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--- |
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library_name: transformers |
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base_model: Rostlab/prot_t5_xl_uniref50 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: msa_prot_t5_repr_seq |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# msa_prot_t5_repr_seq |
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This model is a fine-tuned version of [Rostlab/prot_t5_xl_uniref50](https://huggingface.co/Rostlab/prot_t5_xl_uniref50) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.8396 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| No log | 1.0 | 10 | 2.9787 | |
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| No log | 2.0 | 20 | 2.9960 | |
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| No log | 3.0 | 30 | 2.9192 | |
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| No log | 4.0 | 40 | 2.9534 | |
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| 3.0706 | 5.0 | 50 | 2.9662 | |
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| 3.0706 | 6.0 | 60 | 2.9160 | |
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| 3.0706 | 7.0 | 70 | 2.9198 | |
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| 3.0706 | 8.0 | 80 | 2.9258 | |
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| 3.0706 | 9.0 | 90 | 2.8992 | |
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| 2.9097 | 10.0 | 100 | 2.8073 | |
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| 2.9097 | 11.0 | 110 | 2.8701 | |
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| 2.9097 | 12.0 | 120 | 2.8366 | |
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| 2.9097 | 13.0 | 130 | 2.7131 | |
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| 2.9097 | 14.0 | 140 | 2.7704 | |
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| 2.8396 | 15.0 | 150 | 2.9375 | |
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| 2.8396 | 16.0 | 160 | 2.7965 | |
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| 2.8396 | 17.0 | 170 | 2.7563 | |
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| 2.8396 | 18.0 | 180 | 2.8374 | |
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| 2.8396 | 19.0 | 190 | 2.7491 | |
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| 2.8057 | 20.0 | 200 | 2.6914 | |
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| 2.8057 | 21.0 | 210 | 2.7746 | |
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| 2.8057 | 22.0 | 220 | 2.8187 | |
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| 2.8057 | 23.0 | 230 | 2.9719 | |
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| 2.8057 | 24.0 | 240 | 2.8489 | |
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| 2.8127 | 25.0 | 250 | 2.8719 | |
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| 2.8127 | 26.0 | 260 | 2.8749 | |
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| 2.8127 | 27.0 | 270 | 2.7897 | |
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| 2.8127 | 28.0 | 280 | 2.8159 | |
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| 2.8127 | 29.0 | 290 | 2.8765 | |
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| 2.7912 | 30.0 | 300 | 2.7582 | |
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| 2.7912 | 31.0 | 310 | 2.7970 | |
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| 2.7912 | 32.0 | 320 | 2.8463 | |
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| 2.7912 | 33.0 | 330 | 2.8521 | |
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| 2.7912 | 34.0 | 340 | 2.7665 | |
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| 2.8258 | 35.0 | 350 | 2.7878 | |
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| 2.8258 | 36.0 | 360 | 2.8995 | |
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| 2.8258 | 37.0 | 370 | 3.0310 | |
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| 2.8258 | 38.0 | 380 | 2.9792 | |
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| 2.8258 | 39.0 | 390 | 2.8650 | |
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| 2.908 | 40.0 | 400 | 2.8697 | |
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| 2.908 | 41.0 | 410 | 2.9299 | |
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| 2.908 | 42.0 | 420 | 2.7992 | |
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| 2.908 | 43.0 | 430 | 2.9172 | |
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| 2.908 | 44.0 | 440 | 2.8923 | |
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| 2.8984 | 45.0 | 450 | 2.8248 | |
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| 2.8984 | 46.0 | 460 | 2.9112 | |
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| 2.8984 | 47.0 | 470 | 2.8829 | |
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| 2.8984 | 48.0 | 480 | 2.8336 | |
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| 2.8984 | 49.0 | 490 | 2.7418 | |
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| 2.8658 | 50.0 | 500 | 2.7437 | |
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| 2.8658 | 51.0 | 510 | 2.7814 | |
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| 2.8658 | 52.0 | 520 | 2.8369 | |
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| 2.8658 | 53.0 | 530 | 2.8406 | |
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| 2.8658 | 54.0 | 540 | 2.8157 | |
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| 2.8376 | 55.0 | 550 | 2.9553 | |
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| 2.8376 | 56.0 | 560 | 2.7017 | |
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| 2.8376 | 57.0 | 570 | 2.8666 | |
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| 2.8376 | 58.0 | 580 | 2.7793 | |
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| 2.8376 | 59.0 | 590 | 2.9166 | |
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| 2.8294 | 60.0 | 600 | 2.7619 | |
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| 2.8294 | 61.0 | 610 | 2.9795 | |
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| 2.8294 | 62.0 | 620 | 2.7319 | |
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| 2.8294 | 63.0 | 630 | 2.9738 | |
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| 2.8294 | 64.0 | 640 | 2.8191 | |
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| 2.8127 | 65.0 | 650 | 2.8016 | |
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| 2.8127 | 66.0 | 660 | 3.0365 | |
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| 2.8127 | 67.0 | 670 | 2.7354 | |
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| 2.8127 | 68.0 | 680 | 3.0375 | |
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| 2.8127 | 69.0 | 690 | 2.6959 | |
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| 2.8177 | 70.0 | 700 | 3.0138 | |
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| 2.8177 | 71.0 | 710 | 2.8042 | |
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| 2.8177 | 72.0 | 720 | 2.8472 | |
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| 2.8177 | 73.0 | 730 | 3.0400 | |
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| 2.8177 | 74.0 | 740 | 2.7783 | |
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| 2.7711 | 75.0 | 750 | 2.8213 | |
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| 2.7711 | 76.0 | 760 | 2.7525 | |
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| 2.7711 | 77.0 | 770 | 2.8102 | |
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| 2.7711 | 78.0 | 780 | 3.0207 | |
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| 2.7711 | 79.0 | 790 | 2.9376 | |
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| 2.7756 | 80.0 | 800 | 2.9294 | |
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| 2.7756 | 81.0 | 810 | 3.0247 | |
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| 2.7756 | 82.0 | 820 | 2.9156 | |
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| 2.7756 | 83.0 | 830 | 2.9402 | |
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| 2.7756 | 84.0 | 840 | 2.7519 | |
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| 2.7855 | 85.0 | 850 | 2.8340 | |
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| 2.7855 | 86.0 | 860 | 2.8383 | |
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| 2.7855 | 87.0 | 870 | 2.8201 | |
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| 2.7855 | 88.0 | 880 | 3.0234 | |
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| 2.7855 | 89.0 | 890 | 2.8864 | |
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| 2.7698 | 90.0 | 900 | 2.8733 | |
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| 2.7698 | 91.0 | 910 | 2.9433 | |
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| 2.7698 | 92.0 | 920 | 2.7214 | |
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| 2.7698 | 93.0 | 930 | 2.9910 | |
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| 2.7698 | 94.0 | 940 | 2.6898 | |
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| 2.7683 | 95.0 | 950 | 2.9439 | |
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| 2.7683 | 96.0 | 960 | 2.9992 | |
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| 2.7683 | 97.0 | 970 | 3.0757 | |
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| 2.7683 | 98.0 | 980 | 3.0063 | |
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| 2.7683 | 99.0 | 990 | 3.0445 | |
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| 2.7727 | 100.0 | 1000 | 2.8396 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.7.0+cu126 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
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