VmlldzoxODUxNzQz?accessToken=vueurpbhd2i8n347j880yakggs0sqdf7u0hpz3bpfsbrxcmk1jk4obg18f6wfk9w) [Boreapeyachtclub to mutant-ape-yacht-club](https://wandb.ai/chris1nexus/experiments--my_paperspace_boredapeyachtclub__2__mutant-ape-yacht-club--11/reports/CycleGAN-training-report--VmlldzoxODUxNzg4?accessToken=jpyviwn7kdf5216ycrthwp6l8t3heb0lt8djt7dz12guu64qnpdh3ekecfcnoahu) | a71892fabeae586b29d269a8d5a67e09 |
mit | ['huggan', 'gan', 'image-to-image', 'huggingnft', 'nft', 'image', 'images'] | false | Generated Images In the provided images, row0 and row2 represent real images from the respective collections. Row1 is the translation of the immediate above images in row0 by means of the G_AB translation model. Row3 is the translation of the immediate above images in row2 by means of the G_BA translation model. Visualization over the training iterations for [boreapeyachtclub to mutant-ape-yacht-club](https://wandb.ai/chris1nexus/experiments--my_paperspace_boredapeyachtclub__2__mutant-ape-yacht-club--11/reports/Shared-panel-22-04-15-08-04-99--VmlldzoxODQ0MDI3?accessToken=45m3kxex5m3rpev3s6vmrv69k3u9p9uxcsp2k90wvbxwxzlqbqjqlnmgpl9265c0) Visualization over the training iterations for [Cryptopunks to boreapeyachtclub](https://wandb.ai/chris1nexus/experiments--experiments_cyclegan_punk_to_apes_HQ--0/reports/Shared-panel-22-04-17-11-04-83--VmlldzoxODUxNjk5?accessToken=o25si6nflp2xst649vt6ayt56bnb95mxmngt1ieso091j2oazmqnwaf4h78vc2tu) | d7d82554c36388a4ce127547adcffca3 |
mit | ['huggan', 'gan', 'image-to-image', 'huggingnft', 'nft', 'image', 'images'] | false | References ```bibtex @misc{https://doi.org/10.48550/arxiv.1703.10593, doi = {10.48550/ARXIV.1703.10593}, url = {https://arxiv.org/abs/1703.10593}, author = {Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A.}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks}, publisher = {arXiv}, year = {2017}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` | f442392f19552314ac1d207ae50b630e |
creativeml-openrail-m | ['text-to-image'] | false | drag_queen_Shangela on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook | ec51bc8fcd270fa53d073bd868e70731 |
creativeml-openrail-m | ['text-to-image'] | false | Model by chrisin2d This your the Stable Diffusion model fine-tuned the drag_queen_Shangela concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt(s)`: **** You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb). You can run your new concept via A1111 Colab :[Fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Sample pictures of this concept: | de5dabab7f94032ca9c14bb77d2873cf |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_700k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 1, Step 700k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model | fda2b0329de14e4c615f1577f1deabc5 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_700k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_700k') model = TFBertModel.from_pretrained("google/multiberts-seed_1-step_700k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_700k') model = BertModel.from_pretrained("google/multiberts-seed_1-step_700k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 341a58d10a4848d55ec1b152b6152e38 |
apache-2.0 | [] | false | doc2query/msmarco-german-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/beir-cellar/beir) we have an example how to use docT5query with Pyserini.
- **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. In our [GPL-Paper](https://arxiv.org/abs/2112.07577) / [GPL Example on SBERT.net](https://www.sbert.net/examples/domain_adaptation/README.html | d1c3975a93e36094cf8d13a4bef95ad7 |
apache-2.0 | [] | false | gpl-generative-pseudo-labeling) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.
| 62800460204278538aacd001ef7ac0f6 |
apache-2.0 | [] | false | Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
model_name = 'doc2query/msmarco-german-mt5-base-v1'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
text = "Python ist eine universelle, üblicherweise interpretierte, höhere Programmiersprache. Sie hat den Anspruch, einen gut lesbaren, knappen Programmierstil zu fördern. So werden beispielsweise Blöcke nicht durch geschweifte Klammern, sondern durch Einrückungen strukturiert."
def create_queries(para):
input_ids = tokenizer.encode(para, return_tensors='pt')
with torch.no_grad():
| 12f70ff7a5d308936ec9d953f9744919 |
apache-2.0 | [] | false | Here we use top_k / top_k random sampling. It generates more diverse queries, but of lower quality
sampling_outputs = model.generate(
input_ids=input_ids,
max_length=64,
do_sample=True,
top_p=0.95,
top_k=10,
num_return_sequences=5
)
| 60634c0e4b72b54aa27a4532b6752ea1 |
apache-2.0 | [] | false | Here we use Beam-search. It generates better quality queries, but with less diversity
beam_outputs = model.generate(
input_ids=input_ids,
max_length=64,
num_beams=5,
no_repeat_ngram_size=2,
num_return_sequences=5,
early_stopping=True
)
print("Paragraph:")
print(para)
print("\nBeam Outputs:")
for i in range(len(beam_outputs)):
query = tokenizer.decode(beam_outputs[i], skip_special_tokens=True)
print(f'{i + 1}: {query}')
print("\nSampling Outputs:")
for i in range(len(sampling_outputs)):
query = tokenizer.decode(sampling_outputs[i], skip_special_tokens=True)
print(f'{i + 1}: {query}')
create_queries(text)
```
**Note:** `model.generate()` is non-deterministic for top_k/top_n sampling. It produces different queries each time you run it.
| f6aa352ce09c956345b5faa5c1b489cf |
apache-2.0 | [] | false | Training
This model fine-tuned [google/mt5-base](https://huggingface.co/google/mt5-base) for 66k training steps (4 epochs on the 500k training pairs from MS MARCO). For the training script, see the `train_script.py` in this repository.
The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces.
This model was trained on a (query, passage) from the [mMARCO dataset](https://github.com/unicamp-dl/mMARCO).
| f118421748aadba781c0ca05fed96259 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2_xlsr50k_english_phoneme This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on [the TIMIT dataset](https://catalog.ldc.upenn.edu/LDC93s1). It achieves the following results on the evaluation set: - Loss: 0.5783 - Cer: 0.1178 | 4cda6563e792b6d547a0c0f14588583d |
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