Initial Commit
Browse files- README.md +175 -0
- config.json +159 -0
- pytorch_model.bin +3 -0
- training_args.bin +3 -0
README.md
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| 1 |
+
---
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| 2 |
+
license: mit
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| 3 |
+
base_model: microsoft/mdeberta-v3-base
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| 4 |
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tags:
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| 5 |
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- generated_from_trainer
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| 6 |
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datasets:
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- massive
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| 8 |
+
metrics:
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| 9 |
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- accuracy
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| 10 |
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- f1
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| 11 |
+
model-index:
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| 12 |
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- name: scenario-KD-PR-MSV-D2_data-AmazonScience_massive_all_1_166
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| 13 |
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results: []
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| 14 |
+
---
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| 15 |
+
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| 16 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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| 17 |
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should probably proofread and complete it, then remove this comment. -->
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| 18 |
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| 19 |
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# scenario-KD-PR-MSV-D2_data-AmazonScience_massive_all_1_166
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the massive dataset.
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It achieves the following results on the evaluation set:
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| 23 |
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- Loss: 1.5394
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- Accuracy: 0.8627
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- F1: 0.8453
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| 27 |
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## Model description
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| 28 |
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| 29 |
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More information needed
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| 30 |
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| 31 |
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## Intended uses & limitations
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| 32 |
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| 33 |
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More information needed
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| 34 |
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| 35 |
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## Training and evaluation data
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| 36 |
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| 37 |
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More information needed
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| 38 |
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| 39 |
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## Training procedure
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| 40 |
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| 41 |
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### Training hyperparameters
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| 42 |
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| 43 |
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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| 45 |
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- train_batch_size: 32
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| 46 |
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- eval_batch_size: 32
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| 47 |
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- seed: 66
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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| 49 |
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- lr_scheduler_type: linear
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| 50 |
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- num_epochs: 30
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| 51 |
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| 52 |
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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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| 3.4018 | 0.27 | 5000 | 3.3570 | 0.7587 | 0.6728 |
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| 2.4831 | 0.53 | 10000 | 2.6998 | 0.8028 | 0.7596 |
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| 2.1077 | 0.8 | 15000 | 2.4164 | 0.8202 | 0.7862 |
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| 1.5375 | 1.07 | 20000 | 2.2821 | 0.8317 | 0.8012 |
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| 1.4474 | 1.34 | 25000 | 2.2615 | 0.8325 | 0.8043 |
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| 1.3836 | 1.6 | 30000 | 2.1448 | 0.8366 | 0.8133 |
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| 1.2782 | 1.87 | 35000 | 2.1212 | 0.8390 | 0.8135 |
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| 0.983 | 2.14 | 40000 | 2.0840 | 0.8429 | 0.8209 |
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| 0.9416 | 2.41 | 45000 | 2.1779 | 0.8422 | 0.8206 |
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| 65 |
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| 0.9138 | 2.67 | 50000 | 2.0942 | 0.8447 | 0.8247 |
