Datasets:
davanstrien
/
model_cards_with_readmes_sections

Dataset card Data Studio Files
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Community
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readme_section
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mit
['generated_from_trainer']
false
lilt-en-funsd This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set: - Loss: 1.6459 - Answer: {'precision': 0.8831942789034565, 'recall': 0.9069767441860465, 'f1': 0.894927536231884, 'number': 817} - Header: {'precision': 0.6213592233009708, 'recall': 0.5378151260504201, 'f1': 0.5765765765765765, 'number': 119} - Question: {'precision': 0.8998178506375227, 'recall': 0.9173630454967502, 'f1': 0.9085057471264367, 'number': 1077} - Overall Precision: 0.8789 - Overall Recall: 0.8907 - Overall F1: 0.8848 - Overall Accuracy: 0.8068
3f780a796a244986bfededd8f9f952f3
mit
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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 - training_steps: 2000 - mixed_precision_training: Native AMP
c241f9f474da6b44afe9026f8d84e999
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.4201 | 10.53 | 200 | 0.8003 | {'precision': 0.8321995464852607, 'recall': 0.8984088127294981, 'f1': 0.8640376692171865, 'number': 817} | {'precision': 0.5714285714285714, 'recall': 0.5714285714285714, 'f1': 0.5714285714285714, 'number': 119} | {'precision': 0.8651079136690647, 'recall': 0.89322191272052, 'f1': 0.8789401553220649, 'number': 1077} | 0.8348 | 0.8763 | 0.8551 | 0.8104 | | 0.0376 | 21.05 | 400 | 1.3158 | {'precision': 0.8395904436860068, 'recall': 0.9033047735618115, 'f1': 0.8702830188679245, 'number': 817} | {'precision': 0.4785714285714286, 'recall': 0.5630252100840336, 'f1': 0.5173745173745175, 'number': 119} | {'precision': 0.8887814313346228, 'recall': 0.8532961931290622, 'f1': 0.8706774040738986, 'number': 1077} | 0.8397 | 0.8564 | 0.8480 | 0.7934 | | 0.0119 | 31.58 | 600 | 1.4791 | {'precision': 0.8752941176470588, 'recall': 0.9106487148102815, 'f1': 0.8926214757048591, 'number': 817} | {'precision': 0.5401459854014599, 'recall': 0.6218487394957983, 'f1': 0.578125, 'number': 119} | {'precision': 0.8818681318681318, 'recall': 0.8941504178272981, 'f1': 0.8879668049792531, 'number': 1077} | 0.8567 | 0.8847 | 0.8705 | 0.7961 | | 0.0061 | 42.11 | 800 | 1.5605 | {'precision': 0.8617886178861789, 'recall': 0.9082007343941249, 'f1': 0.8843861740166865, 'number': 817} | {'precision': 0.5963302752293578, 'recall': 0.5462184873949579, 'f1': 0.5701754385964912, 'number': 119} | {'precision': 0.8747763864042933, 'recall': 0.9080779944289693, 'f1': 0.8911161731207289, 'number': 1077} | 0.8549 | 0.8867 | 0.8705 | 0.7965 | | 0.0026 | 52.63 | 1000 | 1.5172 | {'precision': 0.8596491228070176, 'recall': 0.8996328029375765, 'f1': 0.8791866028708135, 'number': 817} | {'precision': 0.7176470588235294, 'recall': 0.5126050420168067, 'f1': 0.5980392156862744, 'number': 119} | {'precision': 0.8737864077669902, 'recall': 0.9192200557103064, 'f1': 0.8959276018099548, 'number': 1077} | 0.8616 | 0.8872 | 0.8742 | 0.8014 | | 0.0019 | 63.16 | 1200 | 1.6132 | {'precision': 0.8735224586288416, 'recall': 0.9045287637698899, 'f1': 0.888755261575466, 'number': 817} | {'precision': 0.6460176991150443, 'recall': 0.6134453781512605, 'f1': 0.6293103448275863, 'number': 119} | {'precision': 0.881508078994614, 'recall': 0.9117920148560817, 'f1': 0.8963943404837974, 'number': 1077} | 0.8654 | 0.8912 | 0.8781 | 0.8040 | | 0.0012 | 73.68 | 1400 | 1.6459 | {'precision': 0.8831942789034565, 'recall': 0.9069767441860465, 'f1': 0.894927536231884, 'number': 817} | {'precision': 0.6213592233009708, 'recall': 0.5378151260504201, 'f1': 0.5765765765765765, 'number': 119} | {'precision': 0.8998178506375227, 'recall': 0.9173630454967502, 'f1': 0.9085057471264367, 'number': 1077} | 0.8789 | 0.8907 | 0.8848 | 0.8068 | | 0.0005 | 84.21 | 1600 | 1.5619 | {'precision': 0.8602771362586605, 'recall': 0.9118727050183598, 'f1': 0.8853238265002972, 'number': 817} | {'precision': 0.6631578947368421, 'recall': 0.5294117647058824, 'f1': 0.5887850467289719, 'number': 119} | {'precision': 0.8944494995450409, 'recall': 0.9127205199628597, 'f1': 0.9034926470588234, 'number': 1077} | 0.8694 | 0.8897 | 0.8795 | 0.8155 | | 0.0003 | 94.74 | 1800 | 1.6571 | {'precision': 0.8649592549476135, 'recall': 0.9094247246022031, 'f1': 0.886634844868735, 'number': 817} | {'precision': 0.6391752577319587, 'recall': 0.5210084033613446, 'f1': 0.5740740740740741, 'number': 119} | {'precision': 0.8971792538671519, 'recall': 0.9155060352831941, 'f1': 0.90625, 'number': 1077} | 0.8715 | 0.8897 | 0.8805 | 0.8098 | | 0.0003 | 105.26 | 2000 | 1.6731 | {'precision': 0.8672875436554133, 'recall': 0.9118727050183598, 'f1': 0.8890214797136038, 'number': 817} | {'precision': 0.62, 'recall': 0.5210084033613446, 'f1': 0.5662100456621004, 'number': 119} | {'precision': 0.9008264462809917, 'recall': 0.9108635097493036, 'f1': 0.9058171745152355, 'number': 1077} | 0.8730 | 0.8882 | 0.8806 | 0.8071 |
5094eb04f917311f9b84548db59e1485
apache-2.0
['generated_from_trainer']
false
emotion_model 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: 1.3046 - Accuracy: 0.7938
ea27567eb173e3ca49130e9edb1d9ae8
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 204 | 1.1915 | 0.7854 | | No log | 2.0 | 408 | 1.1624 | 0.7889 | | 0.0451 | 3.0 | 612 | 1.1865 | 0.7952 | | 0.0451 | 4.0 | 816 | 1.2653 | 0.7945 | | 0.0154 | 5.0 | 1020 | 1.3046 | 0.7938 |
a0881ffa822fa1538b6c2ad983aeb351
apache-2.0
['hf-asr-leaderboard', 'generated_from_trainer']
false
Whisper Tiny Dutch 25 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7024 - Wer: 42.0655
aa6152a931d89098714c4b2bcc1a4fab
apache-2.0
['hf-asr-leaderboard', '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: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP
251376bf1a95e440355390283b704a2a
apache-2.0
