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esm2_t130_150M-lora-classifier_2024-04-26_00-25-40

PEFT
Safetensors
Generated from Trainer
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+ ---
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+ license: mit
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+ library_name: peft
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+ tags:
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+ - generated_from_trainer
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+ base_model: facebook/esm2_t30_150M_UR50D
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: esm2_t130_150M-lora-classifier_2024-04-26_00-25-40
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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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+ # esm2_t130_150M-lora-classifier_2024-04-26_00-25-40
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+
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+ This model is a fine-tuned version of [facebook/esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.6470
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+ - Accuracy: 0.8887
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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: 0.0005701568055793089
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+ - train_batch_size: 28
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+ - eval_batch_size: 28
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+ - seed: 8893
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - num_epochs: 300
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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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+ | 0.7096 | 1.0 | 55 | 0.6718 | 0.6055 |
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+ | 0.6769 | 2.0 | 110 | 0.6739 | 0.6055 |
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+ | 0.579 | 3.0 | 165 | 0.6608 | 0.6484 |
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+ | 0.5726 | 4.0 | 220 | 0.5777 | 0.7109 |
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+ | 0.6381 | 5.0 | 275 | 0.5020 | 0.7676 |
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+ | 0.183 | 6.0 | 330 | 0.3725 | 0.8320 |
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+ | 0.3701 | 7.0 | 385 | 0.3508 | 0.8535 |
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+ | 0.2147 | 8.0 | 440 | 0.3191 | 0.8711 |
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+ | 0.1654 | 9.0 | 495 | 0.3036 | 0.875 |
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+ | 0.1581 | 10.0 | 550 | 0.3761 | 0.8516 |
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+ | 0.3459 | 11.0 | 605 | 0.3746 | 0.8594 |
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+ | 0.3325 | 12.0 | 660 | 0.3025 | 0.8867 |
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+ | 0.1237 | 13.0 | 715 | 0.2983 | 0.8770 |
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+ | 0.5167 | 14.0 | 770 | 0.3044 | 0.8887 |
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+ | 0.3541 | 15.0 | 825 | 0.2927 | 0.8906 |
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+ | 0.0378 | 16.0 | 880 | 0.3669 | 0.8906 |
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+ | 0.062 | 17.0 | 935 | 0.3298 | 0.8887 |
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+ | 0.1695 | 18.0 | 990 | 0.2912 | 0.9004 |
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+ | 0.0444 | 19.0 | 1045 | 0.3034 | 0.9004 |
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+ | 0.1794 | 20.0 | 1100 | 0.3641 | 0.8828 |
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+ | 0.0634 | 21.0 | 1155 | 0.3521 | 0.8867 |
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+ | 0.0446 | 22.0 | 1210 | 0.3438 | 0.8887 |
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+ | 0.0266 | 23.0 | 1265 | 0.4553 | 0.8867 |
