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T5La-Large-WeightedLoss

Text Generation
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t5la
Generated from Trainer
Eval Results (legacy)
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  1. README.md +180 -0
  2. emissions.csv +2 -0
  3. model.safetensors +1 -1
  4. runs/Oct12_14-07-38_gpu25.viking2.yor.alces.network/events.out.tfevents.1760274708.gpu25.viking2.yor.alces.network.633058.0 +2 -2
README.md ADDED
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+ ---
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+ library_name: transformers
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - generator
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: T5La-Large-WeightedLoss
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+ results:
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+ - task:
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+ name: Sequence-to-sequence Language Modeling
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+ type: text2text-generation
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+ dataset:
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+ name: generator
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+ type: generator
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+ config: default
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+ split: train
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.039719373776908026
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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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+ # T5La-Large-WeightedLoss
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+
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+ This model is a fine-tuned version of [](https://huggingface.co/) on the generator dataset.
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+ It achieves the following results on the evaluation set:
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+ - Perplexity: 59.9055
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+ - Loss: 4.0928
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+ - Accuracy: 0.0397
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+ - Lookahead Perplexity: 743.3574
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+ - Lookahead Loss: 6.6112
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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: 5e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - optimizer: Use adamw_torch_fused 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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+ - training_steps: 524288
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Accuracy | Lookahead Loss | Lookahead Perplexity | Validation Loss | Perplexity |
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+ |:-------------:|:------:|:------:|:--------:|:--------------:|:--------------------:|:---------------:|:----------:|
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+ | 4.6301 | 0.0095 | 5000 | 0.0312 | 7.2366 | 1389.3693 | 4.5864 | 98.1387 |
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+ | 4.4871 | 0.0191 | 10000 | 0.0318 | 7.1133 | 1228.2065 | 4.4621 | 86.6700 |
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+ | 4.4417 | 0.0286 | 15000 | 0.0340 | 7.0459 | 1148.1578 | 4.3906 | 80.6868 |
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+ | 4.3978 | 0.0381 | 20000 | 0.0352 | 6.9924 | 1088.3650 | 4.3427 | 76.9114 |
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+ | 4.3419 | 0.0477 | 25000 | 0.0349 | 6.9566 | 1050.0462 | 4.3110 | 74.5165 |
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+ | 4.316 | 0.0572 | 30000 | 0.0361 | 6.9482 | 1041.2903 | 4.3202 | 75.2024 |
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+ | 4.3488 | 0.0668 | 35000 | 0.0346 | 6.9130 | 1005.3051 | 4.2879 | 72.8156 |
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+ | 4.3213 | 0.0763 | 40000 | 0.0347 | 6.8934 | 985.7009 | 4.2729 | 71.7300 |
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+ | 4.2945 | 0.0858 | 45000 | 0.0337 | 6.8687 | 961.7435 | 4.2490 | 70.0335 |