['hf-asr-leaderboard', 'generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5563 | 0.78 | 500 | 0.7838 | 47.5002 | | 0.3949 | 1.56 | 1000 | 0.7301 | 43.9570 | | 0.2666 | 2.34 | 1500 | 0.7103 | 42.8426 | | 0.2307 | 3.12 | 2000 | 0.7024 | 42.0655 |
594acc58562c873499991f811ad97424
mit
[]
false
Scarlet witch on Stable Diffusion This is the `<sw-mom>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<sw-mom> 0](https://huggingface.co/sd-concepts-library/scarlet-witch/resolve/main/concept_images/0.jpeg) ![<sw-mom> 1](https://huggingface.co/sd-concepts-library/scarlet-witch/resolve/main/concept_images/1.jpeg) ![<sw-mom> 2](https://huggingface.co/sd-concepts-library/scarlet-witch/resolve/main/concept_images/3.jpeg) ![<sw-mom> 3](https://huggingface.co/sd-concepts-library/scarlet-witch/resolve/main/concept_images/2.jpeg)
4aaf24a84ab2392b7de0c20bdb19656f
mit
['generated_from_keras_callback']
false
ishaankul67/Adult_contemporary_music-clustered This model is a fine-tuned version of [nandysoham16/15-clustered_aug](https://huggingface.co/nandysoham16/15-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3734 - Train End Logits Accuracy: 0.9167 - Train Start Logits Accuracy: 0.8889 - Validation Loss: 0.1582 - Validation End Logits Accuracy: 0.8571 - Validation Start Logits Accuracy: 1.0 - Epoch: 0
2b764caf38c93c0c0f7c4f40726e5038
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.3734 | 0.9167 | 0.8889 | 0.1582 | 0.8571 | 1.0 | 0 |
21a9f463a3071f394c413a459b8ea884
other
['vision', 'image-segmentation', 'generated_from_trainer']
false
segformer-b5-finetuned-magic-cards-230117-3 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the andrewljohnson/magic_cards dataset. It achieves the following results on the evaluation set: - Loss: 0.0691 - Mean Iou: 0.6585 - Mean Accuracy: 0.9878 - Overall Accuracy: 0.9912 - Accuracy Unlabeled: nan - Accuracy Front: 0.9978 - Accuracy Back: 0.9777 - Iou Unlabeled: 0.0 - Iou Front: 0.9978 - Iou Back: 0.9777
96452cb86ddcf69391eecf3889fac826
other
['vision', 'image-segmentation', 'generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5
3c9d353a27dce4a40891a43b657e8585
other
['vision', 'image-segmentation', 'generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Front | Accuracy Back | Iou Unlabeled | Iou Front | Iou Back | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:-------------:|:---------:|:--------:| | 1.2232 | 0.37 | 20 | 0.4691 | 0.6041 | 0.9201 | 0.9218 | nan | 0.9252 | 0.9150 | 0.0 | 0.9252 | 0.8870 | | 0.2718 | 0.74 | 40 | 0.1983 | 0.6509 | 0.9764 | 0.9785 | nan | 0.9826 | 0.9702 | 0.0 | 0.9826 | 0.9702 | | 0.255 | 1.11 | 60 | 0.0939 | 0.6524 | 0.9785 | 0.9794 | nan | 0.9812 | 0.9758 | 0.0 | 0.9812 | 0.9758 | | 0.1103 | 1.48 | 80 | 0.0682 | 0.6536 | 0.9804 | 0.9813 | nan | 0.9830 | 0.9779 | 0.0 | 0.9830 | 0.9779 | | 0.1373 | 1.85 | 100 | 0.1260 | 0.6631 | 0.9946 | 0.9961 | nan | 0.9989 | 0.9903 | 0.0 | 0.9989 | 0.9903 | | 0.0566 | 2.22 | 120 | 0.1558 | 0.6578 | 0.9868 | 0.9912 | nan | 0.9999 | 0.9736 | 0.0 | 0.9999 | 0.9736 | | 0.1535 | 2.59 | 140 | 0.1330 | 0.6558 | 0.9838 | 0.9883 | nan | 0.9973 | 0.9703 | 0.0 | 0.9973 | 0.9703 | | 0.0586 | 2.96 | 160 | 0.2317 | 0.6599 | 0.9899 | 0.9933 | nan | 1.0000 | 0.9798 | 0.0 | 1.0000 | 0.9798 | | 0.0727 | 3.33 | 180 | 0.1018 | 0.6586 | 0.9880 | 0.9919 | nan | 0.9995 | 0.9764 | 0.0 | 0.9995 | 0.9764 | | 0.3588 | 3.7 | 200 | 0.1151 | 0.6608 | 0.9912 | 0.9939 | nan | 0.9993 | 0.9831 | 0.0 | 0.9993 | 0.9831 | | 0.0463 | 4.07 | 220 | 0.0538 | 0.6610 | 0.9915 | 0.9934 | nan | 0.9969 | 0.9862 | 0.0 | 0.9969 | 0.9862 | | 0.046 | 4.44 | 240 | 0.1201 | 0.6581 | 0.9871 | 0.9912 | nan | 0.9991 | 0.9751 | 0.0 | 0.9991 | 0.9751 | | 0.0468 | 4.81 | 260 | 0.0691 | 0.6585 | 0.9878 | 0.9912 | nan | 0.9978 | 0.9777 | 0.0 | 0.9978 | 0.9777 |
4edd947498d850f8f211716844fff070
apache-2.0
['whisper-event', 'generated_from_trainer']
false
Whisper small Greek Farsioal and El Greco This model is a fine-tuned version of [emilios/whisper-sm-el-farsipal-e4](https://huggingface.co/emilios/whisper-sm-el-farsipal-e4) on the mozilla-foundation/common_voice_11_0,google/fleurs el,el_gr dataset. It achieves the following results on the evaluation set: - Loss: 0.4871 - Wer: 17.1991
452852ac65f4fc3c96646b80d6ef71aa
apache-2.0
['whisper-event', 'generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 20000
d83f8bee6013d8aa9059bf53947edf3c
apache-2.0
['whisper-event', 'generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.1259 | 2.49 | 1000 | 0.4834 | 18.3692 | | 0.1002 | 4.49 | 2000 | 0.4604 | 17.8027 | | 0.1096 | 6.98 | 3000 | 0.4553 | 17.8770 | | 0.0885 | 9.46 | 4000 | 0.4551 | 17.9606 | | 0.0675 | 11.95 | 5000 | 0.4631 | 17.9049 | | 0.0675 | 14.44 | 6000 | 0.4619 | 17.9049 | | 0.0645 | 16.93 | 7000 | 0.4678 | 17.6727 | | 0.0535 | 19.41 | 8000 | 0.4685 | 17.6634 | | 0.039 | 21.49 | 9000 | 0.4746 | 17.6727 | | 0.0447 | 23.98 | 10000 | 0.4761 | 17.6634 | | 0.0393 | 26.46 | 11000 | 0.4792 | 17.7656 | | 0.0308 | 28.95 | 12000 | 0.4851 | 17.8678 | | 0.0301 | 31.44 | 13000 | 0.4846 | 17.4499 | | 0.031 | 33.93 | 14000 | 0.4849 | 17.8306 | | 0.0263 | 36.41 | 15000 | 0.4880 | 17.6170 | | 0.0256 | 38.9 | 16000 | 0.4871 | 17.1991 | | 0.0236 | 41.39 | 17000 | 0.4883 | 17.2641 | | 0.0195 | 43.88 | 18000 | 0.4880 | 17.5706 | | 0.0193 | 46.36 | 19000 | 0.4993 | 17.7285 | | 0.0161 | 48.85 | 20000 | 0.4968 | 17.8306 |
ac13baa07676d36caa0b795ecb7b4076
mit
['generated_from_keras_callback']
false
gabrielgmendonca/bert-base-portuguese-cased-finetuned-chico-xavier-finetuned-chico-xavier This model is a fine-tuned version of [gabrielgmendonca/bert-base-portuguese-cased-finetuned-chico-xavier](https://huggingface.co/gabrielgmendonca/bert-base-portuguese-cased-finetuned-chico-xavier) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.8630 - Validation Loss: 1.7215 - Epoch: 0
d9eda5be786466bcec4abe5841c53f81
mit
['generated_from_keras_callback']
false
Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3430, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32
577b76222f626d969eabcd4b546907ab
apache-2.0
['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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100
1fd96be01830cf9d3aeeaa25fac40330
apache-2.0
['generated_from_trainer']
false
wav2vec2-base-finetuned-sentiment-mesd-v9 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: 0.3500 - Accuracy: 0.9154
6b184887fd009b0c477a41dbd189274b
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 40 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100
b7b938f3e9d8e71e0c197944af08ce57
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.86 | 3 | 1.7825 | 0.1846 | | 1.9553 | 1.86 | 6 | 1.7212 | 0.4308 | | 1.9553 | 2.86 | 9 | 1.6164 | 0.3769 | | 2.002 | 3.86 | 12 | 1.4904 | 0.3769 | | 1.6191 | 4.86 | 15 | 1.4426 | 0.4385 | | 1.6191 | 5.86 | 18 | 1.3516 | 0.5231 | | 1.6209 | 6.86 | 21 | 1.2176 | 0.5538 | | 1.6209 | 7.86 | 24 | 1.1683 | 0.5692 | | 1.371 | 8.86 | 27 | 1.0885 | 0.5923 | | 1.1568 | 9.86 | 30 | 1.0152 | 0.6385 | | 1.1568 | 10.86 | 33 | 0.9289 | 0.6385 | | 1.1023 | 11.86 | 36 | 0.9141 | 0.6308 | | 1.1023 | 12.86 | 39 | 0.8526 | 0.6462 | | 0.9448 | 13.86 | 42 | 0.8420 | 0.6769 | | 0.7972 | 14.86 | 45 | 0.7976 | 0.6692 | | 0.7972 | 15.86 | 48 | 0.8192 | 0.7308 | | 0.7793 | 16.86 | 51 | 0.7108 | 0.7615 | | 0.7793 | 17.86 | 54 | 0.6712 | 0.7769 | | 0.6468 | 18.86 | 57 | 0.6684 | 0.7923 | | 0.5083 | 19.86 | 60 | 0.6922 | 0.7385 | | 0.5083 | 20.86 | 63 | 0.6148 | 0.7923 | | 0.4988 | 21.86 | 66 | 0.5846 | 0.7923 | | 0.4988 | 22.86 | 69 | 0.6050 | 0.8154 | | 0.4123 | 23.86 | 72 | 0.5506 | 0.7846 | | 0.3511 | 24.86 | 75 | 0.6095 | 0.7846 | | 0.3511 | 25.86 | 78 | 0.5916 | 0.8154 | | 0.3268 | 26.86 | 81 | 0.5912 | 0.8077 | | 0.3268 | 27.86 | 84 | 0.5142 | 0.8538 | | 0.3036 | 28.86 | 87 | 0.5492 | 0.8077 | | 0.3066 | 29.86 | 90 | 0.6007 | 0.8231 | | 0.3066 | 30.86 | 93 | 0.5748 | 0.8231 | | 0.2538 | 31.86 | 96 | 0.6027 | 0.7692 | | 0.2538 | 32.86 | 99 | 0.6979 | 0.7462 | | 0.2281 | 33.86 | 102 | 0.7002 | 0.7615 | | 0.2183 | 34.86 | 105 | 0.6650 | 0.7769 | | 0.2183 | 35.86 | 108 | 0.5192 | 0.8462 | | 0.2202 | 36.86 | 111 | 0.5389 | 0.8308 | | 0.2202 | 37.86 | 114 | 0.5050 | 0.8385 | | 0.1906 | 38.86 | 117 | 0.5722 | 0.7769 | | 0.154 | 39.86 | 120 | 0.5239 | 0.8308 | | 0.154 | 40.86 | 123 | 0.4448 | 0.8615 | | 0.1474 | 41.86 | 126 | 0.4623 | 0.8615 | | 0.1474 | 42.86 | 129 | 0.4282 | 0.8615 | | 0.1345 | 43.86 | 132 | 0.5087 | 0.8615 | | 0.1567 | 44.86 | 135 | 0.4859 | 0.8385 | | 0.1567 | 45.86 | 138 | 0.6603 | 0.8077 | | 0.1731 | 46.86 | 141 | 0.5379 | 0.8385 | | 0.1731 | 47.86 | 144 | 0.8666 | 0.7538 | | 0.1606 | 48.86 | 147 | 0.7518 | 0.8 | | 0.1484 | 49.86 | 150 | 0.5986 | 0.8385 | | 0.1484 | 50.86 | 153 | 0.6368 | 0.8231 | | 0.2256 | 51.86 | 156 | 0.4639 | 0.8692 | | 0.2256 | 52.86 | 159 | 0.5533 | 0.8462 | | 0.1178 | 53.86 | 162 | 0.5038 | 0.8615 | | 0.0815 | 54.86 | 165 | 0.5052 | 0.8692 | | 0.0815 | 55.86 | 168 | 0.4337 | 0.8846 | | 0.0998 | 56.86 | 171 | 0.4422 | 0.8769 | | 0.0998 | 57.86 | 174 | 0.4317 | 0.8692 | | 0.0855 | 58.86 | 177 | 0.4025 | 0.8923 | | 0.0962 | 59.86 | 180 | 0.4605 | 0.8769 | | 0.0962 | 60.86 | 183 | 0.4356 | 0.8769 | | 0.0763 | 61.86 | 186 | 0.4614 | 0.8769 | | 0.0763 | 62.86 | 189 | 0.4382 | 0.8846 | | 0.0902 | 63.86 | 192 | 0.4701 | 0.8692 | | 0.0654 | 64.86 | 195 | 0.4922 | 0.8692 | | 0.0654 | 65.86 | 198 | 0.5413 | 0.8538 | | 0.0651 | 66.86 | 201 | 0.5759 | 0.8615 | | 0.0651 | 67.86 | 204 | 0.4238 | 0.9 | | 0.0822 | 68.86 | 207 | 0.3500 | 0.9154 | | 0.0625 | 69.86 | 210 | 0.3878 | 0.8923 | | 0.0625 | 70.86 | 213 | 0.4952 | 0.8615 | | 0.0548 | 71.86 | 216 | 0.4544 | 0.8615 | | 0.0548 | 72.86 | 219 | 0.5497 | 0.8769 | | 0.054 | 73.86 | 222 | 0.4434 | 0.8846 | | 0.0543 | 74.86 | 225 | 0.4732 | 0.8769 | | 0.0543 | 75.86 | 228 | 0.4425 | 0.8923 | | 0.0881 | 76.86 | 231 | 0.4788 | 0.8769 | | 0.0881 | 77.86 | 234 | 0.5448 | 0.8769 | | 0.061 | 78.86 | 237 | 0.4221 | 0.9077 | | 0.0567 | 79.86 | 240 | 0.4404 | 0.8769 | | 0.0567 | 80.86 | 243 | 0.4099 | 0.9 | | 0.052 | 81.86 | 246 | 0.5259 | 0.8769 | | 0.052 | 82.86 | 249 | 0.5874 | 0.8692 | | 0.0444 | 83.86 | 252 | 0.5555 | 0.8846 | | 0.0332 | 84.86 | 255 | 0.5156 | 0.8615 | | 0.0332 | 85.86 | 258 | 0.4564 | 0.8615 | | 0.0449 | 86.86 | 261 | 0.4826 | 0.8692 | | 0.0449 | 87.86 | 264 | 0.4726 | 0.8615 | | 0.0385 | 88.86 | 267 | 0.4206 | 0.8846 | | 0.0356 | 89.86 | 270 | 0.4050 | 0.8769 | | 0.0356 | 90.86 | 273 | 0.4161 | 0.8923 | | 0.0391 | 91.86 | 276 | 0.4100 | 0.9077 | | 0.0391 | 92.86 | 279 | 0.4047 | 0.9 | | 0.0249 | 93.86 | 282 | 0.4044 | 0.9 | | 0.0399 | 94.86 | 285 | 0.3968 | 0.8846 | | 0.0399 | 95.86 | 288 | 0.3802 | 0.9 | | 0.031 | 96.86 | 291 | 0.3689 | 0.9 | | 0.031 | 97.86 | 294 | 0.3616 | 0.9077 | | 0.036 | 98.86 | 297 | 0.3584 | 0.9077 | | 0.0386 | 99.86 | 300 | 0.3574 | 0.9077 |
dd6c169f678da6333623d69cf878ff48
mit
['generated_from_trainer']
false
pensive_keller This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
2cbbccbe4917af9c98c404573ad17fe8
mit
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 3125 - mixed_precision_training: Native AMP
ddce5c01d343d81a7cfb75623ed771e3
mit
['generated_from_trainer']
false
Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True, 'skip_tokens': 1661599744}, 'generation': {'every_n_steps': 32, 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'every_n_steps': 32, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': '81a1701e025d2c65ae6e8c2103df559071523ee0', 'value_head_config': {'is_detached': False}}, 'path_or_name': 'tomekkorbak/goofy_pasteur'}, 'objective': {'alpha': 0.5, 'beta': 10, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 512, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'pensive_keller', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.001, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 3346, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1661599744, 'warmup_ratio': 0.01, 'weight_decay': 0.1}}