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+ | 0.2637 | 24.0 | 1320 | 0.4715 | 0.8867 |
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+ | 0.159 | 25.0 | 1375 | 0.4323 | 0.8945 |
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+ | 0.2401 | 26.0 | 1430 | 0.6019 | 0.8809 |
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+ | 0.1317 | 27.0 | 1485 | 0.5549 | 0.8906 |
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+ | 0.1223 | 28.0 | 1540 | 0.4819 | 0.8926 |
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+ | 0.0015 | 29.0 | 1595 | 0.6432 | 0.8711 |
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+ | 0.0007 | 30.0 | 1650 | 0.6480 | 0.8926 |
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+ | 0.0774 | 31.0 | 1705 | 0.7596 | 0.8926 |
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+ | 0.1262 | 32.0 | 1760 | 0.7614 | 0.8809 |
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+ | 0.034 | 33.0 | 1815 | 0.7392 | 0.8789 |
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+ | 0.0021 | 34.0 | 1870 | 0.9068 | 0.8848 |
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+ | 0.0003 | 35.0 | 1925 | 0.8724 | 0.8711 |
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+ | 0.0001 | 36.0 | 1980 | 0.9483 | 0.8867 |
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+ | 0.0127 | 37.0 | 2035 | 0.9638 | 0.8828 |
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+ | 0.0001 | 38.0 | 2090 | 0.9105 | 0.8926 |
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+ | 0.0001 | 39.0 | 2145 | 0.9231 | 0.8809 |
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+ | 0.0008 | 40.0 | 2200 | 1.0224 | 0.8867 |
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+ | 0.0001 | 41.0 | 2255 | 1.0666 | 0.8848 |
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+ | 0.0002 | 42.0 | 2310 | 1.1028 | 0.8848 |
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+ | 0.0 | 43.0 | 2365 | 0.9653 | 0.8906 |
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+ | 0.0006 | 44.0 | 2420 | 1.1108 | 0.8848 |
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+ | 0.0001 | 45.0 | 2475 | 1.2919 | 0.8730 |
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+ | 0.0002 | 46.0 | 2530 | 1.0834 | 0.8926 |
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+ | 0.0002 | 47.0 | 2585 | 1.1240 | 0.8887 |
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+ | 0.0135 | 48.0 | 2640 | 1.1466 | 0.8887 |
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+ | 0.0008 | 49.0 | 2695 | 1.2674 | 0.8691 |
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+ | 0.0 | 50.0 | 2750 | 1.1311 | 0.8887 |
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+ | 0.0086 | 51.0 | 2805 | 1.0957 | 0.8887 |
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+ | 0.0 | 52.0 | 2860 | 1.1336 | 0.8789 |
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+ | 0.0007 | 53.0 | 2915 | 1.1494 | 0.875 |
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+ | 0.0002 | 54.0 | 2970 | 1.0790 | 0.8848 |
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+ | 0.0002 | 55.0 | 3025 | 1.1489 | 0.8809 |
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+ | 0.0 | 56.0 | 3080 | 1.1479 | 0.8867 |
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+ | 0.0022 | 57.0 | 3135 | 1.2092 | 0.8848 |
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+ | 0.2415 | 58.0 | 3190 | 1.2060 | 0.8848 |
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+ | 0.7813 | 59.0 | 3245 | 1.3750 | 0.8613 |
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+ | 0.0 | 60.0 | 3300 | 1.1202 | 0.875 |
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+ | 0.0 | 61.0 | 3355 | 1.0502 | 0.8848 |
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+ | 0.0 | 62.0 | 3410 | 1.3270 | 0.8730 |
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+ | 0.0015 | 63.0 | 3465 | 1.0082 | 0.875 |
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+ | 0.0002 | 64.0 | 3520 | 0.9724 | 0.8867 |
