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silmi224
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finetune-led2

Summarization
Transformers
TensorBoard
Safetensors
led
text2text-generation
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
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Training complete

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  1. README.md +186 -0
  2. config.json +59 -0
  3. generation_config.json +14 -0
  4. merges.txt +0 -0
  5. model.safetensors +3 -0
  6. runs/Jun20_03-52-10_nkvln4vqi5/events.out.tfevents.1718855536.nkvln4vqi5.5946.0 +3 -0
  7. runs/Jun20_03-52-10_nkvln4vqi5/events.out.tfevents.1718856128.nkvln4vqi5.5946.1 +3 -0
  8. special_tokens_map.json +51 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +57 -0
  11. training_args.bin +3 -0
  12. vocab.json +0 -0
README.md ADDED
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+ ---
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+ tags:
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+ - summarization
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+ - generated_from_trainer
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+ model-index:
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+ - name: finetune-led2
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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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+ # finetune-led2
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+
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+ This model was trained from scratch on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.9753
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+ - Rouge1 Precision: 0.2523
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+ - Rouge1 Recall: 0.3403
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+ - Rouge1 Fmeasure: 0.2864
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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: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 32
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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: 2
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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 | Rouge1 Fmeasure | Rouge1 Precision | Rouge1 Recall |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------------:|:----------------:|:-------------:|
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+ | 2.0588 | 0.06 | 10 | 2.6812 | 0.2738 | 0.2434 | 0.3302 |
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+ | 1.89 | 0.13 | 20 | 2.5273 | 0.2786 | 0.2527 | 0.3216 |
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+ | 1.7108 | 0.19 | 30 | 2.4943 | 0.281 | 0.2471 | 0.337 |
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+ | 1.6632 | 0.26 | 40 | 2.4320 | 0.2805 | 0.2456 | 0.3387 |
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+ | 1.6764 | 0.32 | 50 | 2.3851 | 0.2806 | 0.2473 | 0.3352 |
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+ | 1.6218 | 0.38 | 60 | 2.3718 | 0.2741 | 0.2395 | 0.3306 |
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+ | 1.6105 | 0.11 | 70 | 2.3655 | 0.2851 | 0.2486 | 0.3446 |
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+ | 1.6344 | 0.13 | 80 | 2.3529 | 0.2798 | 0.2485 | 0.3301 |
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+ | 1.6359 | 0.14 | 90 | 2.3933 | 0.2845 | 0.2505 | 0.3394 |
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+ | 1.5291 | 0.16 | 100 | 2.4088 | 0.2802 | 0.2453 | 0.3375 |
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+ | 1.483 | 0.18 | 110 | 2.3258 | 0.2891 | 0.2575 | 0.3397 |
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+ | 1.5317 | 0.19 | 120 | 2.2498 | 0.2785 | 0.2508 | 0.3223 |
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+ | 1.5924 | 0.21 | 130 | 2.2690 | 0.2797 | 0.2471 | 0.3326 |
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+ | 1.5086 | 0.22 | 140 | 2.3032 | 0.2768 | 0.2439 | 0.33 |
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+ | 1.4654 | 0.24 | 150 | 2.2767 | 0.2792 | 0.2511 | 0.324 |
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+ | 1.4575 | 0.26 | 160 | 2.2510 | 0.2825 | 0.2493 | 0.3356 |
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+ | 1.5274 | 0.27 | 170 | 2.2635 | 0.2787 | 0.2446 | 0.3338 |