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+ | 4.3156 | 0.0954 | 50000 | 0.0363 | 6.8545 | 948.1267 | 4.2447 | 69.7315 |
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+ | 4.3335 | 0.1049 | 55000 | 0.0359 | 6.8432 | 937.4398 | 4.2384 | 69.2975 |
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+ | 4.2959 | 0.1144 | 60000 | 0.0374 | 6.8479 | 941.9400 | 4.2551 | 70.4671 |
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+ | 4.2732 | 0.1240 | 65000 | 0.0372 | 6.8335 | 928.3968 | 4.2418 | 69.5327 |
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+ | 4.2924 | 0.1335 | 70000 | 0.0384 | 6.8230 | 918.7655 | 4.2447 | 69.7361 |
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+ | 4.2923 | 0.1431 | 75000 | 0.0364 | 6.8392 | 933.7382 | 4.2519 | 70.2380 |
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+ | 4.2252 | 0.1526 | 80000 | 0.0371 | 6.8151 | 911.5434 | 4.2382 | 69.2813 |
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+ | 4.3282 | 0.1621 | 85000 | 0.0372 | 6.8016 | 899.2575 | 4.2262 | 68.4571 |
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+ | 4.2749 | 0.1717 | 90000 | 0.0379 | 6.7829 | 882.5851 | 4.2115 | 67.4584 |
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+ | 4.2947 | 0.1812 | 95000 | 0.0375 | 6.7889 | 887.9544 | 4.2235 | 68.2707 |
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+ | 4.2664 | 0.1907 | 100000 | 0.0377 | 6.7943 | 892.7447 | 4.2326 | 68.8933 |
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+ | 4.295 | 0.2003 | 105000 | 0.0370 | 6.7986 | 896.5748 | 4.2416 | 69.5213 |
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+ | 4.2937 | 0.2098 | 110000 | 0.0380 | 6.7741 | 874.8497 | 4.2202 | 68.0471 |
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+ | 4.2807 | 0.2193 | 115000 | 0.0391 | 6.7792 | 879.3644 | 4.2272 | 68.5222 |
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+ | 4.3136 | 0.2289 | 120000 | 0.0384 | 6.7866 | 885.9135 | 4.2357 | 69.1117 |
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+ | 4.3081 | 0.2384 | 125000 | 0.0381 | 6.7903 | 889.1959 | 4.2430 | 69.6157 |
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+ | 4.3328 | 0.2480 | 130000 | 0.0396 | 6.7835 | 883.1839 | 4.2406 | 69.4502 |
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+ | 4.3221 | 0.2575 | 135000 | 0.0400 | 6.7754 | 876.0138 | 4.2366 | 69.1692 |
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+ | 4.3163 | 0.2670 | 140000 | 0.0389 | 6.7802 | 880.2415 | 4.2404 | 69.4384 |
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+ | 4.2954 | 0.2766 | 145000 | 0.0381 | 6.7683 | 869.8558 | 4.2300 | 68.7163 |
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+ | 4.3153 | 0.2861 | 150000 | 0.0398 | 6.7570 | 860.0185 | 4.2200 | 68.0362 |
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+ | 4.2762 | 0.2956 | 155000 | 0.0391 | 6.7518 | 855.6387 | 4.2128 | 67.5476 |
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+ | 4.2877 | 0.3052 | 160000 | 0.0382 | 6.7626 | 864.9080 | 4.2276 | 68.5495 |
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+ | 4.3023 | 0.3147 | 165000 | 0.0395 | 6.7556 | 858.8960 | 4.2232 | 68.2494 |
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+ | 4.337 | 0.3242 | 170000 | 0.0396 | 6.7766 | 877.0693 | 4.2479 | 69.9564 |
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+ | 4.2891 | 0.3338 | 175000 | 0.0383 | 6.7645 | 866.5396 | 4.2347 | 69.0438 |
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+ | 4.3681 | 0.3433 | 180000 | 0.0395 | 6.7677 | 869.3163 | 4.2391 | 69.3437 |
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+ | 4.3192 | 0.3529 | 185000 | 0.0390 | 6.7627 | 864.9563 | 4.2324 | 68.8804 |
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+ | 4.2911 | 0.3624 | 190000 | 0.0382 | 6.7589 | 861.7325 | 4.2307 | 68.7676 |
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+ | 4.332 | 0.3719 | 195000 | 0.0394 | 6.7499 | 853.9386 | 4.2234 | 68.2637 |
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+ | 4.311 | 0.3815 | 200000 | 0.0392 | 6.7693 | 870.7021 | 4.2438 | 69.6714 |
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+ | 4.2935 | 0.3910 | 205000 | 0.0388 | 6.7643 | 866.3254 | 4.2368 | 69.1861 |