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| 66 |
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| 0.9093 | 2.94 | 55000 | 2.0603 | 0.8454 | 0.8196 |
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| 67 |
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| 0.7465 | 3.21 | 60000 | 2.0972 | 0.8441 | 0.8245 |
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| 0.6996 | 3.47 | 65000 | 2.0355 | 0.8475 | 0.8270 |
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| 0.7454 | 3.74 | 70000 | 1.9610 | 0.8487 | 0.8298 |
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| 0.6906 | 4.01 | 75000 | 2.0084 | 0.8467 | 0.8288 |
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| 0.6203 | 4.28 | 80000 | 1.9601 | 0.8498 | 0.8306 |
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| 0.6039 | 4.54 | 85000 | 1.9766 | 0.8509 | 0.8347 |
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| 0.616 | 4.81 | 90000 | 1.9302 | 0.8518 | 0.8295 |
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| 0.5404 | 5.08 | 95000 | 1.9323 | 0.8512 | 0.8301 |
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| 0.5448 | 5.34 | 100000 | 1.9360 | 0.8533 | 0.8383 |
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| 0.5377 | 5.61 | 105000 | 1.9353 | 0.8511 | 0.8292 |
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| 0.5373 | 5.88 | 110000 | 1.9015 | 0.8506 | 0.8318 |
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| 0.4744 | 6.15 | 115000 | 1.9116 | 0.8527 | 0.8333 |
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| 0.4885 | 6.41 | 120000 | 1.8676 | 0.8543 | 0.8370 |
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| 0.4886 | 6.68 | 125000 | 1.8716 | 0.8546 | 0.8344 |
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| 0.4861 | 6.95 | 130000 | 1.8664 | 0.8535 | 0.8319 |
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| 0.4488 | 7.22 | 135000 | 1.8560 | 0.8547 | 0.8376 |
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| 0.426 | 7.48 | 140000 | 1.8350 | 0.8535 | 0.8334 |
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| 0.4451 | 7.75 | 145000 | 1.8258 | 0.8544 | 0.8333 |
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| 0.4299 | 8.02 | 150000 | 1.8220 | 0.8560 | 0.8370 |
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| 86 |
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| 0.4207 | 8.28 | 155000 | 1.8048 | 0.8559 | 0.8373 |
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| 0.4033 | 8.55 | 160000 | 1.8295 | 0.8538 | 0.8367 |
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| 0.4039 | 8.82 | 165000 | 1.7818 | 0.8566 | 0.8391 |
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| 0.3874 | 9.09 | 170000 | 1.7857 | 0.8563 | 0.8391 |
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| 0.3843 | 9.35 | 175000 | 1.7860 | 0.8548 | 0.8374 |
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| 0.3882 | 9.62 | 180000 | 1.8074 | 0.8558 | 0.8374 |
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| 92 |
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| 0.3866 | 9.89 | 185000 | 1.7823 | 0.8583 | 0.8404 |
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| 0.36 | 10.15 | 190000 | 1.7294 | 0.8571 | 0.8375 |
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| 0.3592 | 10.42 | 195000 | 1.7363 | 0.8578 | 0.8399 |
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| 0.3628 | 10.69 | 200000 | 1.7460 | 0.8582 | 0.8385 |
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| 96 |
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| 0.3579 | 10.96 | 205000 | 1.7431 | 0.8580 | 0.8399 |
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| 97 |
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| 0.3448 | 11.22 | 210000 | 1.7398 | 0.8564 | 0.8378 |
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| 0.3512 | 11.49 | 215000 | 1.7193 | 0.8584 | 0.8402 |
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| 0.3367 | 11.76 | 220000 | 1.7197 | 0.8594 | 0.8425 |
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| 0.327 | 12.03 | 225000 | 1.7189 | 0.8576 | 0.8385 |
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| 101 |
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| 0.3248 | 12.29 | 230000 | 1.6991 | 0.8602 | 0.8398 |
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| 0.3306 | 12.56 | 235000 | 1.7119 | 0.8577 | 0.8404 |
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| 0.3181 | 12.83 | 240000 | 1.6892 | 0.8606 | 0.8414 |
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| 104 |
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| 0.3167 | 13.09 | 245000 | 1.6647 | 0.8590 | 0.8380 |
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| 0.3149 | 13.36 | 250000 | 1.6780 | 0.8590 | 0.8414 |
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| 106 |
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| 0.3221 | 13.63 | 255000 | 1.6626 | 0.8601 | 0.8437 |
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| 107 |
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| 0.3147 | 13.9 | 260000 | 1.7135 | 0.8595 | 0.8418 |
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| 108 |
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| 0.2954 | 14.16 | 265000 | 1.6915 | 0.8581 | 0.8390 |
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| 109 |
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| 0.2912 | 14.43 | 270000 | 1.6699 | 0.8582 | 0.8392 |
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| 0.3123 | 14.7 | 275000 | 1.6659 | 0.8589 | 0.8399 |
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| 111 |
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| 0.3047 | 14.96 | 280000 | 1.6654 | 0.8610 | 0.8443 |
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| 112 |
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| 0.2916 | 15.23 | 285000 | 1.6408 | 0.8600 | 0.8421 |
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| 113 |
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| 0.282 | 15.5 | 290000 | 1.6729 | 0.8580 | 0.8405 |
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| 114 |
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| 0.2843 | 15.77 | 295000 | 1.6475 | 0.8600 | 0.8416 |
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| 115 |
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| 0.2764 | 16.03 | 300000 | 1.6342 | 0.8607 | 0.8426 |
