Initial Commit
Browse files- README.md +175 -0
- config.json +159 -0
- eval_results_ml.json +1 -0
- pytorch_model.bin +3 -0
- training_args.bin +3 -0
README.md
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| 1 |
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---
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license: mit
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base_model: haryoaw/scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1
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tags:
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- generated_from_trainer
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datasets:
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- massive
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metrics:
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- accuracy
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- f1
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model-index:
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- name: scenario-KD-PO-MSV-D2_data-AmazonScience_massive_all_1_155
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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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# scenario-KD-PO-MSV-D2_data-AmazonScience_massive_all_1_155
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This model is a fine-tuned version of [haryoaw/scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1](https://huggingface.co/haryoaw/scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1) on the massive dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.1074
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- Accuracy: 0.8669
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- F1: 0.8467
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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: 5e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 55
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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: 30
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### Training results
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| 53 |
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| 54 |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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| 55 |
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|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
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| 56 |
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| 1.7059 | 0.27 | 5000 | 1.9979 | 0.8192 | 0.7823 |
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| 1.2894 | 0.53 | 10000 | 1.7859 | 0.8342 | 0.8022 |
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| 1.1214 | 0.8 | 15000 | 1.6643 | 0.8392 | 0.8118 |
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| 0.8728 | 1.07 | 20000 | 1.6370 | 0.8389 | 0.8140 |
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| 0.7934 | 1.34 | 25000 | 1.5969 | 0.8442 | 0.8171 |
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| 0.7466 | 1.6 | 30000 | 1.5752 | 0.8434 | 0.8231 |
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| 0.707 | 1.87 | 35000 | 1.5116 | 0.8482 | 0.8282 |
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| 0.6114 | 2.14 | 40000 | 1.4857 | 0.8502 | 0.8282 |
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| 0.5708 | 2.41 | 45000 | 1.4695 | 0.8512 | 0.8297 |
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| 0.5702 | 2.67 | 50000 | 1.4431 | 0.8495 | 0.8277 |
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| 0.5611 | 2.94 | 55000 | 1.4096 | 0.8554 | 0.8356 |
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| 67 |
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| 0.4894 | 3.21 | 60000 | 1.4057 | 0.8552 | 0.8353 |
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| 0.4851 | 3.47 | 65000 | 1.4022 | 0.8548 | 0.8325 |
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| 0.4802 | 3.74 | 70000 | 1.3887 | 0.8549 | 0.8339 |
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| 0.4699 | 4.01 | 75000 | 1.3778 | 0.8550 | 0.8359 |
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| 0.4361 | 4.28 | 80000 | 1.3448 | 0.8581 | 0.8379 |
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| 0.4315 | 4.54 | 85000 | 1.3343 | 0.8588 | 0.8341 |
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| 0.4145 | 4.81 | 90000 | 1.3291 | 0.8593 | 0.8361 |
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| 0.3971 | 5.08 | 95000 | 1.3136 | 0.8582 | 0.8371 |
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| 0.3976 | 5.34 | 100000 | 1.3166 | 0.8568 | 0.8398 |
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| 0.3884 | 5.61 | 105000 | 1.3187 | 0.8574 | 0.8361 |
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| 0.3797 | 5.88 | 110000 | 1.3076 | 0.8580 | 0.8352 |
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| 0.3801 | 6.15 | 115000 | 1.2889 | 0.8581 | 0.8381 |
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| 0.3735 | 6.41 | 120000 | 1.2824 | 0.8593 | 0.8394 |
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| 0.3734 | 6.68 | 125000 | 1.2736 | 0.8603 | 0.8399 |
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| 0.3752 | 6.95 | 130000 | 1.2806 | 0.8576 | 0.8341 |
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| 0.3477 | 7.22 | 135000 | 1.2640 | 0.8606 | 0.8405 |
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| 0.3464 | 7.48 | 140000 | 1.2610 | 0.8605 | 0.8390 |
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| 0.3437 | 7.75 | 145000 | 1.2585 | 0.8599 | 0.8379 |
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| 0.3329 | 8.02 | 150000 | 1.2506 | 0.8619 | 0.8399 |
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| 86 |
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| 0.3255 | 8.28 | 155000 | 1.2506 | 0.8612 | 0.8398 |
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| 0.3198 | 8.55 | 160000 | 1.2514 | 0.8611 | 0.8400 |
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| 0.3337 | 8.82 | 165000 | 1.2543 | 0.8608 | 0.8416 |
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| 0.3044 | 9.09 | 170000 | 1.2677 | 0.8585 | 0.8406 |
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| 0.3045 | 9.35 | 175000 | 1.2546 | 0.8603 | 0.8394 |