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+ | 0.0014 | 65.0 | 3575 | 1.0862 | 0.8770 |
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+ | 0.0002 | 66.0 | 3630 | 1.1366 | 0.8730 |
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+ | 0.1868 | 67.0 | 3685 | 1.1838 | 0.8770 |
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+ | 0.0004 | 68.0 | 3740 | 1.2073 | 0.875 |
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+ | 0.0007 | 69.0 | 3795 | 1.1793 | 0.8770 |
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+ | 0.2838 | 71.0 | 3905 | 1.2415 | 0.875 |
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+ | 0.0 | 72.0 | 3960 | 1.2346 | 0.8770 |
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+ | 0.0041 | 73.0 | 4015 | 1.0830 | 0.8789 |
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+ | 0.0055 | 74.0 | 4070 | 1.0731 | 0.8867 |
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+ | 0.0 | 75.0 | 4125 | 1.4096 | 0.8652 |
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+ | 0.0034 | 76.0 | 4180 | 1.1142 | 0.8711 |
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+ | 0.0 | 77.0 | 4235 | 1.0250 | 0.8848 |
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+ | 0.0002 | 78.0 | 4290 | 1.0700 | 0.8691 |
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+ | 0.0009 | 79.0 | 4345 | 0.9032 | 0.8789 |
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+ | 0.0001 | 80.0 | 4400 | 1.0556 | 0.8730 |
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+ | 0.0001 | 81.0 | 4455 | 1.0740 | 0.8770 |
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+ | 0.0002 | 82.0 | 4510 | 1.2571 | 0.8691 |
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+ | 0.0 | 83.0 | 4565 | 1.2007 | 0.8809 |
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+ | 0.0 | 84.0 | 4620 | 1.2515 | 0.875 |
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+ | 0.0 | 91.0 | 5005 | 1.3026 | 0.8848 |
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+ | 0.0 | 93.0 | 5115 | 1.4069 | 0.875 |
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+ | 0.0 | 94.0 | 5170 | 1.3988 | 0.8770 |
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+ | 0.0 | 95.0 | 5225 | 1.3675 | 0.8594 |
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+ | 0.0 | 96.0 | 5280 | 1.3366 | 0.8770 |
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+ | 0.0003 | 97.0 | 5335 | 1.2140 | 0.8848 |
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+ | 0.0 | 98.0 | 5390 | 1.3585 | 0.8711 |
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+ | 0.0 | 99.0 | 5445 | 1.1665 | 0.8672 |
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+ | 0.0 | 100.0 | 5500 | 1.0947 | 0.8809 |
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+ | 0.0099 | 101.0 | 5555 | 1.2993 | 0.8730 |
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+ | 0.0 | 102.0 | 5610 | 1.3578 | 0.8789 |
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+ | 0.0 | 103.0 | 5665 | 1.3596 | 0.8867 |
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+ | 0.0006 | 104.0 | 5720 | 1.3164 | 0.8848 |
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+ | 0.0 | 105.0 | 5775 | 1.4100 | 0.8770 |
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+ | 0.0 | 106.0 | 5830 | 1.3459 | 0.875 |
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+ | 0.0005 | 107.0 | 5885 | 1.3783 | 0.8809 |
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+ | 0.0 | 109.0 | 5995 | 1.3933 | 0.8848 |
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+ | 0.0 | 110.0 | 6050 | 1.3813 | 0.8809 |
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+ | 0.0 | 111.0 | 6105 | 1.5747 | 0.875 |
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+ | 0.0001 | 112.0 | 6160 | 1.3368 | 0.8867 |
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+ | 0.1476 | 114.0 | 6270 | 1.4943 | 0.8828 |
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+ | 0.0002 | 115.0 | 6325 | 1.4725 | 0.8789 |
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+ | 0.0 | 116.0 | 6380 | 1.4614 | 0.875 |
