license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['image-classification'] | false | resnet34 Implementation of ResNet proposed in [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) ``` python ResNet.resnet18() ResNet.resnet26() ResNet.resnet34() ResNet.resnet50() ResNet.resnet101() ResNet.resnet152() ResNet.resnet200() Variants (d) proposed in `Bag of Tricks for Image Classification with Convolutional Neural Networks <https://arxiv.org/pdf/1812.01187.pdf`_ ResNet.resnet26d() ResNet.resnet34d() ResNet.resnet50d() | 3f716353386f6de51e5c809c6287af85 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-qnli-target-glue-sst2 This model is a fine-tuned version of [muhtasham/small-mlm-glue-qnli](https://huggingface.co/muhtasham/small-mlm-glue-qnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4217 - Accuracy: 0.8716 | fcfcb9d33beb779a7bab97c7fb9523c5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3912 | 0.24 | 500 | 0.3462 | 0.8406 | | 0.3049 | 0.48 | 1000 | 0.3246 | 0.8544 | | 0.2574 | 0.71 | 1500 | 0.3264 | 0.8739 | | 0.2381 | 0.95 | 2000 | 0.2983 | 0.8807 | | 0.1836 | 1.19 | 2500 | 0.3447 | 0.8784 | | 0.1681 | 1.43 | 3000 | 0.3553 | 0.8819 | | 0.1656 | 1.66 | 3500 | 0.3758 | 0.8784 | | 0.1701 | 1.9 | 4000 | 0.3134 | 0.8991 | | 0.1337 | 2.14 | 4500 | 0.5031 | 0.8521 | | 0.1232 | 2.38 | 5000 | 0.4217 | 0.8716 | | 257d44592b7b6f33e307431fdab760a5 |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-sede-txt2sql This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the sede dataset. It achieves the following results on the evaluation set: - Loss: 1.1577 - Bleu Score: 0.5923 - Parsable Queries Accuracy: 0.0 - Partial Match F1: 0.0 - Partial Match F1 No Values: 0.0 - Partial Match Em: 0.0 - Partial Match No Values Em: 0.0 | 5fc73e7c936a3e828813b699ccd9b426 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu Score | Parsable Queries Accuracy | Partial Match F1 | Partial Match F1 No Values | Partial Match Em | Partial Match No Values Em | |:-------------:|:-----:|:----:|:---------------:|:----------:|:-------------------------:|:----------------:|:--------------------------:|:----------------:|:--------------------------:| | No log | 1.0 | 95 | 13.2410 | 0.0069 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | No log | 2.0 | 190 | 7.6317 | 0.0134 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | No log | 3.0 | 285 | 6.0919 | 0.0058 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | No log | 4.0 | 380 | 5.4922 | 0.0021 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | No log | 5.0 | 475 | 4.7151 | 0.0009 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 12.0698 | 6.0 | 570 | 4.1412 | 0.0003 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 12.0698 | 7.0 | 665 | 3.6398 | 0.0003 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 12.0698 | 8.0 | 760 | 3.2643 | 0.0009 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 12.0698 | 9.0 | 855 | 3.0544 | 0.0013 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 12.0698 | 10.0 | 950 | 2.8015 | 0.0043 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 4.696 | 11.0 | 1045 | 2.5552 | 0.0789 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 4.696 | 12.0 | 1140 | 2.3535 | 0.1036 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 4.696 | 13.0 | 1235 | 2.2132 | 0.0050 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 4.696 | 14.0 | 1330 | 2.1084 | 0.1333 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 4.696 | 15.0 | 1425 | 2.0117 | 0.2972 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 3.1348 | 16.0 | 1520 | 1.9333 | 0.2481 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 3.1348 | 17.0 | 1615 | 1.8395 | 0.4149 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 3.1348 | 18.0 | 1710 | 1.7661 | 0.5439 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 3.1348 | 19.0 | 1805 | 1.7101 | 0.6001 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 3.1348 | 20.0 | 1900 | 1.6562 | 0.6219 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 3.1348 | 21.0 | 1995 | 1.6073 | 0.5865 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 2.4276 | 22.0 | 2090 | 1.5773 | 0.5683 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 2.4276 | 23.0 | 2185 | 1.5478 | 0.5408 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 2.4276 | 24.0 | 2280 | 1.5190 | 0.5749 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 2.4276 | 25.0 | 2375 | 1.4927 | 0.5818 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 2.4276 | 26.0 | 2470 | 1.4671 | 0.5673 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 2.076 | 27.0 | 2565 | 1.4499 | 0.5616 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 2.076 | 28.0 | 2660 | 1.4275 | 0.6041 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 2.076 | 29.0 | 2755 | 1.4096 | 0.5764 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 2.076 | 30.0 | 2850 | 1.3983 | 0.5862 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 2.076 | 31.0 | 2945 | 1.3812 | 0.5982 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.8828 | 32.0 | 3040 | 1.3679 | 0.5927 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.8828 | 33.0 | 3135 | 1.3548 | 0.5916 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.8828 | 34.0 | 3230 | 1.3461 | 0.5769 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.8828 | 35.0 | 3325 | 1.3353 | 0.5871 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.8828 | 36.0 | 3420 | 1.3293 | 0.5687 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.7602 | 37.0 | 3515 | 1.3195 | 0.5689 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.7602 | 38.0 | 3610 | 1.3109 | 0.5949 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.7602 | 39.0 | 3705 | 1.3049 | 0.5619 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.7602 | 40.0 | 3800 | 1.2953 | 0.5872 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.7602 | 41.0 | 3895 | 1.2907 | 0.6014 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.7602 | 42.0 | 3990 | 1.2831 | 0.5917 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.6652 | 43.0 | 4085 | 1.2757 | 0.5718 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.6652 | 44.0 | 4180 | 1.2692 | 0.5707 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.6652 | 45.0 | 4275 | 1.2642 | 0.5758 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.6652 | 46.0 | 4370 | 1.2619 | 0.6012 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.6652 | 47.0 | 4465 | 1.2527 | 0.5749 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.6009 | 48.0 | 4560 | 1.2496 | 0.5722 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.6009 | 49.0 | 4655 | 1.2447 | 0.5633 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.6009 | 50.0 | 4750 | 1.2411 | 0.5615 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.6009 | 51.0 | 4845 | 1.2356 | 0.5691 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.6009 | 52.0 | 4940 | 1.2322 | 0.5636 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.5481 | 53.0 | 5035 | 1.2285 | 0.5724 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.5481 | 54.0 | 5130 | 1.2255 | 0.5771 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.5481 | 55.0 | 5225 | 1.2201 | 0.5827 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.5481 | 56.0 | 5320 | 1.2181 | 0.5928 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.5481 | 57.0 | 5415 | 1.2152 | 0.5599 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.5082 | 58.0 | 5510 | 1.2123 | 0.5779 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.5082 | 59.0 | 5605 | 1.2083 | 0.5609 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.5082 | 60.0 | 5700 | 1.2070 | 0.5654 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.5082 | 61.0 | 5795 | 1.2036 | 0.5566 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.5082 | 62.0 | 5890 | 1.2011 | 0.5569 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.5082 | 63.0 | 5985 | 1.1993 | 0.5567 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4799 | 64.0 | 6080 | 1.1958 | 0.5619 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4799 | 65.0 | 6175 | 1.1950 | 0.5691 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4799 | 66.0 | 6270 | 1.1914 | 0.5572 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4799 | 67.0 | 6365 | 1.1879 | 0.5635 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4799 | 68.0 | 6460 | 1.1866 | 0.5654 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4475 | 69.0 | 6555 | 1.1850 | 0.5575 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4475 | 70.0 | 6650 | 1.1833 | 0.5507 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4475 | 71.0 | 6745 | 1.1820 | 0.5493 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4475 | 72.0 | 6840 | 1.1786 | 0.5525 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4475 | 73.0 | 6935 | 1.1789 | 0.5615 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4233 | 74.0 | 7030 | 1.1770 | 0.5603 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4233 | 75.0 | 7125 | 1.1749 | 0.5699 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4233 | 76.0 | 7220 | 1.1754 | 0.5730 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4233 | 77.0 | 7315 | 1.1735 | 0.5798 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4233 | 78.0 | 7410 | 1.1716 | 0.5771 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4101 | 79.0 | 7505 | 1.1699 | 0.5800 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4101 | 80.0 | 7600 | 1.1675 | 0.5736 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4101 | 81.0 | 7695 | 1.1661 | 0.5845 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4101 | 82.0 | 7790 | 1.1659 | 0.5974 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4101 | 83.0 | 7885 | 1.1664 | 0.5825 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4101 | 84.0 | 7980 | 1.1647 | 0.5871 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3965 | 85.0 | 8075 | 1.1639 | 0.5772 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3965 | 86.0 | 8170 | 1.1628 | 0.5826 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3965 | 87.0 | 8265 | 1.1615 | 0.5960 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3965 | 88.0 | 8360 | 1.1616 | 0.5908 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3965 | 89.0 | 8455 | 1.1613 | 0.5775 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3835 | 90.0 | 8550 | 1.1604 | 0.5917 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3835 | 91.0 | 8645 | 1.1597 | 0.5732 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3835 | 92.0 | 8740 | 1.1594 | 0.5767 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3835 | 93.0 | 8835 | 1.1584 | 0.5719 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3835 | 94.0 | 8930 | 1.1581 | 0.5700 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3766 | 95.0 | 9025 | 1.1583 | 0.5845 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3766 | 96.0 | 9120 | 1.1578 | 0.5808 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3766 | 97.0 | 9215 | 1.1578 | 0.5889 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3766 | 98.0 | 9310 | 1.1577 | 0.5851 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3766 | 99.0 | 9405 | 1.1578 | 0.5923 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3726 | 100.0 | 9500 | 1.1577 | 0.5923 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 6a2ecc36c3abaa44da69ae86ed7c3570 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | hegde- Dreambooth model trained by broidkhegde 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: | 0e9e5cff08bb9be9bd0a5f0437da2f4c |
apache-2.0 | ['automatic-speech-recognition', '../AI_Light_Dance.py', 'generated_from_trainer'] | false | ai-light-dance_singing_ft_wav2vec2-large-lv60-v2 This model is a fine-tuned version of [gary109/ai-light-dance_singing_ft_wav2vec2-large-lv60](https://huggingface.co/gary109/ai-light-dance_singing_ft_wav2vec2-large-lv60) on the ../AI_LIGHT_DANCE.PY - ONSET-SINGING dataset. It achieves the following results on the evaluation set: - Loss: 0.4285 - Wer: 0.1858 | 3f7b6a7df2b638581e6d2eed2c6eb281 |
apache-2.0 | ['automatic-speech-recognition', '../AI_Light_Dance.py', '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 - lr_scheduler_warmup_steps: 500 - num_epochs: 10.0 - mixed_precision_training: Native AMP | b155dc7151ac0e67b9b6ccfc79c89f97 |
apache-2.0 | ['automatic-speech-recognition', '../AI_Light_Dance.py', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.2775 | 1.0 | 1106 | 0.4372 | 0.2117 | | 0.2154 | 2.0 | 2212 | 0.4474 | 0.2044 | | 0.2023 | 3.0 | 3318 | 0.4372 | 0.1920 | | 0.186 | 4.0 | 4424 | 0.4285 | 0.1858 | | 0.1856 | 5.0 | 5530 | 0.4589 | 0.1826 | | 0.1537 | 6.0 | 6636 | 0.4658 | 0.1774 | | 0.1337 | 7.0 | 7742 | 0.4769 | 0.1744 | | 0.108 | 8.0 | 8848 | 0.4604 | 0.1724 | | 0.1593 | 9.0 | 9954 | 0.4731 | 0.1694 | | 0.0904 | 10.0 | 11060 | 0.4843 | 0.1683 | | 7757d117b4820caaff8e618dede7b22a |
mit | ['generated_from_trainer'] | false | TweetEval_roBERTa_5E This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.2770 - Accuracy: 0.9467 | fddbe5a97aadf0efbed7cf7c0ef5352d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5967 | 0.04 | 50 | 0.4851 | 0.7333 | | 0.4085 | 0.08 | 100 | 0.2177 | 0.9333 | | 0.3449 | 0.12 | 150 | 0.2164 | 0.9333 | | 0.2739 | 0.16 | 200 | 0.2285 | 0.9267 | | 0.2588 | 0.2 | 250 | 0.2748 | 0.92 | | 0.3406 | 0.24 | 300 | 0.1956 | 0.9467 | | 0.2726 | 0.28 | 350 | 0.2285 | 0.92 | | 0.2645 | 0.32 | 400 | 0.2192 | 0.9267 | | 0.2549 | 0.37 | 450 | 0.2115 | 0.9333 | | 0.2387 | 0.41 | 500 | 0.2230 | 0.9333 | | 0.2415 | 0.45 | 550 | 0.2156 | 0.94 | | 0.2829 | 0.49 | 600 | 0.2575 | 0.9267 | | 0.2865 | 0.53 | 650 | 0.1572 | 0.9467 | | 0.2107 | 0.57 | 700 | 0.1437 | 0.9467 | | 0.2609 | 0.61 | 750 | 0.1595 | 0.94 | | 0.2234 | 0.65 | 800 | 0.2611 | 0.9333 | | 0.266 | 0.69 | 850 | 0.1544 | 0.9467 | | 0.2407 | 0.73 | 900 | 0.2145 | 0.9333 | | 0.2529 | 0.77 | 950 | 0.1861 | 0.9333 | | 0.2083 | 0.81 | 1000 | 0.1448 | 0.9533 | | 0.2942 | 0.85 | 1050 | 0.1703 | 0.9333 | | 0.1916 | 0.89 | 1100 | 0.1831 | 0.94 | | 0.2425 | 0.93 | 1150 | 0.2349 | 0.9333 | | 0.2521 | 0.97 | 1200 | 0.1268 | 0.94 | | 0.1742 | 1.01 | 1250 | 0.1782 | 0.9333 | | 0.172 | 1.06 | 1300 | 0.2636 | 0.9333 | | 0.1487 | 1.1 | 1350 | 0.1987 | 0.9467 | | 0.1805 | 1.14 | 1400 | 0.3030 | 0.9333 | | 0.1295 | 1.18 | 1450 | 0.2229 | 0.94 | | 0.2114 | 1.22 | 1500 | 0.1441 | 0.9467 | | 0.1714 | 1.26 | 1550 | 0.2157 | 0.9467 | | 0.1886 | 1.3 | 1600 | 0.2353 | 0.9267 | | 0.1666 | 1.34 | 1650 | 0.2572 | 0.94 | | 0.2254 | 1.38 | 1700 | 0.1569 | 0.9467 | | 0.1531 | 1.42 | 1750 | 0.2351 | 0.9333 | | 0.2174 | 1.46 | 1800 | 0.2137 | 0.9267 | | 0.2015 | 1.5 | 1850 | 0.2234 | 0.94 | | 0.1785 | 1.54 | 1900 | 0.1944 | 0.9333 | | 0.1954 | 1.58 | 1950 | 0.2013 | 0.9467 | | 0.1481 | 1.62 | 2000 | 0.2196 | 0.94 | | 0.1426 | 1.66 | 2050 | 0.2005 | 0.9467 | | 0.1951 | 1.7 | 2100 | 0.2281 | 0.9467 | | 0.1943 | 1.75 | 2150 | 0.1934 | 0.94 | | 0.2027 | 1.79 | 2200 | 0.1845 | 0.96 | | 0.2119 | 1.83 | 2250 | 0.1338 | 0.9533 | | 0.208 | 1.87 | 2300 | 0.1605 | 0.94 | | 0.1972 | 1.91 | 2350 | 0.1460 | 0.9533 | | 0.1876 | 1.95 | 2400 | 0.1488 | 0.9467 | | 0.1923 | 1.99 | 2450 | 0.2055 | 0.9533 | | 0.1391 | 2.03 | 2500 | 0.2245 | 0.9533 | | 0.1416 | 2.07 | 2550 | 0.2194 | 0.9533 | | 0.1521 | 2.11 | 2600 | 0.2234 | 0.9533 | | 0.0943 | 2.15 | 2650 | 0.2114 | 0.9533 | | 0.1452 | 2.19 | 2700 | 0.1772 | 0.9467 | | 0.1148 | 2.23 | 2750 | 0.2541 | 0.9333 | | 0.1706 | 2.27 | 2800 | 0.2151 | 0.9533 | | 0.12 | 2.31 | 2850 | 0.2521 | 0.9467 | | 0.181 | 2.35 | 2900 | 0.2518 | 0.9467 | | 0.1308 | 2.39 | 2950 | 0.2610 | 0.9533 | | 0.1482 | 2.44 | 3000 | 0.1789 | 0.9533 | | 0.1019 | 2.48 | 3050 | 0.2377 | 0.9467 | | 0.1474 | 2.52 | 3100 | 0.2468 | 0.94 | | 0.0843 | 2.56 | 3150 | 0.3056 | 0.94 | | 0.1521 | 2.6 | 3200 | 0.2067 | 0.96 | | 0.1333 | 2.64 | 3250 | 0.1921 | 0.94 | | 0.1318 | 2.68 | 3300 | 0.1699 | 0.96 | | 0.1503 | 2.72 | 3350 | 0.2186 | 0.94 | | 0.1242 | 2.76 | 3400 | 0.2322 | 0.94 | | 0.1179 | 2.8 | 3450 | 0.2313 | 0.9467 | | 0.1247 | 2.84 | 3500 | 0.2298 | 0.9467 | | 0.1289 | 2.88 | 3550 | 0.2502 | 0.94 | | 0.1597 | 2.92 | 3600 | 0.1875 | 0.9467 | | 0.1645 | 2.96 | 3650 | 0.2469 | 0.94 | | 0.1366 | 3.0 | 3700 | 0.2469 | 0.94 | | 0.1418 | 3.04 | 3750 | 0.2457 | 0.9467 | | 0.1146 | 3.08 | 3800 | 0.2188 | 0.9467 | | 0.091 | 3.12 | 3850 | 0.2476 | 0.94 | | 0.0972 | 3.17 | 3900 | 0.2791 | 0.94 | | 0.0976 | 3.21 | 3950 | 0.2933 | 0.9333 | | 0.0872 | 3.25 | 4000 | 0.2877 | 0.9467 | | 0.0857 | 3.29 | 4050 | 0.2664 | 0.9467 | | 0.1368 | 3.33 | 4100 | 0.2533 | 0.9467 | | 0.0713 | 3.37 | 4150 | 0.2855 | 0.9467 | | 0.1101 | 3.41 | 4200 | 0.2716 | 0.9533 | | 0.0871 | 3.45 | 4250 | 0.2654 | 0.9467 | | 0.1152 | 3.49 | 4300 | 0.2449 | 0.9467 | | 0.0441 | 3.53 | 4350 | 0.2904 | 0.9467 | | 0.1503 | 3.57 | 4400 | 0.2784 | 0.9467 | | 0.0763 | 3.61 | 4450 | 0.2804 | 0.9467 | | 0.083 | 3.65 | 4500 | 0.3278 | 0.94 | | 0.1111 | 3.69 | 4550 | 0.2899 | 0.9333 | | 0.0791 | 3.73 | 4600 | 0.3137 | 0.9333 | | 0.0837 | 3.77 | 4650 | 0.2799 | 0.9467 | | 0.1048 | 3.81 | 4700 | 0.2496 | 0.9533 | | 0.1031 | 3.86 | 4750 | 0.2689 | 0.9533 | | 0.0837 | 3.9 | 4800 | 0.2753 | 0.9533 | | 0.0929 | 3.94 | 4850 | 0.2357 | 0.9467 | | 0.0856 | 3.98 | 4900 | 0.2615 | 0.9467 | | 0.0619 | 4.02 | 4950 | 0.2983 | 0.9467 | | 0.0974 | 4.06 | 5000 | 0.2706 | 0.9533 | | 0.0548 | 4.1 | 5050 | 0.2978 | 0.9467 | | 0.0425 | 4.14 | 5100 | 0.3217 | 0.9333 | | 0.0808 | 4.18 | 5150 | 0.3054 | 0.94 | | 0.0466 | 4.22 | 5200 | 0.3142 | 0.94 | | 0.0593 | 4.26 | 5250 | 0.3193 | 0.9267 | | 0.0551 | 4.3 | 5300 | 0.3017 | 0.9333 | | 0.0493 | 4.34 | 5350 | 0.2954 | 0.94 | | 0.0897 | 4.38 | 5400 | 0.2912 | 0.9467 | | 0.0529 | 4.42 | 5450 | 0.2956 | 0.94 | | 0.0924 | 4.46 | 5500 | 0.2858 | 0.94 | | 0.1018 | 4.5 | 5550 | 0.2826 | 0.94 | | 0.1137 | 4.55 | 5600 | 0.2711 | 0.94 | | 0.0667 | 4.59 | 5650 | 0.2776 | 0.94 | | 0.0521 | 4.63 | 5700 | 0.2955 | 0.94 | | 0.0334 | 4.67 | 5750 | 0.2972 | 0.94 | | 0.0298 | 4.71 | 5800 | 0.3133 | 0.94 | | 0.1261 | 4.75 | 5850 | 0.2891 | 0.9467 | | 0.0514 | 4.79 | 5900 | 0.2804 | 0.9467 | | 0.0416 | 4.83 | 5950 | 0.2809 | 0.94 | | 0.0745 | 4.87 | 6000 | 0.2774 | 0.9467 | | 0.1134 | 4.91 | 6050 | 0.2715 | 0.9467 | | 0.0446 | 4.95 | 6100 | 0.2748 | 0.9467 | | 0.0581 | 4.99 | 6150 | 0.2770 | 0.9467 | | 9239bc11a0eb84a4954c483287bc7a7e |
mit | ['ja', 'japanese', 'tokenizer'] | false | Japanese Dummy Tokenizer Repository containing a dummy Japanese Tokenizer trained on ```snow_simplified_japanese_corpus``` dataset. The tokenizer has been trained using Hugging Face datasets in a streaming manner. | 044fdaeb0d610750d212726ea02f3141 |
apache-2.0 | ['bert'] | false | Erlangshen-Deberta-97M-Chinese，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM). The 97 million parameter deberta-V2 base model, using 180G Chinese data, 24 A100(40G) training for 7 days，which is a encoder-only transformer structure. Consumed totally 1B samples. | 86f2028fc1a79af892c509c665f2d063 |
apache-2.0 | ['bert'] | false | Usage ```python from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline import torch tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese', use_fast=False) model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese') text = '生活的真谛是[MASK]。' fillmask_pipe = FillMaskPipeline(model, tokenizer, device=7) print(fillmask_pipe(text, top_k=10)) ``` | 76487b756c163847caa1918b030747ad |
apache-2.0 | ['bert'] | false | Finetune We present the dev results on some tasks. | Model | OCNLI | CMNLI | | ---------------------------------- | ----- | ------ | | RoBERTa-base | 0.743 | 0.7973 | | **Erlangshen-Deberta-97M-Chinese** | 0.752 | 0.807 | | cc6555d1055cbb3adcfe3c1b8dc8534c |
apache-2.0 | ['bert'] | false | Citation If you find the resource is useful, please cite the following website in your paper. ``` @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2022}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ``` | 7be58359883a05a37f7dfe76f8d440bf |
apache-2.0 | ['translation'] | false | opus-mt-en-sm * source languages: en * target languages: sm * OPUS readme: [en-sm](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-sm/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-sm/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-sm/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-sm/opus-2020-01-08.eval.txt) | 74a454ee13f66bdd21b61cfac280eed8 |
mit | ['generated_from_trainer'] | false | roberta-base.CEBaB_confounding.uniform.sa.5-class.seed_42 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE dataset. It achieves the following results on the evaluation set: - Loss: 0.6956 - Accuracy: 0.7262 - Macro-f1: 0.7053 - Weighted-macro-f1: 0.7201 | 234c33eab00749001e339a9cf455518f |
mit | [] | false | Description This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022) We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022) We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models: | method | epoch 1 | epoch 3 | epoch 3 | epoch 4 | |--- |--- |--- |--- |--- | | raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) | | m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) | | m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) | | regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) | | w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) | | w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) | This model is `w-m-vote-nonstrict-epoch-4` | 5ddb4eba057ab11cbd5658fa932c5ce2 |