fa590a95b8493c7098cc88a8457d62dd
apache-2.0
['generated_from_trainer']
false
recipe-lr0.0001-wd0.02-bs64 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: 0.2792 - Rmse: 0.5284 - Mse: 0.2792 - Mae: 0.4268
9bf4d1aa0f945269be3416cdf730c6ab
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2799 | 1.0 | 623 | 0.2789 | 0.5281 | 0.2789 | 0.4218 | | 0.2786 | 2.0 | 1246 | 0.2792 | 0.5284 | 0.2792 | 0.4268 | | 0.2785 | 3.0 | 1869 | 0.2792 | 0.5284 | 0.2792 | 0.4268 |
809a1f1ab66549caa0a0c3696cbab8c0
cc-by-4.0
[]
false
Icelandic ConvBERT-Small This model was pretrained on the [Icelandic Gigaword Corpus](http://igc.arnastofnun.is/), which contains approximately 1.69B tokens, using default settings. The model uses a Unigram tokenizer with a vocabulary size of 96,000.
2e7880813c614d2be092af828eb91b09
cc-by-4.0
[]
false
Acknowledgments This research was supported with Cloud TPUs from Google's TPU Research Cloud (TRC). This project was funded by the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by [Almannarómur](https://almannaromur.is/), is funded by the Icelandic Ministry of Education, Science and Culture.
92bec0a336745def8748a1b0fbe08354
apache-2.0
['exbert', 'multiberts', 'multiberts-seed-4']
false
MultiBERTs Seed 4 Checkpoint 900k (uncased) Seed 4 intermediate checkpoint 900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-4](https://hf.co/multberts-seed-4). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani).
ac0dc310d2257bcf8c980195602ac029
apache-2.0
['exbert', 'multiberts', 'multiberts-seed-4']
false
How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-4-900k') model = BertModel.from_pretrained("multiberts-seed-4-900k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ```
a16871183eb62d4443c4357942057779
apache-2.0
['generated_from_trainer']
false
distilbert-base-uncased-mlm-ta-local This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0658
01ac82ff7d3947facffc4082f63ef107
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP
93d4bb83d0671dce815fb78e0f782a39
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4431 | 1.0 | 3125 | 2.1817 | | 2.2197 | 2.0 | 6250 | 2.0929 | | 2.1519 | 3.0 | 9375 | 2.0696 |
ef93f4db836607bedf3e6332c55ed4f7
mit
['conversational']
false
Large-Scale Pre-Training for Goal-Directed Dialog (GODEL) GODEL is a large-scale pre-trained model for goal-directed dialogs. It is parameterized with a Transformer-based encoder-decoder model and trained for response generation grounded in external text, which allows more effective fine-tuning on dialog tasks that require conditioning the response on information that is external to the current conversation (e.g., a retrieved document). The pre-trained model can be efficiently fine-tuned and adapted to accomplish a new dialog task with a handful of task-specific dialogs. The v1.1 model is trained on 551M multi-turn dialogs from Reddit discussion thread, and 5M instruction and knowledge grounded dialogs.
481b4329f0bc765236e29cab23244ef9
mit
['conversational']
false
Multi-turn generation examples from an interactive environment: Chitchat example: > Instruction: given a dialog context, you need to response empathically. <br> > User: Does money buy happiness? <br> > Agent: It is a question. Money buys you a lot of things, but not enough to buy happiness. <br> > User: What is the best way to buy happiness ? <br> > Agent: Happiness is bought through your experience and not money. <br> Grounded response generation example: > Instruction: given a dialog context and related knowledge, you need to response safely based on the knowledge. <br> > Knowledge: The best Stardew Valley mods PCGamesN_0 / About SMAPI <br> > User: My favorite game is stardew valley. stardew valley is very fun. <br> > Agent: I love Stardew Valley mods, like PCGamesN_0 / About SMAPI. <br> Please find the information about preprocessing, training and full details of the GODEL in the [project webpage](https://aka.ms/GODEL). ArXiv paper: [https://arxiv.org/abs/2206.11309](https://arxiv.org/abs/2206.11309)
c1470cc8983204b91f225ce3d613db5f
mit
['conversational']
false
How to use Now we are ready to try out how the model works as a chatting partner! ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq") model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq") def generate(instruction, knowledge, dialog): if knowledge != '': knowledge = '[KNOWLEDGE] ' + knowledge dialog = ' EOS '.join(dialog) query = f"{instruction} [CONTEXT] {dialog} {knowledge}" input_ids = tokenizer(f"{query}", return_tensors="pt").input_ids outputs = model.generate(input_ids, max_length=128, min_length=8, top_p=0.9, do_sample=True) output = tokenizer.decode(outputs[0], skip_special_tokens=True) return output
abb73a82eb04f91e9d85d9e9d29dc731
mit
['conversational']
false
Leave the knowldge empty knowledge = '' dialog = [ 'Does money buy happiness?', 'It is a question. Money buys you a lot of things, but not enough to buy happiness.', 'What is the best way to buy happiness ?' ] response = generate(instruction, knowledge, dialog) print(response) ```
63ba9a652644738636328e5ff203dea1
mit
['conversational']
false
Citation if you use this code and data in your research, please cite our arxiv paper: ``` @misc{peng2022godel, author = {Peng, Baolin and Galley, Michel and He, Pengcheng and Brockett, Chris and Liden, Lars and Nouri, Elnaz and Yu, Zhou and Dolan, Bill and Gao, Jianfeng}, title = {GODEL: Large-Scale Pre-training for Goal-Directed Dialog}, howpublished = {arXiv}, year = {2022}, month = {June}, url = {https://www.microsoft.com/en-us/research/publication/godel-large-scale-pre-training-for-goal-directed-dialog/}, } ```
f225237e74c10a03bc6da01849cb6bf9
apache-2.0
['automatic-speech-recognition', 'de']
false
exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s756 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.