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| 116 |
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| 0.2726 | 16.3 | 305000 | 1.6541 | 0.8597 | 0.8425 |
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| 117 |
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| 0.2895 | 16.57 | 310000 | 1.6280 | 0.8597 | 0.8413 |
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| 118 |
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| 0.2744 | 16.84 | 315000 | 1.6453 | 0.8607 | 0.8422 |
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| 119 |
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| 0.2727 | 17.1 | 320000 | 1.6319 | 0.8600 | 0.8432 |
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| 120 |
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| 0.2708 | 17.37 | 325000 | 1.6395 | 0.8599 | 0.8427 |
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| 121 |
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| 0.271 | 17.64 | 330000 | 1.6232 | 0.8600 | 0.8403 |
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| 122 |
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| 0.2695 | 17.9 | 335000 | 1.6294 | 0.8597 | 0.8419 |
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| 123 |
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| 0.2698 | 18.17 | 340000 | 1.6158 | 0.8620 | 0.8438 |
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| 124 |
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| 0.2582 | 18.44 | 345000 | 1.6214 | 0.8625 | 0.8448 |
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| 125 |
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| 0.2614 | 18.71 | 350000 | 1.6112 | 0.8610 | 0.8431 |
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| 126 |
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| 0.2583 | 18.97 | 355000 | 1.5978 | 0.8620 | 0.8440 |
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| 127 |
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| 0.258 | 19.24 | 360000 | 1.5902 | 0.8623 | 0.8446 |
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| 128 |
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| 0.2498 | 19.51 | 365000 | 1.6081 | 0.8611 | 0.8427 |
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| 129 |
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| 0.2569 | 19.77 | 370000 | 1.6165 | 0.8604 | 0.8420 |
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| 130 |
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| 0.2395 | 20.04 | 375000 | 1.5880 | 0.8614 | 0.8433 |
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| 131 |
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| 0.2527 | 20.31 | 380000 | 1.6055 | 0.8599 | 0.8428 |
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| 132 |
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| 0.2504 | 20.58 | 385000 | 1.5929 | 0.8614 | 0.8443 |
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| 133 |
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| 0.2494 | 20.84 | 390000 | 1.5841 | 0.8624 | 0.8444 |
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| 134 |
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| 0.2434 | 21.11 | 395000 | 1.5833 | 0.8614 | 0.8446 |
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| 135 |
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| 0.243 | 21.38 | 400000 | 1.5739 | 0.8619 | 0.8438 |
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| 136 |
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| 0.2389 | 21.65 | 405000 | 1.5816 | 0.8619 | 0.8438 |
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| 137 |
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| 0.2467 | 21.91 | 410000 | 1.5844 | 0.8616 | 0.8439 |
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| 138 |
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| 0.2352 | 22.18 | 415000 | 1.5748 | 0.8628 | 0.8446 |
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| 139 |
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| 0.2323 | 22.45 | 420000 | 1.5654 | 0.8623 | 0.8427 |
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| 140 |
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| 0.2314 | 22.71 | 425000 | 1.5537 | 0.8627 | 0.8449 |
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| 141 |
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| 0.238 | 22.98 | 430000 | 1.5613 | 0.8624 | 0.8424 |
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| 142 |
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| 0.223 | 23.25 | 435000 | 1.5661 | 0.8626 | 0.8441 |
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| 143 |
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| 0.2287 | 23.52 | 440000 | 1.5714 | 0.8627 | 0.8447 |
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| 144 |
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| 0.239 | 23.78 | 445000 | 1.5594 | 0.8634 | 0.8455 |
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| 145 |
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| 0.2275 | 24.05 | 450000 | 1.5629 | 0.8615 | 0.8436 |
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| 146 |
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| 0.2232 | 24.32 | 455000 | 1.5725 | 0.8618 | 0.8451 |
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| 147 |
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| 0.2267 | 24.58 | 460000 | 1.5550 | 0.8627 | 0.8455 |
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| 148 |
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| 0.2248 | 24.85 | 465000 | 1.5574 | 0.8633 | 0.8455 |
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| 149 |
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| 0.2214 | 25.12 | 470000 | 1.5602 | 0.8613 | 0.8432 |
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| 150 |
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| 0.2205 | 25.39 | 475000 | 1.5599 | 0.8617 | 0.8432 |
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| 151 |
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| 0.2189 | 25.65 | 480000 | 1.5395 | 0.8620 | 0.8452 |
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| 152 |
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| 0.2174 | 25.92 | 485000 | 1.5577 | 0.8625 | 0.8445 |
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| 153 |