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+ | 1.4993 | 0.29 | 180 | 2.2363 | 0.2845 | 0.2496 | 0.3406 |
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+ | 1.5557 | 0.3 | 190 | 2.2319 | 0.282 | 0.2479 | 0.3364 |
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+ | 1.476 | 0.32 | 200 | 2.2196 | 0.2735 | 0.2437 | 0.3216 |
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+ | 1.4695 | 0.34 | 210 | 2.2604 | 0.2828 | 0.2477 | 0.3394 |
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+ | 1.4819 | 0.35 | 220 | 2.2487 | 0.2806 | 0.2481 | 0.3329 |
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+ | 1.4132 | 0.37 | 230 | 2.2176 | 0.2903 | 0.2543 | 0.348 |
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+ | 1.4574 | 0.38 | 240 | 2.1912 | 0.2847 | 0.2513 | 0.3378 |
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+ | 1.5263 | 0.4 | 250 | 2.1772 | 0.2761 | 0.2463 | 0.3234 |
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+ | 1.4955 | 0.42 | 260 | 2.2065 | 0.2859 | 0.2483 | 0.3467 |
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+ | 1.4698 | 0.43 | 270 | 2.1837 | 0.2831 | 0.2495 | 0.3368 |
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+ | 1.5245 | 0.45 | 280 | 2.1769 | 0.2846 | 0.2513 | 0.3377 |
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+ | 1.4691 | 0.46 | 290 | 2.1495 | 0.2832 | 0.252 | 0.3326 |
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+ | 1.4203 | 0.48 | 300 | 2.1742 | 0.2791 | 0.2483 | 0.328 |
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+ | 1.4827 | 0.5 | 310 | 2.1789 | 0.2793 | 0.2445 | 0.335 |
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+ | 1.4478 | 0.51 | 320 | 2.1456 | 0.279 | 0.2483 | 0.328 |
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+ | 1.4188 | 0.53 | 330 | 2.1637 | 0.2795 | 0.2499 | 0.3268 |
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+ | 1.4096 | 0.54 | 340 | 2.1568 | 0.2863 | 0.2522 | 0.3407 |
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+ | 1.4355 | 0.56 | 350 | 2.1613 | 0.2818 | 0.2467 | 0.3384 |
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+ | 1.4587 | 0.58 | 360 | 2.1394 | 0.278 | 0.2441 | 0.3319 |
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+ | 1.4674 | 0.59 | 370 | 2.1284 | 0.2818 | 0.2503 | 0.3317 |
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+ | 1.3842 | 0.61 | 380 | 2.1286 | 0.2828 | 0.2521 | 0.3311 |
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+ | 1.456 | 0.62 | 390 | 2.1013 | 0.2806 | 0.2513 | 0.3268 |
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+ | 1.3512 | 0.64 | 400 | 2.1385 | 0.2817 | 0.2463 | 0.3384 |
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+ | 1.3697 | 0.66 | 410 | 2.1282 | 0.2779 | 0.2451 | 0.3303 |
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+ | 1.4404 | 0.67 | 420 | 2.1087 | 0.2837 | 0.2498 | 0.3376 |
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+ | 1.4956 | 0.69 | 430 | 2.1034 | 0.2773 | 0.2446 | 0.3293 |
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+ | 1.5532 | 0.7 | 440 | 2.1103 | 0.2834 | 0.2471 | 0.342 |
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+ | 1.3254 | 0.72 | 450 | 2.1337 | 0.2811 | 0.2466 | 0.3363 |
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+ | 1.4318 | 0.74 | 460 | 2.0690 | 0.287 | 0.2548 | 0.3382 |
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+ | 1.3766 | 0.75 | 470 | 2.1392 | 0.2837 | 0.2438 | 0.3487 |
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+ | 1.318 | 0.77 | 480 | 2.1353 | 0.2864 | 0.2492 | 0.3465 |
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+ | 1.343 | 0.78 | 490 | 2.1262 | 0.282 | 0.2478 | 0.3375 |
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+ | 1.3887 | 0.8 | 500 | 2.1246 | 0.2904 | 0.2552 | 0.3471 |
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+ | 1.4047 | 0.82 | 510 | 2.0871 | 0.2835 | 0.2552 | 0.3285 |
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+ | 1.459 | 0.83 | 520 | 2.1219 | 0.2824 | 0.247 | 0.3391 |
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+ | 1.5071 | 0.85 | 530 | 2.0846 | 0.2799 | 0.2472 | 0.3321 |
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+ | 1.3899 | 0.86 | 540 | 2.1019 | 0.2857 | 0.2505 | 0.3416 |
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+ | 1.4277 | 0.88 | 550 | 2.0739 | 0.2831 | 0.2503 | 0.335 |