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+ | 4.3381 | 0.4005 | 210000 | 0.0398 | 6.7418 | 847.1075 | 4.2172 | 67.8446 |
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+ | 4.2862 | 0.4101 | 215000 | 0.0387 | 6.7396 | 845.2019 | 4.2118 | 67.4806 |
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+ | 4.2843 | 0.4196 | 220000 | 0.0397 | 6.7369 | 842.9649 | 4.2114 | 67.4486 |
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+ | 4.3212 | 0.4292 | 225000 | 0.0399 | 6.7340 | 840.4940 | 4.2066 | 67.1268 |
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+ | 4.2976 | 0.4387 | 230000 | 0.0390 | 6.7264 | 834.1246 | 4.2024 | 66.8453 |
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+ | 4.322 | 0.4482 | 235000 | 0.0370 | 6.7456 | 850.2819 | 4.2172 | 67.8427 |
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+ | 4.2604 | 0.4578 | 240000 | 0.0389 | 6.7255 | 833.4224 | 4.2010 | 66.7518 |
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+ | 4.2827 | 0.4673 | 245000 | 0.0374 | 6.7311 | 838.0625 | 4.2070 | 67.1551 |
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+ | 4.2629 | 0.4768 | 250000 | 0.0398 | 6.7232 | 831.4937 | 4.2016 | 66.7902 |
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+ | 4.2719 | 0.4864 | 255000 | 0.0387 | 6.7234 | 831.6196 | 4.2018 | 66.8038 |
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+ | 4.2518 | 0.4959 | 260000 | 0.0392 | 6.7199 | 828.7639 | 4.1976 | 66.5268 |
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+ | 4.2439 | 0.5054 | 265000 | 0.0381 | 6.7225 | 830.8789 | 4.1985 | 66.5882 |
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+ | 4.2885 | 0.5245 | 275000 | 0.0394 | 6.7120 | 822.2442 | 4.1908 | 66.0741 |
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+ | 4.2603 | 0.5341 | 280000 | 0.0392 | 6.7111 | 821.5043 | 4.1885 | 65.9229 |
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+ | 4.2623 | 0.5436 | 285000 | 0.0399 | 6.7100 | 820.5507 | 4.1877 | 65.8688 |
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+ | 4.2669 | 0.5531 | 290000 | 0.0385 | 6.7113 | 821.6451 | 4.1869 | 65.8204 |
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+ | 4.3208 | 0.5627 | 295000 | 0.0400 | 6.6984 | 811.1413 | 4.1780 | 65.2359 |
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+ | 4.249 | 0.5722 | 300000 | 0.0397 | 6.6998 | 812.2105 | 4.1786 | 65.2716 |
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+ | 4.2383 | 0.5817 | 305000 | 0.0390 | 6.6950 | 808.3597 | 4.1740 | 64.9756 |
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+ | 4.2473 | 0.5913 | 310000 | 0.0387 | 6.7067 | 817.9059 | 4.1827 | 65.5417 |
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+ | 4.2612 | 0.6008 | 315000 | 0.0391 | 6.6928 | 806.6095 | 4.1715 | 64.8147 |
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+ | 4.1997 | 0.6104 | 320000 | 0.0392 | 6.6874 | 802.2146 | 4.1658 | 64.4451 |
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+ | 4.2437 | 0.6199 | 325000 | 0.0400 | 6.6893 | 803.7320 | 4.1681 | 64.5955 |
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+ | 4.2243 | 0.6294 | 330000 | 0.0399 | 6.6818 | 797.7855 | 4.1615 | 64.1649 |
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+ | 4.2524 | 0.6390 | 335000 | 0.0399 | 6.6764 | 793.4797 | 4.1580 | 63.9440 |
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+ | 4.2496 | 0.6485 | 340000 | 0.0396 | 6.6827 | 798.4811 | 4.1633 | 64.2805 |
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+ | 4.2444 | 0.6580 | 345000 | 0.0394 | 6.6818 | 797.7289 | 4.1606 | 64.1105 |
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+ | 4.2127 | 0.6676 | 350000 | 0.0402 | 6.6686 | 787.2920 | 4.1505 | 63.4653 |
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+ | 4.2095 | 0.6771 | 355000 | 0.0390 | 6.6778 | 794.5674 | 4.1568 | 63.8654 |
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+ | 4.2228 | 0.6866 | 360000 | 0.0392 | 6.6670 | 786.0658 | 4.1472 | 63.2587 |
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+ | 4.2044 | 0.6962 | 365000 | 0.0397 | 6.6652 | 784.5846 | 4.1455 | 63.1497 |
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+ | 4.2489 | 0.7057 | 370000 | 0.0395 | 6.6617 | 781.8825 | 4.1434 | 63.0145 |