2cce04006c21f9a4ae341ef1d7655c23
apache-2.0
['generated_from_trainer']
false
t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol-finetuned-nl-to-fol This model is a fine-tuned version of [anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol](https://huggingface.co/anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1468 - Bleu: 30.3266 - Gen Len: 18.8824
f786389f90cbac8d3b69a05a8a8e91c5
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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: 10 - mixed_precision_training: Native AMP
c025b79cf38819a00d9848346485abde
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 17 | 0.1486 | 30.3537 | 18.8824 | | No log | 2.0 | 34 | 0.1474 | 30.2522 | 18.8824 | | No log | 3.0 | 51 | 0.1465 | 30.2522 | 18.8824 | | No log | 4.0 | 68 | 0.1461 | 30.2522 | 18.8824 | | No log | 5.0 | 85 | 0.1469 | 30.2522 | 18.8824 | | No log | 6.0 | 102 | 0.1457 | 29.8889 | 18.8824 | | No log | 7.0 | 119 | 0.1470 | 30.3537 | 18.8824 | | No log | 8.0 | 136 | 0.1469 | 30.3537 | 18.8824 | | No log | 9.0 | 153 | 0.1469 | 30.3266 | 18.8824 | | No log | 10.0 | 170 | 0.1468 | 30.3266 | 18.8824 |
3a3891d87cae62def5b59001c04cd419
apache-2.0
['automatic-speech-recognition', 'ar']
false
exp_w2v2t_ar_hubert_s947 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (ar)](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.
7a4f5282894731fc3360a2b7b976f10e
wtfpl
[]
false
One of the first embeddings I have created, adds a horror atmosphere and monsters to an image. Download it into the embeddings folder and use it with "by HorrorByDave" or what ever you have renamed the embed. Samples (if hugging face keeps the png data then you can get the prompt by putting the sample into pnginfo): ![Sample 1](https://huggingface.co/GamingDaveUK/HorrorByDave/resolve/main/Sample%20\(1\).png) ![Sample 2](https://huggingface.co/GamingDaveUK/HorrorByDave/resolve/main/Sample%20\(2\).png) ![Sample 3](https://huggingface.co/GamingDaveUK/HorrorByDave/resolve/main/Sample%20\(3\).png) ![Sample 4](https://huggingface.co/GamingDaveUK/HorrorByDave/resolve/main/Sample%20\(4\).png) ![Sample 5](https://huggingface.co/GamingDaveUK/HorrorByDave/resolve/main/Sample%20\(5\).png) ![Sample 6](https://huggingface.co/GamingDaveUK/HorrorByDave/resolve/main/Sample%20\(6\).png) ![Sample 7](https://huggingface.co/GamingDaveUK/HorrorByDave/resolve/main/Sample%20\(7\).png) ![Sample 8](https://huggingface.co/GamingDaveUK/HorrorByDave/resolve/main/Sample%20\(8\).png) ![Sample 9](https://huggingface.co/GamingDaveUK/HorrorByDave/resolve/main/Sample%20\(9\).png) ![Sample 10](https://huggingface.co/GamingDaveUK/HorrorByDave/resolve/main/Sample%20\(10\).png)
1eb175922dcb5b690d018d078e1da487
apache-2.0
['translation']
false
opus-mt-fr-lu * source languages: fr * target languages: lu * OPUS readme: [fr-lu](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-lu/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-lu/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-lu/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-lu/opus-2020-01-20.eval.txt)
28f8f0b09a0c9107fe7d7ef3042b197c
mit
['generated_from_keras_callback']
false
fourth_iteration_model 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:
68119dff94bb27f668697589581995a3
mit
['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': 2e-05, 'decay_steps': 65805, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16
0c8341612af5708aa5515f1fee7bbe99
apache-2.0
['automatic-speech-recognition', 'es']
false
exp_w2v2r_es_vp-100k_gender_male-0_female-10_s33 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.
42da60087f04fbeb6dfe14406e8f2ec2
mit
['deberta', 'deberta-v3', 'mdeberta', 'korean', 'pretraining']
false
mDeBERTa-v3-base-kor-further > 💡 아래 프로젝트는 KPMG Lighthouse Korea에서 진행하였습니다. > KPMG Lighthouse Korea에서는, Financial area의 다양한 문제들을 해결하기 위해 Edge Technology의 NLP/Vision AI를 모델링하고 있습니다. > https://kpmgkr.notion.site/
4b84726e5bfda6263c186446adf299e3
mit
['deberta', 'deberta-v3', 'mdeberta', 'korean', 'pretraining']
false
What is DeBERTa? - [DeBERTa](https://arxiv.org/abs/2006.03654)는 `Disentangled Attention` + `Enhanced Mask Decoder` 를 적용하여 단어의 positional information을 효과적으로 학습합니다. 이와 같은 아이디어를 통해, 기존의 BERT, RoBERTa에서 사용했던 absolute position embedding과는 달리 DeBERTa는 단어의 상대적인 위치 정보를 학습 가능한 벡터로 표현하여 모델을 학습하게 됩니다. 결과적으로, BERT, RoBERTA 와 비교했을 때 더 준수한 성능을 보여주었습니다. - [DeBERTa-v3](https://arxiv.org/abs/2111.09543)에서는, 이전 버전에서 사용했던 MLM (Masked Language Model) 을 RTD (Replaced Token Detection) Task 로 대체한 ELECTRA 스타일의 사전학습 방법과, Gradient-Disentangled Embedding Sharing 을 적용하여 모델 학습의 효율성을 개선하였습니다. - DeBERTa의 아키텍처로 풍부한 한국어 데이터를 학습하기 위해서, `mDeBERTa-v3-base-kor-further` 는 microsoft 가 발표한 `mDeBERTa-v3-base` 를 약 40GB의 한국어 데이터에 대해서 **추가적인 사전학습**을 진행한 언어 모델입니다.
f6d5de219ba133a62a25b47d3225d202
mit
['deberta', 'deberta-v3', 'mdeberta', 'korean', 'pretraining']
false
How to Use - Requirements ``` pip install transformers pip install sentencepiece ``` - Huggingface Hub ```python from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained("mdeberta-v3-base-kor-further")
7b477e1b17a7bbfab0144f6ab2c76103
mit
['deberta', 'deberta-v3', 'mdeberta', 'korean', 'pretraining']
false
Pre-trained Models - 모델의 아키텍처는 기존 microsoft에서 발표한 `mdeberta-v3-base`와 동일한 구조입니다. | | Vocabulary(K) | Backbone Parameters(M) | Hidden Size | Layers | Note | | --- | --- | --- | --- | --- | --- | | mdeberta-v3-base-kor-further (mdeberta-v3-base와 동일) | 250 | 86 | 768 | 12 | 250K new SPM vocab |
6bda553b80ba340fde88e68a036bbcb8
mit
['deberta', 'deberta-v3', 'mdeberta', 'korean', 'pretraining']
false
Further Pretraing Details (MLM Task) - `mDeBERTa-v3-base-kor-further` 는 `microsoft/mDeBERTa-v3-base` 를 약 40GB의 한국어 데이터에 대해서 MLM Task를 적용하여 추가적인 사전 학습을 진행하였습니다. | | Max length | Learning Rate | Batch Size | Train Steps | Warm-up Steps | | --- | --- | --- | --- | --- | --- | | mdeberta-v3-base-kor-further | 512 | 2e-5 | 8 | 5M | 50k |
b673c956f23d37e95a4559269199c15c
mit
['deberta', 'deberta-v3', 'mdeberta', 'korean', 'pretraining']
false
Datasets - 모두의 말뭉치(신문, 구어, 문어), 한국어 Wiki, 국민청원 등 약 40 GB 의 한국어 데이터셋이 추가적인 사전학습에 사용되었습니다. - Train: 10M lines, 5B tokens - Valid: 2M lines, 1B tokens - cf) 기존 mDeBERTa-v3은 XLM-R 과 같이 [cc-100 데이터셋](https://data.statmt.org/cc-100/)으로 학습되었으며, 그 중 한국어 데이터셋의 크기는 54GB입니다.