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+ | 1.3651 | 0.9 | 560 | 2.0838 | 0.2788 | 0.2458 | 0.3312 |
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+ | 1.4135 | 0.91 | 570 | 2.0783 | 0.2878 | 0.2547 | 0.3403 |
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+ | 1.4165 | 0.93 | 580 | 2.0414 | 0.2848 | 0.2554 | 0.3311 |
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+ | 1.3862 | 0.94 | 590 | 2.0631 | 0.2824 | 0.2497 | 0.3343 |
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+ | 1.3839 | 0.96 | 600 | 2.0496 | 0.2778 | 0.2477 | 0.3252 |
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+ | 1.3881 | 0.98 | 610 | 2.0682 | 0.2836 | 0.2492 | 0.3387 |
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+ | 1.4462 | 0.99 | 620 | 2.0703 | 0.2824 | 0.2492 | 0.3355 |
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+ | 1.2929 | 1.14 | 710 | 2.0588 | 0.2834 | 0.2487 | 0.339 |
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+ | 1.2662 | 1.17 | 730 | 2.0552 | 0.2777 | 0.2452 | 0.3295 |
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+ | 1.2999 | 1.18 | 740 | 2.0337 | 0.2823 | 0.2512 | 0.3314 |
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+ | 1.327 | 1.2 | 750 | 2.0363 | 0.2843 | 0.2486 | 0.3416 |
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+ | 1.2588 | 1.22 | 760 | 2.0550 | 0.2889 | 0.2549 | 0.3437 |
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+ | 1.2676 | 1.23 | 770 | 2.0146 | 0.2819 | 0.2483 | 0.3351 |
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+ | 1.3498 | 1.25 | 780 | 2.0291 | 0.2812 | 0.2475 | 0.3348 |
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+ | 1.3004 | 1.26 | 790 | 2.0501 | 0.2836 | 0.2484 | 0.3398 |
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+ | 1.2663 | 1.28 | 800 | 2.0205 | 0.2838 | 0.2505 | 0.3364 |
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+ | 1.2419 | 1.3 | 810 | 2.0274 | 0.2837 | 0.2511 | 0.3353 |
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+ | 1.3022 | 1.31 | 820 | 2.0277 | 0.2838 | 0.2496 | 0.3376 |
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+ | 1.3404 | 1.33 | 830 | 2.0417 | 0.2835 | 0.2491 | 0.3384 |
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+ | 1.2684 | 1.34 | 840 | 2.0474 | 0.2841 | 0.2485 | 0.3412 |
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+ | 1.3272 | 1.36 | 850 | 2.0520 | 0.2889 | 0.254 | 0.3444 |
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+ | 1.2857 | 1.46 | 910 | 1.9979 | 0.2891 | 0.2542 | 0.3447 |
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+ | 1.1536 | 1.57 | 980 | 1.9899 | 0.2905 | 0.2558 | 0.3456 |
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+ | 1.2731 | 1.58 | 990 | 1.9915 | 0.2864 | 0.2528 | 0.3401 |
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+ | 1.2415 | 1.79 | 1120 | 1.9927 | 0.288 | 0.2541 | 0.342 |
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+ | 1.2879 | 1.81 | 1130 | 1.9839 | 0.2831 | 0.2491 | 0.3378 |
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+ | 1.2328 | 1.82 | 1140 | 1.9698 | 0.2852 | 0.2526 | 0.337 |
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+ | 1.2562 | 1.84 | 1150 | 1.9953 | 0.2866 | 0.2504 | 0.3444 |
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+ | 1.3071 | 1.86 | 1160 | 1.9698 | 0.2847 | 0.2507 | 0.3389 |
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+ | 1.2276 | 1.87 | 1170 | 1.9787 | 0.2863 | 0.2517 | 0.3415 |
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+ | 1.1608 | 1.89 | 1180 | 1.9944 | 0.289 | 0.2528 | 0.3469 |
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+ | 1.3046 | 1.9 | 1190 | 1.9750 | 0.2863 | 0.2516 | 0.3414 |
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+ | 1.2356 | 1.98 | 1240 | 1.9747 | 0.286 | 0.2522 | 0.3399 |
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+ | 1.1865 | 2.0 | 1250 | 1.9753 | 0.2863 | 0.2523 | 0.3404 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.1+cu121
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+ - Datasets 2.14.5
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+ - Tokenizers 0.15.1
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+ "LEDForConditionalGeneration"
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+ }
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