mit | [] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline model_name = 'w-m-vote-nonstrict-epoch-4' tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased") full_model_path = f'MartinoMensio/racism-models-{model_name}' model = AutoModelForSequenceClassification.from_pretrained(full_model_path) pipe = pipeline("text-classification", model = model, tokenizer = tokenizer) texts = [ 'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!', 'Es que los judíos controlan el mundo' ] print(pipe(texts)) | ff54dcc0002b33307953a34233615710 |
apache-2.0 | ['translation'] | false | opus-mt-en-bem * source languages: en * target languages: bem * OPUS readme: [en-bem](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-bem/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-bem/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-bem/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-bem/opus-2020-01-08.eval.txt) | c49e67da386311b8d4b3cc1c304eee10 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'es', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Spanish Added custom language model to https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Spanish using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint | 546bb82be4cb221c44eed7d1446e89ed |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'es', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows... Using the [ASRecognition](https://github.com/jonatasgrosman/asrecognition) library: ```python from asrecognition import ASREngine asr = ASREngine("es", model_path="jonatasgrosman/wav2vec2-large-xlsr-53-spanish") audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] transcriptions = asr.transcribe(audio_paths) ``` Writing your own inference script: ```python import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "es" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-spanish" SAMPLES = 10 test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) | edeb26d8cccd7265803a4cdf037bfe30 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'es', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021wav2vec2-large-xlsr-53-spanish, title={XLSR Wav2Vec2 Spanish by Jonatas Grosman}, author={Grosman, Jonatas}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish}}, year={2021} } ``` | 73af57646337521fa4f5ac898696a94b |
mit | ['msmarco', 'miniLM', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Introduction mMiniLM-L6-v2-mmarco-v1 is a multilingual miniLM-based model finetuned on a multilingual version of MS MARCO passage dataset. This dataset, named mMARCO, is formed by passages in 9 different languages, translated from English MS MARCO passages collection. In the version v1, the datasets were translated using [Helsinki](https://huggingface.co/Helsinki-NLP) NMT model. Further information about the dataset or the translation method can be found on our [**mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset**](https://arxiv.org/abs/2108.13897) and [mMARCO](https://github.com/unicamp-dl/mMARCO) repository. | 7a47c32fba06df024cf7357e994e5643 |
mit | ['msmarco', 'miniLM', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Usage ```python from transformers import AutoTokenizer, AutoModel model_name = 'unicamp-dl/mMiniLM-L6-v2-mmarco-v1' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) ``` | 7a9a248ad4354ed93ab8958ecbc725d2 |
mit | ['msmarco', 'miniLM', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Citation If you use mMiniLM-L6-v2-mmarco-v1, please cite: @misc{bonifacio2021mmarco, title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset}, author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and and Roberto Lotufo and Rodrigo Nogueira}, year={2021}, eprint={2108.13897}, archivePrefix={arXiv}, primaryClass={cs.CL} } | 540de832d47d4a369bd0326c7b31e726 |
apache-2.0 | ['generated_from_trainer'] | false | nli-distilroberta-base-finetuned-cola This model is a fine-tuned version of [cross-encoder/nli-distilroberta-base](https://huggingface.co/cross-encoder/nli-distilroberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8280 - Matthews Correlation: 0.4957 | 5146f0cc4ca552b416e2a26910bf502f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5646 | 1.0 | 535 | 0.6462 | 0.3422 | | 0.4267 | 2.0 | 1070 | 0.5672 | 0.4422 | | 0.3354 | 3.0 | 1605 | 0.6441 | 0.4698 | | 0.2723 | 4.0 | 2140 | 0.7464 | 0.4670 | | 0.2204 | 5.0 | 2675 | 0.8280 | 0.4957 | | 0c09a061569fdb669e0b041cf170868a |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_sst2 This model is a fine-tuned version of [gokuls/mobilebert_sa_pre-training-complete](https://huggingface.co/gokuls/mobilebert_sa_pre-training-complete) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2364 - Accuracy: 0.9266 | 64e17885aa237d2fdf456346fa0cc0d6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.4176 | 1.0 | 527 | 0.2978 | 0.9197 | | 0.1807 | 2.0 | 1054 | 0.2951 | 0.9174 | | 0.1163 | 3.0 | 1581 | 0.2749 | 0.9186 | | 0.0862 | 4.0 | 2108 | 0.2988 | 0.9083 | | 0.0695 | 5.0 | 2635 | 0.2760 | 0.9174 | | 0.0598 | 6.0 | 3162 | 0.2695 | 0.9151 | | 0.0525 | 7.0 | 3689 | 0.2723 | 0.9255 | | 0.0464 | 8.0 | 4216 | 0.2430 | 0.9243 | | 0.0422 | 9.0 | 4743 | 0.2814 | 0.9243 | | 0.0395 | 10.0 | 5270 | 0.2464 | 0.9163 | | 0.0357 | 11.0 | 5797 | 0.2390 | 0.9197 | | 0.0341 | 12.0 | 6324 | 0.2713 | 0.9197 | | 0.0328 | 13.0 | 6851 | 0.2685 | 0.9220 | | 0.0315 | 14.0 | 7378 | 0.2585 | 0.9186 | | 0.0296 | 15.0 | 7905 | 0.2367 | 0.9220 | | 0.0283 | 16.0 | 8432 | 0.2560 | 0.9186 | | 0.0277 | 17.0 | 8959 | 0.2635 | 0.9174 | | 0.0269 | 18.0 | 9486 | 0.2364 | 0.9266 | | 0.026 | 19.0 | 10013 | 0.2749 | 0.9209 | | 0.0252 | 20.0 | 10540 | 0.2507 | 0.9174 | | 0.0248 | 21.0 | 11067 | 0.2769 | 0.9163 | | 0.0248 | 22.0 | 11594 | 0.2543 | 0.9220 | | 0.024 | 23.0 | 12121 | 0.2677 | 0.9209 | | 1236efa40982fdd48d3c185bc48578eb |
apache-2.0 | ['generated_from_trainer'] | false | english-filipino-wav2vec2-l-xls-r-test-02 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the filipino_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4561 - Wer: 0.2632 | 9cc07e9cd537709d4806909aaacf0aa3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 40 - mixed_precision_training: Native AMP | 87b52d5a6adcf2fbe3908401fe10996f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.1707 | 2.09 | 400 | 0.8006 | 0.8224 | | 0.4801 | 4.19 | 800 | 0.3363 | 0.4329 | | 0.2541 | 6.28 | 1200 | 0.3365 | 0.3676 | | 0.1851 | 8.38 | 1600 | 0.3485 | 0.3739 | | 0.1408 | 10.47 | 2000 | 0.3628 | 0.3420 | | 0.1098 | 12.57 | 2400 | 0.3979 | 0.3277 | | 0.1019 | 14.66 | 2800 | 0.4031 | 0.2896 | | 0.0887 | 16.75 | 3200 | 0.3977 | 0.3024 | | 0.0798 | 18.85 | 3600 | 0.3959 | 0.3129 | | 0.0671 | 20.94 | 4000 | 0.4489 | 0.3241 | | 0.0633 | 23.04 | 4400 | 0.4455 | 0.3026 | | 0.055 | 25.13 | 4800 | 0.4668 | 0.2910 | | 0.0523 | 27.23 | 5200 | 0.4670 | 0.2960 | | 0.0468 | 29.32 | 5600 | 0.4536 | 0.2781 | | 0.0392 | 31.41 | 6000 | 0.4612 | 0.2860 | | 0.0381 | 33.51 | 6400 | 0.4651 | 0.2841 | | 0.034 | 35.6 | 6800 | 0.4723 | 0.2716 | | 0.0315 | 37.7 | 7200 | 0.4546 | 0.2642 | | 0.0294 | 39.79 | 7600 | 0.4561 | 0.2632 | | ebf7c521f91d4dd7284120eb842e0376 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Nepali This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 ne-NP dataset. It achieves the following results on the evaluation set: - Loss: 1.5835 - Wer: 231.7073 | 6398199e008b7a8f2be8e4f4ab2065b4 