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+ | 0.0 | 118.0 | 6490 | 1.4459 | 0.8848 |
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+ | 0.0026 | 119.0 | 6545 | 1.4150 | 0.8730 |
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+ | 0.0 | 120.0 | 6600 | 1.6055 | 0.8555 |
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+ | 0.0001 | 121.0 | 6655 | 1.3710 | 0.8789 |
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+ | 0.3319 | 122.0 | 6710 | 1.3940 | 0.8867 |
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+ | 0.0001 | 123.0 | 6765 | 1.2486 | 0.875 |
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+ | 0.0002 | 124.0 | 6820 | 1.2946 | 0.8711 |
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+ | 0.0001 | 131.0 | 7205 | 1.2939 | 0.8730 |
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+ | 0.0 | 214.0 | 11770 | 1.6562 | 0.8789 |
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+ | 0.0005 | 215.0 | 11825 | 1.6297 | 0.8887 |
269
+ | 0.0 | 216.0 | 11880 | 1.6070 | 0.8809 |
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+ | 0.0 | 247.0 | 13585 | 1.6896 | 0.8828 |
301
+ | 0.0 | 248.0 | 13640 | 1.6900 | 0.8828 |
302
+ | 0.0 | 249.0 | 13695 | 1.6906 | 0.8848 |
303
+ | 0.0 | 250.0 | 13750 | 1.6928 | 0.8828 |
304
+ | 0.0 | 251.0 | 13805 | 1.6943 | 0.8828 |
305
+ | 0.0 | 252.0 | 13860 | 1.6902 | 0.8789 |
306
+ | 0.0 | 253.0 | 13915 | 1.6638 | 0.8887 |
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+ | 0.0 | 254.0 | 13970 | 1.6632 | 0.8867 |
308
+ | 0.0 | 255.0 | 14025 | 1.6627 | 0.8867 |
309
+ | 0.0 | 256.0 | 14080 | 1.6631 | 0.8867 |
310
+ | 0.0 | 257.0 | 14135 | 1.6626 | 0.8867 |
311
+ | 0.0 | 258.0 | 14190 | 1.6629 | 0.8867 |
312
+ | 0.0 | 259.0 | 14245 | 1.6617 | 0.8867 |
313
+ | 0.0 | 260.0 | 14300 | 1.6606 | 0.8867 |
314
+ | 0.0 | 261.0 | 14355 | 1.6598 | 0.8867 |
315
+ | 0.0 | 262.0 | 14410 | 1.6559 | 0.8867 |
316
+ | 0.0 | 263.0 | 14465 | 1.6564 | 0.8867 |
317
+ | 0.0 | 264.0 | 14520 | 1.6555 | 0.8867 |
318
+ | 0.0 | 265.0 | 14575 | 1.6588 | 0.8867 |
319
+ | 0.0 | 266.0 | 14630 | 1.6565 | 0.8867 |
320
+ | 0.0 | 267.0 | 14685 | 1.6558 | 0.8867 |
321
+ | 0.0 | 268.0 | 14740 | 1.6564 | 0.8848 |
322
+ | 0.0 | 269.0 | 14795 | 1.6578 | 0.8848 |
323
+ | 0.0 | 270.0 | 14850 | 1.6566 | 0.8848 |
324
+ | 0.0 | 271.0 | 14905 | 1.6560 | 0.8867 |
325
+ | 0.0 | 272.0 | 14960 | 1.6587 | 0.8848 |
326
+ | 0.0 | 273.0 | 15015 | 1.6575 | 0.8867 |
327
+ | 0.0 | 274.0 | 15070 | 1.6575 | 0.8848 |
328
+ | 0.0 | 275.0 | 15125 | 1.6570 | 0.8867 |
329
+ | 0.0 | 276.0 | 15180 | 1.6586 | 0.8848 |
330
+ | 0.0 | 277.0 | 15235 | 1.6572 | 0.8887 |
331
+ | 0.0 | 278.0 | 15290 | 1.6577 | 0.8848 |
332
+ | 0.0 | 279.0 | 15345 | 1.6570 | 0.8867 |
333
+ | 0.0 | 280.0 | 15400 | 1.6567 | 0.8887 |
334
+ | 0.0 | 281.0 | 15455 | 1.6548 | 0.8887 |
335
+ | 0.0 | 282.0 | 15510 | 1.6558 | 0.8867 |
336
+ | 0.0 | 283.0 | 15565 | 1.6505 | 0.8887 |
337
+ | 0.0 | 284.0 | 15620 | 1.6515 | 0.8887 |
338
+ | 0.0 | 285.0 | 15675 | 1.6513 | 0.8887 |
339
+ | 0.0 | 286.0 | 15730 | 1.6456 | 0.8887 |
340
+ | 0.0 | 287.0 | 15785 | 1.6471 | 0.8887 |
341
+ | 0.0 | 288.0 | 15840 | 1.6451 | 0.8887 |
342
+ | 0.0 | 289.0 | 15895 | 1.6468 | 0.8887 |
343
+ | 0.0 | 290.0 | 15950 | 1.6470 | 0.8887 |
344
+ | 0.0 | 291.0 | 16005 | 1.6448 | 0.8887 |
345
+ | 0.0 | 292.0 | 16060 | 1.6478 | 0.8887 |
346
+ | 0.0 | 293.0 | 16115 | 1.6475 | 0.8887 |
347
+ | 0.0 | 294.0 | 16170 | 1.6471 | 0.8887 |
348
+ | 0.0 | 295.0 | 16225 | 1.6476 | 0.8887 |
349
+ | 0.0 | 296.0 | 16280 | 1.6475 | 0.8887 |
350
+ | 0.0 | 297.0 | 16335 | 1.6460 | 0.8887 |
351
+ | 0.0 | 298.0 | 16390 | 1.6471 | 0.8887 |
352
+ | 0.0 | 299.0 | 16445 | 1.6469 | 0.8887 |
353
+ | 0.0 | 300.0 | 16500 | 1.6470 | 0.8887 |
354
+
355
+
356
+ ### Framework versions
357
+
358
+ - PEFT 0.10.0
359
+ - Transformers 4.39.3
360
+ - Pytorch 2.2.1
361
+ - Datasets 2.16.1
362
+ - Tokenizers 0.15.2
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