ec74969142b8858c7d80a64a30853d72
mit
['deberta', 'deberta-v3', 'mdeberta', 'korean', 'pretraining']
false
Fine-tuning on NLU Tasks - Base Model | Model | Size | NSMC(acc) | Naver NER(F1) | PAWS (acc) | KorNLI (acc) | KorSTS (spearman) | Question Pair (acc) | KorQuaD (Dev) (EM/F1) | Korean-Hate-Speech (Dev) (F1) | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | XLM-Roberta-Base | 1.03G | 89.03 | 86.65 | 82.80 | 80.23 | 78.45 | 93.80 | 64.70 / 88.94 | 64.06 | | mdeberta-base | 534M | 90.01 | 87.43 | 85.55 | 80.41 | **82.65** | 94.06 | 65.48 / 89.74 | 62.91 | | mdeberta-base-kor-further (Ours) | 534M | **90.52** | **87.87** | **85.85** | **80.65** | 81.90 | **94.98** | **66.07 / 90.35** | **68.16** |
ad652f7db4fc5791019e546ca03c1de6
mit
['deberta', 'deberta-v3', 'mdeberta', 'korean', 'pretraining']
false
Citation ``` @misc{he2021debertav3, title={DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing}, author={Pengcheng He and Jianfeng Gao and Weizhu Chen}, year={2021}, eprint={2111.09543}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @inproceedings{ he2021deberta, title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION}, author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen}, booktitle={International Conference on Learning Representations}, year={2021}, url={https://openreview.net/forum?id=XPZIaotutsD} } ```
df7355e08c2de5d3d5c364baa471a8dd
mit
['deberta', 'deberta-v3', 'mdeberta', 'korean', 'pretraining']
false
Reference - [mDeBERTa-v3-base-kor-further](https://github.com/kpmg-kr/mDeBERTa-v3-base-kor-further) - [DeBERTa](https://github.com/microsoft/DeBERTa) - [Huggingface Transformers](https://github.com/huggingface/transformers) - [모두의 말뭉치](https://corpus.korean.go.kr/) - [Korpora: Korean Corpora Archives](https://github.com/ko-nlp/Korpora) - [sooftware/Korean PLM](https://github.com/sooftware/Korean-PLM)
4207db3ee78f311fc5df5cbeb62cbacc
mit
['roberta-base', 'roberta-base-epoch_24']
false
RoBERTa, Intermediate Checkpoint - Epoch 24 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly initialized weights before the training) to provide the ability to study the training dynamics of such models, and other possible use-cases. These models were trained in part of a work that studies how simple statistics from data, such as co-occurrences affects model predictions, which are described in the paper [Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions](https://arxiv.org/abs/2207.14251). This is RoBERTa-base epoch_24.
dc16b552fa7f3aae6d4a9275a7eb48ab
mit
['generated_from_trainer']
false
deberta-v3-large__sst2__train-8-8 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7414 - Accuracy: 0.5623
9749b3bb405faa4207ea5963c6fbf441
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6597 | 1.0 | 3 | 0.7716 | 0.25 | | 0.6376 | 2.0 | 6 | 0.7802 | 0.25 | | 0.5857 | 3.0 | 9 | 0.6625 | 0.75 | | 0.4024 | 4.0 | 12 | 0.5195 | 0.75 | | 0.2635 | 5.0 | 15 | 0.4222 | 1.0 | | 0.1714 | 6.0 | 18 | 0.4410 | 0.5 | | 0.1267 | 7.0 | 21 | 0.7773 | 0.75 | | 0.0582 | 8.0 | 24 | 0.9070 | 0.75 | | 0.0374 | 9.0 | 27 | 0.9539 | 0.75 | | 0.0204 | 10.0 | 30 | 1.0507 | 0.75 | | 0.012 | 11.0 | 33 | 1.2802 | 0.5 | | 0.0086 | 12.0 | 36 | 1.4272 | 0.5 | | 0.0049 | 13.0 | 39 | 1.4803 | 0.5 | | 0.0039 | 14.0 | 42 | 1.4912 | 0.5 | | 0.0031 | 15.0 | 45 | 1.5231 | 0.5 |
10de571333d586b7530cf4f2642e758f
apache-2.0
['squad']
false
Training data Fine-tuning was done based on the pre-trained model [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased). Training and dev datasets are our [Swedish translation of SQuAD v2](https://github.com/susumu2357/SQuAD_v2_sv). [Here](https://huggingface.co/datasets/susumu2357/squad_v2_sv) is the HuggingFace Datasets.
25c96bec876d8eb42f1c080ca9388f8a
apache-2.0
['squad']
false
Eval results ``` 'exact': 66.72642524202223 'f1': 70.11149581003404 'total': 11156 'HasAns_exact': 55.574745730186144 'HasAns_f1': 62.821693965983044 'HasAns_total': 5211 'NoAns_exact': 76.50126156433979 'NoAns_f1': 76.50126156433979 'NoAns_total': 5945 ```
111ae686a95cdb17deedd22bc10802d6
apache-2.0
['squad']
false
BibTeX entry and citation info ```bibtex @misc{svSQuADbert, author = {Susumu Okazawa}, title = {Swedish BERT Fine-tuned on Swedish SQuAD 2.0}, year = {2021}, howpublished = {\url{https://huggingface.co/susumu2357/bert-base-swedish-squad2}}, } ```
7ae625f1e593ec4d2b8695414aec825c
apache-2.0
['generated_from_trainer']
false
SentimentClassifier This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the amazon_polarity dataset. It achieves the following results on the evaluation set: - Loss: 0.4425 - Accuracy: 0.91 - F1: 0.91
2e5058a4b1e02158bfdce0bbecaada94
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5
8bac1586dcc2684c3e9324995d9bd0a1
apache-2.0
['generated_from_trainer']
false
librispeech-100h-supervised-meta This model is a fine-tuned version of [Kuray107/librispeech-5h-supervised](https://huggingface.co/Kuray107/librispeech-5h-supervised) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0965 - Wer: 0.0330
609d35e33bb87138d91124748ec9b09c
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 - mixed_precision_training: Native AMP
84f7614f240b8e011d149d84cb5798ae
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.1131 | 1.12 | 1000 | 0.0755 | 0.0487 | | 0.0725 | 2.24 | 2000 | 0.0637 | 0.0404 | | 0.0539 | 3.36 | 3000 | 0.0661 | 0.0389 | | 0.0441 | 4.48 | 4000 | 0.0637 | 0.0371 | | 0.0379 | 5.61 | 5000 | 0.0675 | 0.0356 | | 0.0341 | 6.73 | 6000 | 0.0735 | 0.0360 | | 0.0295 | 7.85 | 7000 | 0.0737 | 0.0362 | | 0.0265 | 8.97 | 8000 | 0.0741 | 0.0350 | | 0.0244 | 10.09 | 9000 | 0.0779 | 0.0337 | | 0.0217 | 11.21 | 10000 | 0.0835 | 0.0343 | | 0.0203 | 12.33 | 11000 | 0.0785 | 0.0339 | | 0.0188 | 13.45 | 12000 | 0.0827 | 0.0344 | | 0.0179 | 14.57 | 13000 | 0.0875 | 0.0332 | | 0.0169 | 15.7 | 14000 | 0.0860 | 0.0330 | | 0.0158 | 16.82 | 15000 | 0.0954 | 0.0330 | | 0.0147 | 17.94 | 16000 | 0.0934 | 0.0329 | | 0.0148 | 19.06 | 17000 | 0.0965 | 0.0330 |
b6fa6c20e69abe7c56fd9cc1e2541afe
apache-2.0
['generated_from_trainer']
false
tiny-mlm-glue-rte-custom-tokenizer This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.3646
9f470873844b964ebbf8aa9b8366e9dc
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.71 | 1.6 | 500 | 7.1503 | | 6.8618 | 3.21 | 1000 | 7.2787 | | 6.816 | 4.81 | 1500 | 7.2543 | | 6.7094 | 6.41 | 2000 | 7.3646 |
663a0cf88f7d73fa4554c22ac43ba65e
cc-by-4.0
['question generation']
false
Model Card of `lmqg/t5-large-subjqa-tripadvisor-qg` This model is fine-tuned version of [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: tripadvisor) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
d0d10ea906b29e61580adc1dcb958760
cc-by-4.0
['question generation']
false
Overview - **Language model:** [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (tripadvisor) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
7893a9a420358978028d6f964c5f5a59
cc-by-4.0
['question generation']
false
model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-large-subjqa-tripadvisor-qg") output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ```
5f1e9fbcbd9ecc9999b98ba3a96647dd
cc-by-4.0
['question generation']
false
Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-large-subjqa-tripadvisor-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.tripadvisor.json) | | Score | Type | Dataset | |:-----------|--------:|:------------|:-----------------------------------------------------------------| | BERTScore | 94.46 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_1 | 26.44 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_2 | 17.84 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_3 | 9.13 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_4 | 5.35 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | METEOR | 27.45 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | MoverScore | 67.76 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | ROUGE_L | 27.69 | tripadvisor | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) |
52ff50b560d4ec82e82d88d264483d54
cc-by-4.0
['question generation']
false
Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: tripadvisor - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: lmqg/t5-large-squad - max_length: 512 - max_length_output: 32 - epoch: 1 - batch: 16 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-large-subjqa-tripadvisor-qg/raw/main/trainer_config.json).