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.0 | 999.0 | 1000 | 1.5835 | 231.7073 | | 0.0 | 1999.0 | 2000 | 1.9067 | 231.7073 | | 0.0 | 2999.0 | 3000 | 2.1258 | 236.5854 | | 0.0 | 3999.0 | 4000 | 2.3147 | 243.9024 | | 0.0 | 4999.0 | 5000 | 2.3599 | 234.1463 | | 22cda70c29f6d10ecfe77afb0975a277 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-stsb-target-glue-qnli This model is a fine-tuned version of [muhtasham/small-mlm-glue-stsb](https://huggingface.co/muhtasham/small-mlm-glue-stsb) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3477 - Accuracy: 0.8547 | 8d8c59531175a13a51e4a9123b73df81 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4913 | 0.15 | 500 | 0.3941 | 0.8287 | | 0.4468 | 0.31 | 1000 | 0.3872 | 0.8303 | | 0.4246 | 0.46 | 1500 | 0.3619 | 0.8411 | | 0.4133 | 0.61 | 2000 | 0.3757 | 0.8375 | | 0.4133 | 0.76 | 2500 | 0.3445 | 0.8503 | | 0.3958 | 0.92 | 3000 | 0.3340 | 0.8574 | | 0.3576 | 1.07 | 3500 | 0.3426 | 0.8558 | | 0.318 | 1.22 | 4000 | 0.3568 | 0.8559 | | 0.3166 | 1.37 | 4500 | 0.3477 | 0.8547 | | 38fe0f388ced6d29ba6a0c507ea5b968 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Thai - Parinthapat Pengpun This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 and the FLEURS datasets. It achieves the following results on the evaluation set: - eval_loss: 0.1875 - eval_wer: 17.5807 - eval_cer: 8.9942 - eval_runtime: 14734.8594 - eval_samples_per_second: 0.742 - eval_steps_per_second: 0.046 - epoch: 10.02 - step: 11000 | 5f2e0f5a4fcd6df137707a80361ff3b7 |
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: 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 - training_steps: 15000 - mixed_precision_training: Native AMP | 81715c0e99dbf4eedbdad237a335a285 |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-ca-finetuned-mnli This model is a fine-tuned version of [BSC-TeMU/roberta-base-ca](https://huggingface.co/BSC-TeMU/roberta-base-ca) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4137 - Accuracy: 0.8778 | a3a87fd5236b9af8b951898ea9f571ff |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3699 | 1.0 | 1255 | 0.3712 | 0.8669 | | 0.3082 | 2.0 | 2510 | 0.3401 | 0.8766 | | 0.2375 | 3.0 | 3765 | 0.4137 | 0.8778 | | 0.1889 | 4.0 | 5020 | 0.4671 | 0.8733 | | 0.1486 | 5.0 | 6275 | 0.5205 | 0.8749 | | 888be37fcc78c3856b0f8d1cf072842f |
mit | [] | false | model by alxdfy This your the Stable Diffusion model fine-tuned the noggles_glasses_1200 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of a person wearing sks glasses** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). Here are the images used for training this concept:                  | fee52744aeaed226d12b2a80c1c6e21c |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'hy', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the /WORKSPACE/DATA/HY/NOIZY_STUDENT_4/ - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.1693 - Wer: 0.2373 - Cer: 0.0429 | 5de493b6ec2414525e8d3206935c54b2 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'hy', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 842 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - training_steps: 5000 - mixed_precision_training: Native AMP | 045cf0b351b5c7011e20ca4a16f7d917 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'hy', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 1.255 | 7.24 | 500 | 0.2978 | 0.4294 | 0.0758 | | 1.0058 | 14.49 | 1000 | 0.1883 | 0.2838 | 0.0483 | | 0.9371 | 21.73 | 1500 | 0.1813 | 0.2627 | 0.0457 | | 0.8999 | 28.98 | 2000 | 0.1693 | 0.2373 | 0.0429 | | 0.8814 | 36.23 | 2500 | 0.1760 | 0.2420 | 0.0435 | | 0.8364 | 43.47 | 3000 | 0.1765 | 0.2416 | 0.0419 | | 0.8019 | 50.72 | 3500 | 0.1758 | 0.2311 | 0.0398 | | 0.7665 | 57.96 | 4000 | 0.1745 | 0.2240 | 0.0399 | | 0.7376 | 65.22 | 4500 | 0.1717 | 0.2190 | 0.0385 | | 0.716 | 72.46 | 5000 | 0.1700 | 0.2147 | 0.0382 | | 046b22af9f1df42e3644fc7db1db91eb |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-imdb 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: 3.5540 | 355bd13645f2cc96cab8b18465af3f88 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 4.2358 | 0.16 | 500 | 3.8225 | | 4.1206 | 0.32 | 1000 | 3.7793 | | 4.0857 | 0.48 | 1500 | 3.7520 | | 4.0699 | 0.64 | 2000 | 3.7277 | | 4.0378 | 0.8 | 2500 | 3.7125 | | 4.0191 | 0.96 | 3000 | 3.7019 | | 3.9747 | 1.12 | 3500 | 3.6871 | | 3.9647 | 1.28 | 4000 | 3.6735 | | 3.956 | 1.44 | 4500 | 3.6773 | | 3.9574 | 1.6 | 5000 | 3.6580 | | 3.9408 | 1.76 | 5500 | 3.6435 | | 3.9421 | 1.92 | 6000 | 3.6419 | | 3.9265 | 2.08 | 6500 | 3.6343 | | 3.9198 | 2.24 | 7000 | 3.6306 | | 3.9205 | 2.4 | 7500 | 3.6198 | | 3.8985 | 2.56 | 8000 | 3.6158 | | 3.9167 | 2.72 | 8500 | 3.6091 | | 3.9111 | 2.88 | 9000 | 3.6073 | | 3.8882 | 3.04 | 9500 | 3.5922 | | 3.8761 | 3.2 | 10000 | 3.5908 | | 3.8603 | 3.36 | 10500 | 3.5841 | | 3.8621 | 3.52 | 11000 | 3.5835 | | 3.8332 | 3.68 | 11500 | 3.5883 | | 3.8523 | 3.84 | 12000 | 3.5798 | | 3.8449 | 4.0 | 12500 | 3.5771 | | 3.8284 | 4.16 | 13000 | 3.5653 | | 3.8253 | 4.32 | 13500 | 3.5701 | | 3.8021 | 4.48 | 14000 | 3.5681 | | 3.8316 | 4.64 | 14500 | 3.5537 | | 3.8318 | 4.8 | 15000 | 3.5609 | | 3.82 | 4.96 | 15500 | 3.5579 | | 3.8094 | 5.12 | 16000 | 3.5540 | | 7362d50fd4c33f01c2e9b0ad8ffbf6ce |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-base-squad-qg-no-paragraph` This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). This model is fine-tuned without pargraph information but only the sentence that contains the answer. | 68d5ece1298648cf3ca1a7dad0737e0a |
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", "research-backup/bart-base-squad-qg-no-paragraph") output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` | 1d0ea686c5a9c76e02172a887dcb88dc |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-base-squad-qg-no-paragraph/raw/main/eval/metric.first.sentence.sentence_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:---------------------------------------------------------------| | BERTScore | 90.7 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_1 | 55.85 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_2 | 39.85 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_3 | 30.44 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_4 | 23.86 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | METEOR | 25.18 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | MoverScore | 63.85 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | ROUGE_L | 51.43 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | 286778d96b7d154b61e90b258beec858 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['sentence_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-base - max_length: 128 - max_length_output: 32 - epoch: 3 - batch: 64 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 2 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/bart-base-squad-qg-no-paragraph/raw/main/trainer_config.json). | 524510572cd39d1a8045289745fdef23 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_unispeech-sat_s459 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition on English 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. | 53ef484e01ce2eb4920b362a00b74807 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 8.9728 | 0.19 | 500 | 8.6854 | | 8.7387 | 0.39 | 1000 | 8.7712 | | 8.6739 | 0.58 | 1500 | 8.7362 | | 8.786 | 0.77 | 2000 | 8.7816 | | 8.6918 | 0.97 | 2500 | 8.6802 | | 8.595 | 1.16 | 3000 | 8.7086 | | 8.5342 | 1.36 | 3500 | 8.6558 | | 8.6484 | 1.55 | 4000 | 8.7442 | | 8.5594 | 1.74 | 4500 | 8.7238 | | 8.4791 | 1.94 | 5000 | 8.7073 | | 8.4489 | 2.13 | 5500 | 8.6470 | | 8.42 | 2.32 | 6000 | 8.7016 | | 8.4389 | 2.52 | 6500 | 8.6039 | | 8.5176 | 2.71 | 7000 | 8.6179 | | 8.5392 | 2.9 | 7500 | 8.6394 | | 0295613f741e216ea1c1f2885182583f |
