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AAVesm2_650M_800_v1

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  1. README.md +100 -0
  2. all_results.json +15 -0
  3. checkpoint-13650/config.json +30 -0
  4. checkpoint-13650/optimizer.pt +3 -0
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  35. train_results.json +8 -0
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  37. training_args.bin +3 -0
  38. vocab.txt +33 -0
README.md ADDED
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+ ---
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+ license: mit
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: output_AAVESM2_650M_v1
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+ results: []
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+ ---
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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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+
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+ # output_AAVESM2_650M_v1
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+
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+ This model is a fine-tuned version of [facebook/esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3707
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+ - Accuracy: 0.8905
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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+ - train_batch_size: 1
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+ - eval_batch_size: 1
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+ - seed: 42
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+ - distributed_type: multi-GPU
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+ - num_devices: 2
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 8
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+ - total_eval_batch_size: 2
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 36.0
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | No log | 1.0 | 390 | 1.3347 | 0.5993 |
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+ | 1.5408 | 2.0 | 780 | 1.0699 | 0.6796 |
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+ | 1.1283 | 3.0 | 1170 | 0.8751 | 0.7373 |
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+ | 0.9078 | 4.0 | 1560 | 0.7534 | 0.7711 |
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+ | 0.9078 | 5.0 | 1950 | 0.6711 | 0.8022 |
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+ | 0.7705 | 6.0 | 2340 | 0.6078 | 0.8169 |
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+ | 0.6863 | 7.0 | 2730 | 0.5668 | 0.8318 |
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+ | 0.6277 | 8.0 | 3120 | 0.5461 | 0.8386 |
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+ | 0.5863 | 9.0 | 3510 | 0.5143 | 0.8514 |
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+ | 0.5863 | 10.0 | 3900 | 0.4992 | 0.8522 |
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+ | 0.5564 | 11.0 | 4290 | 0.4940 | 0.8533 |
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+ | 0.5199 | 12.0 | 4680 | 0.4727 | 0.8633 |
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+ | 0.5025 | 13.0 | 5070 | 0.4586 | 0.8638 |
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+ | 0.5025 | 14.0 | 5460 | 0.4549 | 0.8673 |
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+ | 0.4814 | 15.0 | 5850 | 0.4442 | 0.8698 |
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+ | 0.4746 | 16.0 | 6240 | 0.4306 | 0.8750 |
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+ | 0.4527 | 17.0 | 6630 | 0.4291 | 0.8742 |
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+ | 0.4382 | 18.0 | 7020 | 0.4213 | 0.8751 |
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+ | 0.4382 | 19.0 | 7410 | 0.4193 | 0.8751 |
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+ | 0.4328 | 20.0 | 7800 | 0.4143 | 0.8760 |
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+ | 0.4191 | 21.0 | 8190 | 0.4071 | 0.8836 |
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+ | 0.4106 | 22.0 | 8580 | 0.3980 | 0.8819 |
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+ | 0.4106 | 23.0 | 8970 | 0.3987 | 0.8822 |
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+ | 0.4037 | 24.0 | 9360 | 0.4027 | 0.8819 |
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+ | 0.3893 | 25.0 | 9750 | 0.3868 | 0.8893 |
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+ | 0.3991 | 26.0 | 10140 | 0.3882 | 0.8846 |
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+ | 0.3786 | 27.0 | 10530 | 0.3939 | 0.8859 |
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+ | 0.3786 | 28.0 | 10920 | 0.3959 | 0.8848 |
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+ | 0.38 | 29.0 | 11310 | 0.3950 | 0.8850 |
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+ | 0.3764 | 30.0 | 11700 | 0.3783 | 0.8893 |
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+ | 0.3708 | 31.0 | 12090 | 0.3799 | 0.8891 |
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+ | 0.3708 | 32.0 | 12480 | 0.3915 | 0.8867 |
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+ | 0.3656 | 33.0 | 12870 | 0.3780 | 0.8903 |
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+ | 0.3617 | 34.0 | 13260 | 0.3805 | 0.8874 |
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+ | 0.361 | 35.0 | 13650 | 0.3776 | 0.8920 |
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+ | 0.3595 | 36.0 | 14040 | 0.3712 | 0.8888 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.1
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+ - Pytorch 1.13.1+cu117
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+ - Datasets 2.9.0
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+ - Tokenizers 0.13.2
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14
+ D
15
+ P
16
+ K
17
+ Q
18
+ N
19
+ F
20
+ Y
21
+ M
22
+ H
23
+ W
24
+ C
25
+ X
26
+ B
27
+ U
28
+ Z
29
+ O
30
+ .
31
+ -
32
+ <null_1>
33
+ <mask>