c55142d809026a876cb187ff60bd71f4
apache-2.0
['translation']
false
This is a finetuning of a MarianMT pretrained on English-Chinese. The target language pair is English-Vietnamese. The first phase of training (mixed) is performed on a dataset containing both English-Chinese and English-Vietnamese sentences. The second phase of training (pure) is performed on a dataset containing only English-Vietnamese sentences.
249266b671c3a44087e04eaadb96f93d
apache-2.0
['translation']
false
This token is needed to identify the target language input_sentence = "<2vi> " + sentence translated = model.generate(**tokenizer_en(input_sentence, return_tensors="pt", padding=True)) output_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in translated] ```
0f197f266fc5260f28af5dbccc74c479
apache-2.0
['translation']
false
Training results MIXED | Epoch | Bleu | |:-----:|:-------:| | 1.0 | 26.2407 | | 2.0 | 32.6016 | | 3.0 | 35.4060 | | 4.0 | 36.6737 | | 5.0 | 37.3774 | PURE | Epoch | Bleu | |:-----:|:-------:| | 1.0 | 37.3169 | | 2.0 | 37.4407 | | 3.0 | 37.6696 | | 4.0 | 37.8765 | | 5.0 | 38.0105 |
0b07f09dfa2c45deab2bd1ca1e3f5a8f
mit
['Image Translation']
false
Citation Information ```bibtex @Article{Texler20-SIG, author = "Ond\v{r}ej Texler and David Futschik and Michal Ku\v{c}era and Ond\v{r}ej Jamri\v{s}ka and \v{S}\'{a}rka Sochorov\'{a} and Menglei Chai and Sergey Tulyakov and Daniel S\'{y}kora", title = "Interactive Video Stylization Using Few-Shot Patch-Based Training", journal = "ACM Transactions on Graphics", volume = "39", number = "4", pages = "73", year = "2020", } ```
74905be414baee473c74e36315e20c5a
apache-2.0
['translation']
false
opus-mt-sv-ilo * source languages: sv * target languages: ilo * OPUS readme: [sv-ilo](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-ilo/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/sv-ilo/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-ilo/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-ilo/opus-2020-01-16.eval.txt)
ee5f49871db1aa75c9baa5413b86fbf6
apache-2.0
['korean']
false
KoELECTRA (Base Generator) Pretrained ELECTRA Language Model for Korean (`koelectra-base-generator`) For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
6c1711f48fe9a67c57c519da33ff38f2
apache-2.0
['korean']
false
Load model and tokenizer ```python >>> from transformers import ElectraModel, ElectraTokenizer >>> model = ElectraModel.from_pretrained("monologg/koelectra-base-generator") >>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-generator") ```
76c3f930973a76c68c1bc551112b3c0c
apache-2.0
['korean']
false
Tokenizer example ```python >>> from transformers import ElectraTokenizer >>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-generator") >>> tokenizer.tokenize("[CLS] 한국어 ELECTRA를 공유합니다. [SEP]") ['[CLS]', '한국어', 'E', '
b3a3e5207aa1e33392c854d2fedd3983
apache-2.0
['korean']
false
Example using ElectraForMaskedLM ```python from transformers import pipeline fill_mask = pipeline( "fill-mask", model="monologg/koelectra-base-generator", tokenizer="monologg/koelectra-base-generator" ) print(fill_mask("나는 {} 밥을 먹었다.".format(fill_mask.tokenizer.mask_token))) ```
f0732d433cdda7cfc93f3a1b24552ea5
creativeml-openrail-m
['text-to-image', 'stable-diffusion']
false
profile Dreambooth model trained by mastergruffly 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) 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) Sample pictures of this concept:
d80732cc8f1ab56b52b2b469ca10fd0d
cc-by-4.0
['espnet', 'audio', 'automatic-speech-recognition']
false
Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout bf8c8f00194bdfed8ca388d8b20d14791b7d270e pip install -e . cd egs2/voxforge/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model pyf98/voxforge_it_conformer_e12_linear2048 ``` <!-- Generated by scripts/utils/show_asr_result.sh -->
a5741504c550dc4f8403d8b77d4d38a1
cc-by-4.0
['espnet', 'audio', 'automatic-speech-recognition']
false
Environments - date: `Thu Dec 29 01:45:02 EST 2022` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.12.1` - Git hash: `bf8c8f00194bdfed8ca388d8b20d14791b7d270e` - Commit date: `Wed Dec 28 22:43:13 2022 -0500`
3c4398337f982d73e96ee4a93237f3ba
cc-by-4.0
['espnet', 'audio', 'automatic-speech-recognition']
false
WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dt_it|1035|12587|70.3|24.6|5.1|3.3|33.0|95.4| |decode_asr_asr_model_valid.acc.ave/et_it|1103|13699|72.4|22.5|5.1|2.9|30.5|91.5|
e407668edfeeb04fb78dc8613782f5af
cc-by-4.0
['espnet', 'audio', 'automatic-speech-recognition']
false
CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dt_it|1035|75494|92.9|3.9|3.2|1.8|8.9|95.4| |decode_asr_asr_model_valid.acc.ave/et_it|1103|81228|93.7|3.5|2.8|1.7|8.0|91.5|
5cfb980c98eafd22942952ad8751ced5
cc-by-4.0
['espnet', 'audio', 'automatic-speech-recognition']
false
ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_e12_linear2048.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_e12_linear2048_raw_it_char_normalize_confnorm_varsFalse ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 100 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: true log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 128 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_it_char/train/speech_shape - exp/asr_stats_raw_it_char/train/text_shape.char valid_shape_file: - exp/asr_stats_raw_it_char/valid/speech_shape - exp/asr_stats_raw_it_char/valid/text_shape.char batch_type: folded valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/tr_it/wav.scp - speech - sound - - dump/raw/tr_it/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dt_it/wav.scp - speech - sound - - dump/raw/dt_it/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.002 scheduler: warmuplr scheduler_conf: warmup_steps: 10000 token_list: - <blank> - <unk> - <space> - A - E - I - O - R - N - L - S - T - C - D - U - M - P - V - G - F - H - B - Q - Z - '''' - Ò - À - È - Ú - X - W - Í - É - Y - K - J - '1' - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: null use_preprocessor: true token_type: char bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 frontend: default frontend_conf: fs: 16k specaug: null specaug_conf: {} normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_it_char/train/feats_stats.npz norm_vars: false model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true rel_pos_type: latest pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.0 src_attention_dropout_rate: 0.0 preprocessor: default preprocessor_conf: {} required: - output_dir - token_list version: '202211' distributed: false ``` </details>