apache-2.0 | ['generated_from_keras_callback'] | false | tomthekkan/mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.1138 - Validation Loss: 3.3816 - Epoch: 7 | 5076e9b10075d1c8fb9dbd65bd305485 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 9.9822 | 4.2802 | 0 | | 5.9654 | 3.7811 | 1 | | 5.2343 | 3.6557 | 2 | | 4.8190 | 3.5433 | 3 | | 4.5149 | 3.4695 | 4 | | 4.3105 | 3.4202 | 5 | | 4.1907 | 3.3909 | 6 | | 4.1138 | 3.3816 | 7 | | f1ac31996c8314c1e6bc8f599aa66289 |
creativeml-openrail-m | ['text-to-image'] | false | Open Potion Bottle v2 Dreambooth model trained by [piEsposito](https://twitter.com/piesposi_to) with open weights, configs and prompts (as it should be) - Concept: `potionbottle` You can run this 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: | 6d7bc6b29fc269169a3c0c98282d63d5 |
creativeml-openrail-m | ['text-to-image'] | false | Usage examples with `potionbottle` - Prompt: fantasy dragon inside a potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/pottionbottle_1.png" width=512/> - Prompt: potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/potionbottle_2.png" width=512/> - Prompt: green potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/potionbottle_3.png" width=512/> - Prompt: spiral galaxy inside a potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/potionbottle_4.png" width=512/> - Prompt: lightning storm inside a potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/pottionbottle_5.png" width=512/> - Prompt: pomeranian as a potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/potionbottle_6.png" width=512/> - Prompt: milkshake as potionbottle, perfectly ornated, intricate details, 3d render vray, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/pottionbottle_7.png" width=512/> - Prompt: a square potionbottle full of fire. Art by smoose2. Caustic reflections, shadows - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/pottionbottle_8.png" width=512/> | aeac67eb67c80bfa44765094641712f6 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-sst2-target-glue-cola This model is a fine-tuned version of [muhtasham/small-mlm-glue-sst2](https://huggingface.co/muhtasham/small-mlm-glue-sst2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5598 - Matthews Correlation: 0.3885 | 48d3b16e50083d934c9ec524c56f6aa5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5397 | 1.87 | 500 | 0.6364 | 0.2396 | | 0.3514 | 3.73 | 1000 | 0.7722 | 0.3110 | | 0.2254 | 5.6 | 1500 | 0.8466 | 0.3528 | | 0.1675 | 7.46 | 2000 | 0.9693 | 0.3824 | | 0.1238 | 9.33 | 2500 | 1.1907 | 0.3798 | | 0.1043 | 11.19 | 3000 | 1.2831 | 0.4028 | | 0.0934 | 13.06 | 3500 | 1.3186 | 0.3478 | | 0.0807 | 14.93 | 4000 | 1.3018 | 0.4120 | | 0.0616 | 16.79 | 4500 | 1.4735 | 0.3913 | | 0.0626 | 18.66 | 5000 | 1.5598 | 0.3885 | | 6151dec8e706a477d5ee7d6b37c54ec6 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | marian-finetuned-kde4-en-to-vi-190322 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-vi](https://huggingface.co/Helsinki-NLP/opus-mt-en-vi) on the mt_eng_vietnamese dataset. It achieves the following results on the evaluation set: - Loss: 1.2652 - Bleu: 37.2837 | ff2d198eef69d481b7820691fbcbd679 |
apache-2.0 | [] | false | Model description **CAMeLBERT-CA POS-GLF Model** is a Gulf Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-CA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/) model. For the fine-tuning, we used the [Gumar](https://camel.abudhabi.nyu.edu/annotated-gumar-corpus/) dataset. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT). | 7b311b230fd4442ab233e4d9db0ae678 |
apache-2.0 | [] | false | How to use To use the model with a transformers pipeline: ```python >>> from transformers import pipeline >>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-glf') >>> text = 'شلونك ؟ شخبارك ؟' >>> pos(text) [{'entity': 'noun', 'score': 0.99572617, 'index': 1, 'word': 'شلون', 'start': 0, 'end': 4}, {'entity': 'noun', 'score': 0.9411187, 'index': 2, 'word': ' | 15e515217a0f8d8723a859105f9c4b6b |
apache-2.0 | [] | false | ك', 'start': 4, 'end': 5}, {'entity': 'punc', 'score': 0.9999661, 'index': 3, 'word': '؟', 'start': 6, 'end': 7}, {'entity': 'noun', 'score': 0.99286526, 'index': 4, 'word': 'ش', 'start': 8, 'end': 9}, {'entity': 'noun', 'score': 0.9983397, 'index': 5, 'word': ' | ff944d6f8bc9300f3446ee85aeafa74e |
apache-2.0 | [] | false | ك', 'start': 13, 'end': 14}, {'entity': 'punc', 'score': 0.9999668, 'index': 7, 'word': '؟', 'start': 15, 'end': 16}] ``` *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually. | 7eb509f0bf62ccdf749972b17b2ede94 |
mit | ['generated_from_trainer'] | false | amh_xlmr This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1295 | 66fadbc319d937a305fa5131bd627697 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2_imtiaz This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the cvbn dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1956 - eval_wer: 0.2202 - eval_runtime: 574.912 - eval_samples_per_second: 8.697 - eval_steps_per_second: 0.544 - epoch: 9.41 - step: 22000 | a66dae6dcca5b441070eb34dda38887d |
apache-2.0 | ['generated_from_trainer', 'text-generation', 'opt', 'non-commercial', 'dialogue', 'chatbot'] | false | pszemraj/opt-peter-1.3B This model is a fine-tuned version of [pszemraj/opt-peter-1.3B-1E](https://huggingface.co/pszemraj/opt-peter-1.3B-1E) on 80k Whatsapp/iMessages (mine). It achieves the following results on the evaluation set, after training for 1 epoch (_on top of the 1E checkpoint linked above_): - eval_loss: 3.4220 - eval_runtime: 954.9678 - eval_samples_per_second: 9.114 - eval_steps_per_second: 2.279 - epoch: 1.0 - step: 1235 | 09731b72dc5f5a82436cdb8ffbe3eea3 |
apache-2.0 | ['generated_from_trainer', 'text-generation', 'opt', 'non-commercial', 'dialogue', 'chatbot'] | false | Intended uses & limitations - OPT has a license that does not allow for commercial use, see original for details - **any statements or claims made by this model do not reflect actual claims/statements by me** | 9090f042ce852fe25dcacd479268ff14 |
apache-2.0 | ['generated_from_trainer', 'text-generation', 'opt', 'non-commercial', 'dialogue', 'chatbot'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 2 | 541aeef70eb367857bc275a68051fcc4 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/sentence-t5-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
This model was converted from the Tensorflow model [st5-base-1](https://tfhub.dev/google/sentence-t5/st5-base/1) to PyTorch. When using this model, have a look at the publication: [Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877). The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results.