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+ | 4.2101 | 0.7153 | 375000 | 0.0396 | 6.6568 | 778.0572 | 4.1384 | 62.7031 |
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+ | 4.2117 | 0.7248 | 380000 | 0.0395 | 6.6607 | 781.1064 | 4.1406 | 62.8427 |
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+ | 4.2015 | 0.7343 | 385000 | 0.0397 | 6.6533 | 775.3367 | 4.1356 | 62.5253 |
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+ | 4.2242 | 0.7439 | 390000 | 0.0395 | 6.6529 | 775.0460 | 4.1344 | 62.4528 |
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+ | 4.1964 | 0.7534 | 395000 | 0.0397 | 6.6506 | 773.2814 | 4.1314 | 62.2637 |
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+ | 4.1939 | 0.7629 | 400000 | 0.0401 | 6.6492 | 772.1992 | 4.1299 | 62.1728 |
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+ | 4.229 | 0.7725 | 405000 | 0.0397 | 6.6447 | 768.7131 | 4.1276 | 62.0275 |
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+ | 4.1805 | 0.7820 | 410000 | 0.0399 | 6.6408 | 765.7219 | 4.1229 | 61.7394 |
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+ | 4.2036 | 0.7915 | 415000 | 0.0394 | 6.6451 | 769.0443 | 4.1261 | 61.9348 |
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+ | 4.169 | 0.8011 | 420000 | 0.0404 | 6.6364 | 762.3091 | 4.1192 | 61.5076 |
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+ | 4.1815 | 0.8106 | 425000 | 0.0395 | 6.6399 | 764.9846 | 4.1210 | 61.6187 |
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+ | 4.189 | 0.8202 | 430000 | 0.0397 | 6.6364 | 762.3756 | 4.1181 | 61.4406 |
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+ | 4.1774 | 0.8297 | 435000 | 0.0391 | 6.6365 | 762.4051 | 4.1172 | 61.3861 |
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+ | 4.1925 | 0.8392 | 440000 | 0.0398 | 6.6308 | 758.1226 | 4.1129 | 61.1245 |
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+ | 4.1868 | 0.8488 | 445000 | 0.0391 | 6.6303 | 757.6731 | 4.1124 | 61.0916 |
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+ | 4.1189 | 0.8583 | 450000 | 61.2059 | 4.1142 | 0.0394 | 759.8155 | 6.6331 |
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+ | 4.2156 | 0.8678 | 455000 | 61.0326 | 4.1114 | 0.0394 | 757.6308 | 6.6302 |
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+ | 4.1966 | 0.8774 | 460000 | 60.7623 | 4.1070 | 0.0398 | 754.0364 | 6.6254 |
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+ | 4.158 | 0.8869 | 465000 | 60.7495 | 4.1068 | 0.0398 | 754.6634 | 6.6263 |
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+ | 4.1639 | 0.8965 | 470000 | 60.5520 | 4.1035 | 0.0392 | 751.4165 | 6.6220 |
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+ | 4.1936 | 0.9060 | 475000 | 60.4407 | 4.1017 | 0.0396 | 750.2459 | 6.6204 |
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+ | 4.154 | 0.9155 | 480000 | 60.3311 | 4.0998 | 0.0393 | 748.5255 | 6.6181 |
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+ | 4.1473 | 0.9251 | 485000 | 60.3326 | 4.0999 | 0.0397 | 748.9214 | 6.6186 |
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+ | 4.1857 | 0.9346 | 490000 | 60.1977 | 4.0976 | 0.0397 | 746.6764 | 6.6156 |
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+ | 4.1724 | 0.9441 | 495000 | 60.1996 | 4.0977 | 0.0397 | 747.4420 | 6.6167 |
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+ | 4.166 | 0.9537 | 500000 | 60.1117 | 4.0962 | 0.0394 | 746.2841 | 6.6151 |
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+ | 4.1581 | 1.0095 | 505000 | 60.1274 | 4.0965 | 0.0395 | 746.5216 | 6.6154 |
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+ | 4.1443 | 1.0191 | 510000 | 60.1217 | 4.0964 | 0.0395 | 746.7120 | 6.6157 |
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+ | 4.1641 | 1.0286 | 515000 | 60.0107 | 4.0945 | 0.0397 | 744.8850 | 6.6132 |
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+ | 4.1673 | 1.0381 | 520000 | 59.9055 | 4.0928 | 0.0397 | 743.3574 | 6.6112 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.57.0.dev0
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+ - Pytorch 2.8.0+cu128
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+ - Datasets 4.0.0
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+ - Tokenizers 0.22.1
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