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| 0.2148 | 26.19 | 490000 | 1.5533 | 0.8628 | 0.8457 |
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| 154 |
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| 0.2175 | 26.46 | 495000 | 1.5496 | 0.8619 | 0.8443 |
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| 155 |
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| 0.2121 | 26.72 | 500000 | 1.5509 | 0.8617 | 0.8443 |
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| 156 |
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| 0.2163 | 26.99 | 505000 | 1.5560 | 0.8624 | 0.8453 |
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| 157 |
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| 0.211 | 27.26 | 510000 | 1.5491 | 0.8629 | 0.8459 |
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| 158 |
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| 0.2142 | 27.52 | 515000 | 1.5576 | 0.8607 | 0.8438 |
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| 159 |
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| 0.2084 | 27.79 | 520000 | 1.5522 | 0.8624 | 0.8456 |
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| 160 |
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| 0.2119 | 28.06 | 525000 | 1.5429 | 0.8621 | 0.8449 |
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| 161 |
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| 0.2008 | 28.33 | 530000 | 1.5452 | 0.8627 | 0.8465 |
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| 162 |
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| 0.2084 | 28.59 | 535000 | 1.5458 | 0.8628 | 0.8464 |
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| 163 |
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| 0.2086 | 28.86 | 540000 | 1.5454 | 0.8622 | 0.8446 |
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| 164 |
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| 0.2102 | 29.13 | 545000 | 1.5487 | 0.8622 | 0.8449 |
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| 165 |
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| 0.2118 | 29.39 | 550000 | 1.5448 | 0.8621 | 0.8451 |
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| 166 |
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| 0.2049 | 29.66 | 555000 | 1.5411 | 0.8626 | 0.8454 |
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| 167 |
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| 0.2026 | 29.93 | 560000 | 1.5394 | 0.8627 | 0.8453 |
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| 168 |
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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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|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "microsoft/mdeberta-v3-base",
|
| 3 |
+
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|
| 4 |
+
"DebertaForSequenceClassificationKD"
|
| 5 |
+
],
|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
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|
| 39 |
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|
| 40 |
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| 41 |
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| 42 |
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| 43 |
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| 44 |
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| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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| 49 |
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| 50 |
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| 53 |
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| 54 |
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| 55 |
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| 56 |
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| 57 |
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| 59 |
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| 60 |
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|
| 61 |
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|
| 62 |
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| 63 |
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| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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|
| 69 |
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|
| 70 |
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|
| 71 |
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|
| 72 |
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|
| 73 |
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|
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|
| 75 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
| 135 |
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|
| 136 |
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|
| 137 |
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|
| 138 |
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|
| 139 |
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|
| 140 |
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|
| 141 |
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|
| 142 |
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|
| 143 |
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|
| 144 |
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|
| 145 |
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|
| 146 |
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|
| 147 |
+
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|
| 148 |
+
"p2c",
|
| 149 |
+
"c2p"
|
| 150 |
+
],
|
| 151 |
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|
| 152 |
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|
| 153 |
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|
| 154 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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"type_vocab_size": 0,
|
| 158 |
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"vocab_size": 251000
|
| 159 |
+
}
|
pytorch_model.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:c33068fbbb01b0aa84b2472b00f118aaad990800e8bd3fb3b5315a290cd240b4
|
| 3 |
+
size 946915690
|
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:5fdcd1076d3e0ab36e14063e58207a7a7fe5f7b8fb6c8a1ee41c1dd6cd702d17
|
| 3 |
+
size 4600
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