The model uses only the encoder from a T5-base model. The weights are stored in FP16.
| c4bd6adb311e8de2453e1e383506352f |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/sentence-t5-base')
embeddings = model.encode(sentences)
print(embeddings)
```
The model requires sentence-transformers version 2.2.0 or newer.
| 310e4a1ac5f326f987515a55781d509b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/sentence-t5-base)
| eb9958cd1606e2fecf20c67a79d77eb7 |
apache-2.0 | ['generated_from_trainer'] | false | beit-base-patch16-224-pt22k-ft22k This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1433 - Accuracy: 0.3333 | f9590bf513eed63f73c952431128dc94 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.67 | 1 | 1.5398 | 0.1667 | | No log | 1.67 | 2 | 1.1394 | 0.5556 | | No log | 2.67 | 3 | 1.1433 | 0.3333 | | 14bc42bafedb0c1799a1b6cfe46d3f02 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-base-subjqa-vanilla-books-qg` This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: books) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | a3cf10c37365f8ce97299f4796788f5e |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [facebook/bart-base](https://huggingface.co/facebook/bart-base) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (books) - **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) | 74fecb9ed9edb4a67a09fbb9fc1d95e2 |
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", "research-backup/bart-base-subjqa-vanilla-books-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.") ``` | d726d399a16a04993f4fb5da68596024 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-base-subjqa-vanilla-books-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.books.json) | | Score | Type | Dataset | |:-----------|--------:|:-------|:-----------------------------------------------------------------| | BERTScore | 84.11 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_1 | 3.75 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_2 | 1.84 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_3 | 0.52 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_4 | 0 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | METEOR | 11.37 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | MoverScore | 52.79 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | ROUGE_L | 8.31 | books | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | aaf64a5676a4b4ab5f9d49ec1c9abc72 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: books - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: facebook/bart-base - max_length: 512 - max_length_output: 32 - epoch: 1 - batch: 8 - lr: 5e-05 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 16 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/bart-base-subjqa-vanilla-books-qg/raw/main/trainer_config.json). | 701f5625c60807a280b4f38003f4ea36 |
creativeml-openrail-m | ['text-to-image'] | false | model by kingery This your the Stable Diffusion model fine-tuned the hyc_01_sdv1-5_2e_6_1500_man_ddim concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of yangguangkechuang man** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And 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) Here are the images used for training this concept:       | 81d1197697be5e1acc6a25da9c66b657 |
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 8ee35df7260008e9a8a20d9a9b64773a02f706ef pip install -e . cd egs2/tedlium2/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model pyf98/tedlium2_conformer_e15 ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 330af2749e6bfa4c1ad15f090c78595b |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Sat Dec 17 04:27:41 CST 2022` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202209` - pytorch version: `pytorch 1.12.1` - Git hash: `26f432bc859e5e40cac1a86042d498ba7baffbb0` - Commit date: `Fri Dec 9 02:16:01 2022 +0000` | 225b98f704784bbcb27c20ca0aaf5ee9 |
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/dev|466|14671|93.5|4.1|2.5|1.0|7.5|70.0| |decode_asr_asr_model_valid.acc.ave/test|1155|27500|93.4|4.0|2.6|1.0|7.6|64.2| | 6e4ff7fe0c27f5de106d75e6beecd2c7 |
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/dev|466|78259|97.0|0.8|2.1|0.8|3.8|70.0| |decode_asr_asr_model_valid.acc.ave/test|1155|145066|97.0|0.9|2.2|0.9|4.0|64.2| | 703e8f12bb23d182a84f55ee69259731 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev|466|28296|95.0|2.8|2.2|0.8|5.9|70.0| |decode_asr_asr_model_valid.acc.ave/test|1155|52113|95.1|2.5|2.4|0.9|5.8|64.2| | 5e7f71621e43085f3e09057c162c7ce1 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_e15.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_e15_raw_en_bpe500_sp ngpu: 1 seed: 2022 num_workers: 6 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 2 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 59747 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 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: 20 valid_batch_size: null batch_bins: 50000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe500_sp/train/speech_shape - exp/asr_stats_raw_en_bpe500_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe500_sp/valid/speech_shape - exp/asr_stats_raw_en_bpe500_sp/valid/text_shape.bpe batch_type: numel 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/train_sp/wav.scp - speech - kaldi_ark - - dump/raw/train_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - kaldi_ark - - dump/raw/dev/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 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 15000 token_list: - <blank> - <unk> - s - ▁the - t - ▁a - ▁and - ▁to - d - e - ▁of - '''' - n - ing - ▁in - ▁i - ▁that - i - a - l - p - m - y - o - ▁it - ▁we - c - u - ▁you - ed - ▁ - r - ▁is - re - ▁this - ar - g - ▁so - al - b - ▁s - or - ▁f - ▁c - in - k - f - ▁for - ic - er - le - ▁be - ▁do - ▁re - ve - ▁e - ▁w - ▁was - es - ▁they - ly - h - ▁on - v - ▁are - ri - ▁have - an - ▁what - ▁with - ▁t - w - ur - it - ent - ▁can - ▁he - ▁but - ra - ce - ▁me - ▁b - ▁ma - ▁p - ll - ▁st - ▁one - 'on' - ▁about - th - ▁de - en - ▁all - ▁not - il - ▁g - ch - at - ▁there - ▁mo - ter - ation - tion - ▁at - ▁my - ro - ▁as - te - ▁le - ▁con - ▁like - ▁people - ▁or - ▁an - el - ▁if - ▁from - ver - ▁su - ▁co - ate - ▁these - ol - ci - ▁now - ▁see - ▁out - ▁our - ion - ▁know - ect - ▁just - as - ▁ex - ▁ch - ▁d - ▁when - ▁very - ▁think - ▁who - ▁because - ▁go - ▁up - ▁us - ▁pa - ▁no - ies - ▁di - ▁ho - om - ive - ▁get - id - ▁o - ▁hi - un - ▁how - ▁by - ir - et - ck - ity - ▁po - ul - ▁which - ▁mi - ▁some - z - ▁sp - ▁un - ▁going - ▁pro - ist - ▁se - ▁look - ▁time - ment - de - ▁more - ▁had - ng - ▁would - ge - la - ▁here - ▁really - x - ▁your - ▁them - us - me - ▁en - ▁two - ▁k - ▁li - ▁world - ne - ow - ▁way - ▁want - ▁work - ▁don - ▁lo - ▁fa - ▁were - ▁their - age - vi - ▁ha - ac - der - est - ▁bo - am - ▁other - able - ▁actually - ▁sh - ▁make - ▁ba - ▁la - ine - ▁into - ▁where - ▁could - ▁comp - ting - ▁has - ▁will - ▁ne - j - ical - ally - ▁vi - ▁things - ▁te - igh - ▁say - ▁years - ers - ▁ra - ther - ▁than - ru - ▁ro - op - ▁did - ▁any - ▁new - ound - ig - ▁well - mo - ▁she - ▁na - ▁been - he - ▁thousand - ▁car - ▁take - ▁right - ▁then - ▁need - ▁start - ▁hundred - ▁something - ▁over - ▁com - ia - ▁kind - um - if - ▁those - ▁first - ▁pre - ta - ▁said - ize - end - ▁even - ▁thing - one - ▁back - ite - ▁every - ▁little - ry - ▁life - ▁much - ke - ▁also - ▁most - ant - per - ▁three - ▁come - ▁lot - ance - ▁got - ▁talk - ▁per - ▁inter - ▁sa - ▁use - ▁mu - ▁part - ish - ence - ▁happen - ▁bi - ▁mean - ough - ▁qu - ▁bu - ▁day - ▁ga - ▁only - ▁many - ▁different - ▁dr - ▁th - ▁show - ful - ▁down - ated - ▁good - ▁tra - ▁around - ▁idea - ▁human - ous - ▁put - ▁through - ▁five - ▁why - ▁change - ▁real - ff - ible - ▁fact - ▁same - ▁jo - ▁live - ▁year - ▁problem - ▁ph - ▁four - ▁give - ▁big - ▁tell - ▁great - ▁try - ▁va - ▁ru - ▁system - ▁six - ▁plan - ▁place - ▁build - ▁called - ▁again - ▁point - ▁twenty - ▁percent - ▁nine - ▁find - ▁app - ▁after - ▁long - ▁eight - ▁imp - ▁gene - ▁design - ▁today - ▁should - ▁made - ious - ▁came - ▁learn - ▁last - ▁own - way - ▁turn - ▁seven - ▁high - ▁question - ▁person - ▁brain - ▁important - ▁another - ▁thought - ▁trans - ▁create - ness - ▁hu - ▁power - ▁act - land - ▁play - ▁sort - ▁old - ▁before - ▁course - ▁understand - ▁feel - ▁might - ▁each - ▁million - ▁better - ▁together - ▁ago - ▁example - ▁help - ▁story - ▁next - ▁hand - ▁school - ▁water - ▁develop - ▁technology - que - ▁second - ▁grow - ▁still - ▁cell - ▁believe - ▁number - ▁small - ▁between - qui - ▁data - ▁become - ▁america - ▁maybe - ▁space - ▁project - ▁organ - ▁vo - ▁children - ▁book - graph - ▁open - ▁fifty - ▁picture - ▁health - ▁thirty - ▁africa - ▁reason - ▁large - ▁hard - ▁computer - ▁always - ▁sense - ▁money - ▁women - ▁everything - ▁information - ▁country - ▁teach - ▁energy - ▁experience - ▁food - ▁process - qua - ▁interesting - ▁future - ▁science - q - '0' - '5' - '6' - '9' - '3' - '8' - '4' - N - A - '7' - S - G - F - R - L - U - E - T - H - _ - B - D - J - M - ă - ō - ť - '2' - '-' - '1' - C - <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: bpe bpemodel: data/en_token_list/bpe_unigram500/bpe.model 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: n_fft: 512 win_length: 400 hop_length: 160 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 5 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_bpe500_sp/train/feats_stats.npz 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: 1024 num_blocks: 15 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: 0.1 src_attention_dropout_rate: 0.1 preprocessor: default preprocessor_conf: {} required: - output_dir - token_list version: '202209' distributed: true ``` </details> | f3af97f757f0dfe9d765b342472b55fc |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | MultiBERTs Seed 1 Checkpoint 80k (uncased) Seed 1 intermediate checkpoint 80k 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-1](https://hf.co/multberts-seed-1). 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). | bb198938bb29e0633a9139ea9973be00 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | 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-1-80k') model = BertModel.from_pretrained("multiberts-seed-1-80k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | c3c5e98c7e59ae15b4ee12d136676073 |
mit | ['fill-mask', 'alloys', 'metallurgy'] | false | Abstract: Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously. This is modeled as a Supervised Task wherein Alloy Composition is provided for the Model to predict a desired property. Efficiency of tasks such as *Alloy Property Prediction*, Alloy Synthesis can be modeled additionally with an Unsupervised Pre-training Task. We describe the idea of Pre-training using Language Modelling kind of approach in terms of Alloy Compositions.We specifically inspect that random masking proposed in is not suitable for modelling Alloys. We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition. The results suggest that Pre-training is an important field of direction in this field of research for further improvement. | 8a88c225c0dc98765e251b79bc963b35 |
mit | ['fill-mask', 'alloys', 'metallurgy'] | false | Footnote: Work done via [MLDMM Lab](https://sites.google.com/view/mldmm-lab/home)  | 3c5fd6ee7a26930339e3ae02ece22b75 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-arabic-gpu-colab-similar-to-german-bigger-warm-up This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6370 - Wer: 0.4146 | bfe423418e7093dbabd2d8d3aad18352 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 6 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5000 - num_epochs: 40 - mixed_precision_training: Native AMP | 7f99cacb65c656e87b0497694323eec0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.4958 | 2.83 | 400 | 3.4822 | 1.0 | | 3.2281 | 5.67 | 800 | 2.9404 | 1.0 | | 2.942 | 8.51 | 1200 | 2.8690 | 1.0 | | 2.6346 | 11.35 | 1600 | 1.5452 | 0.9994 | | 1.3472 | 14.18 | 2000 | 0.8261 | 0.6853 | | 0.8972 | 17.02 | 2400 | 0.6812 | 0.5737 | | 0.6924 | 19.85 | 2800 | 0.6552 | 0.5291 | | 0.5687 | 22.69 | 3200 | 0.6108 | 0.4909 | | 0.4734 | 25.53 | 3600 | 0.5877 | 0.4674 | | 0.4029 | 28.37 | 4000 | 0.6204 | 0.4662 | | 0.3483 | 31.2 | 4400 | 0.5932 | 0.4451 | | 0.307 | 34.04 | 4800 | 0.6445 | 0.4392 | | 0.2722 | 36.88 | 5200 | 0.6126 | 0.4292 | | 0.2247 | 39.71 | 5600 | 0.6370 | 0.4146 | | 2d5730de9a654dd01bea606f3686e4bd |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/distilbert-base-nli-stsb-quora-ranking This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. | a5f2e996e3da0f475441da86f88e3701 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/distilbert-base-nli-stsb-quora-ranking') embeddings = model.encode(sentences) print(embeddings) ``` | cbb6f1ec24a311bcb0c5499baaccb1e4 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/distilbert-base-nli-stsb-quora-ranking') model = AutoModel.from_pretrained('sentence-transformers/distilbert-base-nli-stsb-quora-ranking') | 938f5e4e604916d0a53b916dc13e5ee1 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/distilbert-base-nli-stsb-quora-ranking) | 3e98b8c5a474f7cc3e20715663a7dde8 |
mit | [] | false | Retro-Girl on Stable Diffusion This is the `<retro-girl>` 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 a `style`:      | 37392625606e6acb5f812a49f7bb78c4 |
mit | ['stable-diffusion', 'text-to-image'] | false | Usage To use this model you have to download the .ckpt file as well as drop it into the "\stable-diffusion-webui\models\Stable-diffusion" folder To use it in a prompt: ```"Rebecca girl"``` for highest strength or just "Rebecca" To increase the strength put "Rebecca girl" in () brackets To decrease the strength put "Rebecca girl" in [] brackets Waifu_diffusion base trained model trained to 3,500 steps Have fun :) | c2efde139b9ccdded48dbf147fce7c27 |
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