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| 0.3151 | 9.62 | 180000 | 1.2258 | 0.8616 | 0.8420 |
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| 0.3091 | 9.89 | 185000 | 1.2356 | 0.8618 | 0.8409 |
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| 0.2889 | 10.15 | 190000 | 1.2244 | 0.8613 | 0.8417 |
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| 94 |
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| 0.2931 | 10.42 | 195000 | 1.2106 | 0.8623 | 0.8423 |
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| 0.2923 | 10.69 | 200000 | 1.2272 | 0.8611 | 0.8409 |
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| 96 |
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| 0.2988 | 10.96 | 205000 | 1.2070 | 0.8632 | 0.8418 |
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| 0.2817 | 11.22 | 210000 | 1.2079 | 0.8624 | 0.8429 |
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| 0.2878 | 11.49 | 215000 | 1.2132 | 0.8633 | 0.8410 |
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| 0.2803 | 11.76 | 220000 | 1.2023 | 0.8619 | 0.8428 |
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| 0.2769 | 12.03 | 225000 | 1.2024 | 0.8621 | 0.8438 |
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| 0.2807 | 12.29 | 230000 | 1.1938 | 0.8632 | 0.8434 |
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| 0.2795 | 12.56 | 235000 | 1.2024 | 0.8632 | 0.8417 |
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| 0.277 | 12.83 | 240000 | 1.1924 | 0.8623 | 0.8419 |
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| 104 |
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| 0.2602 | 13.09 | 245000 | 1.1960 | 0.8623 | 0.8424 |
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| 105 |
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| 0.268 | 13.36 | 250000 | 1.1893 | 0.8617 | 0.8427 |
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| 106 |
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| 0.2653 | 13.63 | 255000 | 1.1890 | 0.8620 | 0.8394 |
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| 107 |
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| 0.2558 | 13.9 | 260000 | 1.1790 | 0.8634 | 0.8422 |
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| 108 |
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| 0.2602 | 14.16 | 265000 | 1.1760 | 0.8645 | 0.8429 |
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| 109 |
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| 0.256 | 14.43 | 270000 | 1.1714 | 0.8635 | 0.8442 |
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| 0.2463 | 14.7 | 275000 | 1.1855 | 0.8626 | 0.8421 |
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| 111 |
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| 0.2546 | 14.96 | 280000 | 1.1791 | 0.8640 | 0.8439 |
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| 112 |
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| 0.2499 | 15.23 | 285000 | 1.1763 | 0.8640 | 0.8451 |
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| 113 |
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| 0.2539 | 15.5 | 290000 | 1.1693 | 0.8643 | 0.8447 |
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| 114 |
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| 0.2466 | 15.77 | 295000 | 1.1607 | 0.8646 | 0.8444 |
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| 115 |
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| 0.2376 | 16.03 | 300000 | 1.1665 | 0.8637 | 0.8427 |
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| 116 |
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| 0.2397 | 16.3 | 305000 | 1.1754 | 0.8639 | 0.8441 |
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| 117 |
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| 0.2408 | 16.57 | 310000 | 1.1732 | 0.8639 | 0.8437 |
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| 118 |
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| 0.2443 | 16.84 | 315000 | 1.1621 | 0.8631 | 0.8421 |
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| 119 |
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| 0.2273 | 17.1 | 320000 | 1.1572 | 0.8646 | 0.8447 |
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| 120 |
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| 0.2314 | 17.37 | 325000 | 1.1578 | 0.8643 | 0.8438 |
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| 121 |
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| 0.2376 | 17.64 | 330000 | 1.1571 | 0.8644 | 0.8434 |
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| 122 |
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| 0.2296 | 17.9 | 335000 | 1.1504 | 0.8657 | 0.8470 |
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| 123 |
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| 0.2254 | 18.17 | 340000 | 1.1542 | 0.8640 | 0.8435 |
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| 124 |
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| 0.2305 | 18.44 | 345000 | 1.1599 | 0.8640 | 0.8427 |
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| 125 |
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| 0.2236 | 18.71 | 350000 | 1.1566 | 0.8638 | 0.8439 |
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| 126 |
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| 0.2276 | 18.97 | 355000 | 1.1425 | 0.8661 | 0.8469 |
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| 127 |
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| 0.2223 | 19.24 | 360000 | 1.1580 | 0.8648 | 0.8454 |
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| 128 |
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| 0.2242 | 19.51 | 365000 | 1.1406 | 0.8651 | 0.8455 |
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| 129 |
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| 0.2235 | 19.77 | 370000 | 1.1490 | 0.8652 | 0.8455 |
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| 130 |
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| 0.2183 | 20.04 | 375000 | 1.1342 | 0.8652 | 0.8451 |
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| 131 |
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| 0.2123 | 20.31 | 380000 | 1.1457 | 0.8649 | 0.8443 |
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| 132 |
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| 0.2162 | 20.58 | 385000 | 1.1328 | 0.8655 | 0.8452 |