7c3dc787542c0a99ab985304254bf552
apache-2.0
[]
false
albert-small-kor-cross-encoder-v1 - albert-small-kor-v1 모델을 훈련시켜 cross-encoder로 파인튜닝한 모델 - This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
5e1ee3480b028b4620f1a2bd3e351b03
apache-2.0
[]
false
Training - sts(10)-nli(3)-sts(10)-nli(3)-sts(10) 훈련 시킴 (**distil 훈련 없음**) - STS : seed=111,epoch=10, lr=1e-4, eps=1e-6, warm_step=10%, max_seq_len=128, train_batch=128(small 모델=32) (albert 13m/7G) [훈련코드](https://github.com/kobongsoo/BERT/blob/master/sbert/cross-encoder/sbert-corossencoder-train-nli.ipynb) - NLI 훈련 : seed=111,epoch=3, lr=3e-5, eps=1e-8, warm_step=10%, max_seq_len=128, train_batch=64, eval_bath=64(albert 2h/7G) [훈련코드](https://github.com/kobongsoo/BERT/blob/master/sbert/cross-encoder/sbert-corossencoder-train-sts.ipynb) - [평가코드](https://github.com/kobongsoo/BERT/blob/master/sbert/cross-encoder/sbert-crossencoder-test3.ipynb),[테스트코드](https://github.com/kobongsoo/BERT/blob/master/sbert/cross-encoder/sbert-crossencoder-test.ipynb) - |모델 |korsts|klue-sts|glue(stsb)|stsb_multi_mt(en)| |:--------|------:|--------:|--------------:|------------:| |**albert-small-kor-cross-encoder-v1** |0.8455 |0.8526 |0.8513 |0.7976| |klue-cross-encoder-v1 |0.8262 |0.8833 |0.8512 |0.7889| |kpf-cross-encoder-v1 |0.8799 |0.9133 |0.8626 |0.8027|
8afa458d75d85ecd2f41515445bb636a
apache-2.0
[]
false
Usage and Performance Pre-trained models can be used like this: ``` from sentence_transformers import CrossEncoder model = CrossEncoder('bongsoo/albert-small-kor-cross-encoder-v1') scores = model.predict([('오늘 날씨가 좋다', '오늘 등산을 한다'), ('오늘 날씨가 흐리다', '오늘 비가 내린다')]) print(scores) ``` ``` [0.45417202 0.6294121 ] ``` The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`. You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class
2d5401994d194ba16a167a44ddd5fb2e
mit
['m2m100-12B']
false
M2M100 12B M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository. The model that can directly translate between the 9,900 directions of 100 languages. To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method. *Note: `M2M100Tokenizer` depends on `sentencepiece`, so make sure to install it before running the example.* To install `sentencepiece` run `pip install sentencepiece` ```python from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer hi_text = "जीवन एक चॉकलेट बॉक्स की तरह है।" chinese_text = "生活就像一盒巧克力。" model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100-12B-last-ckpt") tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100-12B-last-ckpt")
2ffbc8a1a2e6c7cfb4a7e3404cf34f0a
mit
['m2m100-12B']
false
translate Hindi to French tokenizer.src_lang = "hi" encoded_hi = tokenizer(hi_text, return_tensors="pt") generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.get_lang_id("fr")) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
6bf9eb781ce6fc2e44d66c8ccd64bede
mit
['m2m100-12B']
false
translate Chinese to English tokenizer.src_lang = "zh" encoded_zh = tokenizer(chinese_text, return_tensors="pt") generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en")) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
cdd97ba019191f734996503777228f7d
mit
['m2m100-12B']
false
Languages covered Afrikaans (af), Amharic (am), Arabic (ar), Asturian (ast), Azerbaijani (az), Bashkir (ba), Belarusian (be), Bulgarian (bg), Bengali (bn), Breton (br), Bosnian (bs), Catalan; Valencian (ca), Cebuano (ceb), Czech (cs), Welsh (cy), Danish (da), German (de), Greeek (el), English (en), Spanish (es), Estonian (et), Persian (fa), Fulah (ff), Finnish (fi), French (fr), Western Frisian (fy), Irish (ga), Gaelic; Scottish Gaelic (gd), Galician (gl), Gujarati (gu), Hausa (ha), Hebrew (he), Hindi (hi), Croatian (hr), Haitian; Haitian Creole (ht), Hungarian (hu), Armenian (hy), Indonesian (id), Igbo (ig), Iloko (ilo), Icelandic (is), Italian (it), Japanese (ja), Javanese (jv), Georgian (ka), Kazakh (kk), Central Khmer (km), Kannada (kn), Korean (ko), Luxembourgish; Letzeburgesch (lb), Ganda (lg), Lingala (ln), Lao (lo), Lithuanian (lt), Latvian (lv), Malagasy (mg), Macedonian (mk), Malayalam (ml), Mongolian (mn), Marathi (mr), Malay (ms), Burmese (my), Nepali (ne), Dutch; Flemish (nl), Norwegian (no), Northern Sotho (ns), Occitan (post 1500) (oc), Oriya (or), Panjabi; Punjabi (pa), Polish (pl), Pushto; Pashto (ps), Portuguese (pt), Romanian; Moldavian; Moldovan (ro), Russian (ru), Sindhi (sd), Sinhala; Sinhalese (si), Slovak (sk), Slovenian (sl), Somali (so), Albanian (sq), Serbian (sr), Swati (ss), Sundanese (su), Swedish (sv), Swahili (sw), Tamil (ta), Thai (th), Tagalog (tl), Tswana (tn), Turkish (tr), Ukrainian (uk), Urdu (ur), Uzbek (uz), Vietnamese (vi), Wolof (wo), Xhosa (xh), Yiddish (yi), Yoruba (yo), Chinese (zh), Zulu (zu)
03aff28e928b8390fb40e08c8615d17c
mit
['m2m100-12B']
false
BibTeX entry and citation info ``` @misc{fan2020englishcentric, title={Beyond English-Centric Multilingual Machine Translation}, author={Angela Fan and Shruti Bhosale and Holger Schwenk and Zhiyi Ma and Ahmed El-Kishky and Siddharth Goyal and Mandeep Baines and Onur Celebi and Guillaume Wenzek and Vishrav Chaudhary and Naman Goyal and Tom Birch and Vitaliy Liptchinsky and Sergey Edunov and Edouard Grave and Michael Auli and Armand Joulin}, year={2020}, eprint={2010.11125}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
a1c1c1e507af9bb66e2e99c062c5cbd5