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| 133 |
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| 0.2111 | 20.84 | 390000 | 1.1362 | 0.8657 | 0.8450 |
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| 134 |
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| 0.2121 | 21.11 | 395000 | 1.1349 | 0.8655 | 0.8450 |
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| 135 |
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| 0.204 | 21.38 | 400000 | 1.1332 | 0.8651 | 0.8447 |
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| 136 |
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| 0.2133 | 21.65 | 405000 | 1.1330 | 0.8642 | 0.8438 |
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| 137 |
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| 0.2115 | 21.91 | 410000 | 1.1339 | 0.8647 | 0.8440 |
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| 138 |
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| 0.2054 | 22.18 | 415000 | 1.1316 | 0.8647 | 0.8444 |
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| 139 |
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| 0.211 | 22.45 | 420000 | 1.1286 | 0.8660 | 0.8452 |
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| 140 |
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| 0.2015 | 22.71 | 425000 | 1.1290 | 0.8656 | 0.8462 |
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| 141 |
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| 0.2112 | 22.98 | 430000 | 1.1342 | 0.8654 | 0.8450 |
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| 142 |
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| 0.2016 | 23.25 | 435000 | 1.1288 | 0.8650 | 0.8453 |
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| 143 |
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| 0.1991 | 23.52 | 440000 | 1.1303 | 0.8657 | 0.8468 |
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| 144 |
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| 0.1988 | 23.78 | 445000 | 1.1238 | 0.8658 | 0.8463 |
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| 145 |
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| 0.1955 | 24.05 | 450000 | 1.1189 | 0.8664 | 0.8471 |
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| 146 |
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| 0.1964 | 24.32 | 455000 | 1.1254 | 0.8655 | 0.8447 |
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| 147 |
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| 0.2009 | 24.58 | 460000 | 1.1209 | 0.8659 | 0.8468 |
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| 148 |
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| 0.1999 | 24.85 | 465000 | 1.1166 | 0.8657 | 0.8449 |
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| 149 |
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| 0.1871 | 25.12 | 470000 | 1.1251 | 0.8657 | 0.8449 |
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| 150 |
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| 0.1934 | 25.39 | 475000 | 1.1145 | 0.8657 | 0.8446 |
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| 151 |
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| 0.1933 | 25.65 | 480000 | 1.1187 | 0.8651 | 0.8453 |
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| 152 |
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| 0.197 | 25.92 | 485000 | 1.1188 | 0.8658 | 0.8448 |
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| 153 |
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| 0.1938 | 26.19 | 490000 | 1.1176 | 0.8657 | 0.8455 |
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| 154 |
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| 0.196 | 26.46 | 495000 | 1.1221 | 0.8660 | 0.8459 |
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| 155 |
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| 0.1868 | 26.72 | 500000 | 1.1166 | 0.8660 | 0.8454 |
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| 156 |
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| 0.1917 | 26.99 | 505000 | 1.1148 | 0.8668 | 0.8468 |
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| 157 |
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| 0.1893 | 27.26 | 510000 | 1.1130 | 0.8660 | 0.8457 |
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| 158 |
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| 0.1881 | 27.52 | 515000 | 1.1138 | 0.8658 | 0.8454 |
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| 159 |
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| 0.1857 | 27.79 | 520000 | 1.1139 | 0.8662 | 0.8457 |
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| 160 |
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| 0.1904 | 28.06 | 525000 | 1.1112 | 0.8657 | 0.8457 |
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| 161 |
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| 0.186 | 28.33 | 530000 | 1.1121 | 0.8664 | 0.8454 |
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| 162 |
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| 0.186 | 28.59 | 535000 | 1.1115 | 0.8665 | 0.8471 |
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| 163 |
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| 0.1866 | 28.86 | 540000 | 1.1091 | 0.8659 | 0.8450 |
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| 164 |
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| 0.1847 | 29.13 | 545000 | 1.1094 | 0.8668 | 0.8467 |
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| 165 |
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| 0.1826 | 29.39 | 550000 | 1.1116 | 0.8662 | 0.8463 |
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| 166 |
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| 0.1831 | 29.66 | 555000 | 1.1085 | 0.8660 | 0.8459 |
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| 167 |
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| 0.1819 | 29.93 | 560000 | 1.1074 | 0.8669 | 0.8467 |
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### Framework versions
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- Transformers 4.33.3
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- Pytorch 2.1.1+cu121
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- Datasets 2.14.5
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- Tokenizers 0.13.3
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config.json
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