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 |
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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 |
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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_144
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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_144
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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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- Loss: 1.5372
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- Accuracy: 0.8628
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- F1: 0.8443
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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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- seed: 44
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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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| 3.3933 | 0.27 | 5000 | 3.3770 | 0.7592 | 0.6735 |
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| 2.4971 | 0.53 | 10000 | 2.6953 | 0.8035 | 0.7540 |
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| 2.0927 | 0.8 | 15000 | 2.3912 | 0.8204 | 0.7848 |
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| 1.5883 | 1.07 | 20000 | 2.3109 | 0.8293 | 0.7958 |
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| 1.4485 | 1.34 | 25000 | 2.2594 | 0.8334 | 0.8011 |
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| 1.307 | 1.6 | 30000 | 2.1968 | 0.8378 | 0.8114 |
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| 1.2493 | 1.87 | 35000 | 2.1525 | 0.8409 | 0.8173 |
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| 0.9916 | 2.14 | 40000 | 2.1361 | 0.8424 | 0.8104 |
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| 0.9827 | 2.41 | 45000 | 2.1188 | 0.8429 | 0.8220 |
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| 0.944 | 2.67 | 50000 | 2.1135 | 0.8434 | 0.8169 |
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| 0.9152 | 2.94 | 55000 | 2.0101 | 0.8462 | 0.8247 |
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| 0.7317 | 3.21 | 60000 | 2.0755 | 0.8439 | 0.8213 |
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| 0.7081 | 3.47 | 65000 | 2.0258 | 0.8485 | 0.8280 |
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| 0.7277 | 3.74 | 70000 | 2.0031 | 0.8470 | 0.8284 |
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| 0.6924 | 4.01 | 75000 | 2.0036 | 0.8465 | 0.8244 |
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| 0.6096 | 4.28 | 80000 | 1.9999 | 0.8481 | 0.8309 |
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| 0.585 | 4.54 | 85000 | 1.9466 | 0.8514 | 0.8287 |
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| 0.6191 | 4.81 | 90000 | 1.9612 | 0.8472 | 0.8240 |
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| 0.5445 | 5.08 | 95000 | 1.9839 | 0.8480 | 0.8308 |
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| 0.5231 | 5.34 | 100000 | 1.9036 | 0.8531 | 0.8322 |
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| 0.5315 | 5.61 | 105000 | 1.9101 | 0.8528 | 0.8353 |
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| 0.5295 | 5.88 | 110000 | 1.9476 | 0.8482 | 0.8299 |
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| 0.4722 | 6.15 | 115000 | 1.9299 | 0.8497 | 0.8272 |
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| 0.4737 | 6.41 | 120000 | 1.8646 | 0.8523 | 0.8299 |
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| 0.4869 | 6.68 | 125000 | 1.8722 | 0.8535 | 0.8335 |
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| 0.4662 | 6.95 | 130000 | 1.8699 | 0.8515 | 0.8314 |
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| 0.4278 | 7.22 | 135000 | 1.8489 | 0.8533 | 0.8330 |
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| 0.4383 | 7.48 | 140000 | 1.8333 | 0.8549 | 0.8345 |
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| 0.4427 | 7.75 | 145000 | 1.8657 | 0.8549 | 0.8372 |
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| 0.415 | 8.02 | 150000 | 1.8319 | 0.8552 | 0.8347 |
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| 0.3984 | 8.28 | 155000 | 1.8365 | 0.8519 | 0.8322 |
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| 0.4037 | 8.55 | 160000 | 1.7974 | 0.8556 | 0.8393 |
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| 0.3978 | 8.82 | 165000 | 1.7839 | 0.8570 | 0.8372 |
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| 0.3899 | 9.09 | 170000 | 1.7743 | 0.8568 | 0.8383 |
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| 0.3772 | 9.35 | 175000 | 1.7579 | 0.8584 | 0.8381 |
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| 0.3828 | 9.62 | 180000 | 1.7751 | 0.8555 | 0.8357 |
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| 0.3857 | 9.89 | 185000 | 1.7670 | 0.8588 | 0.8409 |
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| 0.3606 | 10.15 | 190000 | 1.7508 | 0.8574 | 0.8405 |
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| 0.3568 | 10.42 | 195000 | 1.7329 | 0.8585 | 0.8404 |
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| 0.3555 | 10.69 | 200000 | 1.7450 | 0.8574 | 0.8359 |
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| 96 |
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| 0.3589 | 10.96 | 205000 | 1.7490 | 0.8581 | 0.8373 |
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| 0.3229 | 11.22 | 210000 | 1.7103 | 0.8586 | 0.8395 |
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| 0.3433 | 11.49 | 215000 | 1.7174 | 0.8571 | 0.8372 |
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| 0.3441 | 11.76 | 220000 | 1.6939 | 0.8571 | 0.8379 |
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| 0.3307 | 12.03 | 225000 | 1.6927 | 0.8593 | 0.8413 |
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| 0.3356 | 12.29 | 230000 | 1.7138 | 0.8575 | 0.8374 |
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| 0.3185 | 12.56 | 235000 | 1.7078 | 0.8579 | 0.8376 |
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| 0.3291 | 12.83 | 240000 | 1.6861 | 0.8592 | 0.8420 |
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| 104 |
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| 0.3198 | 13.09 | 245000 | 1.6635 | 0.8601 | 0.8415 |
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| 0.3152 | 13.36 | 250000 | 1.6871 | 0.8589 | 0.8396 |
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| 106 |
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| 0.3158 | 13.63 | 255000 | 1.6959 | 0.8578 | 0.8380 |
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| 107 |
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| 0.3108 | 13.9 | 260000 | 1.6792 | 0.8585 | 0.8398 |
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| 108 |
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| 0.3032 | 14.16 | 265000 | 1.6630 | 0.8605 | 0.8417 |
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| 109 |
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| 0.2983 | 14.43 | 270000 | 1.6545 | 0.8599 | 0.8416 |
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| 0.2942 | 14.7 | 275000 | 1.6757 | 0.8597 | 0.8414 |
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| 111 |
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| 0.3039 | 14.96 | 280000 | 1.6613 | 0.8585 | 0.8409 |
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| 112 |
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| 0.2865 | 15.23 | 285000 | 1.6440 | 0.8584 | 0.8413 |
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| 113 |
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| 0.2989 | 15.5 | 290000 | 1.6612 | 0.8579 | 0.8396 |
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| 114 |
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| 0.2875 | 15.77 | 295000 | 1.6469 | 0.8594 | 0.8394 |
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| 115 |
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| 0.2774 | 16.03 | 300000 | 1.6531 | 0.8589 | 0.8432 |
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| 116 |
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| 0.2783 | 16.3 | 305000 | 1.6534 | 0.8603 | 0.8424 |
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| 117 |
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| 0.2789 | 16.57 | 310000 | 1.6438 | 0.8589 | 0.8390 |
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| 118 |
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| 0.2819 | 16.84 | 315000 | 1.6277 | 0.8598 | 0.8394 |
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| 119 |
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| 0.2759 | 17.1 | 320000 | 1.6124 | 0.8605 | 0.8426 |
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| 120 |
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| 0.2725 | 17.37 | 325000 | 1.6262 | 0.8616 | 0.8437 |
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| 121 |
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| 0.2678 | 17.64 | 330000 | 1.6184 | 0.8599 | 0.8416 |
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| 122 |
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| 0.2778 | 17.9 | 335000 | 1.6167 | 0.8611 | 0.8418 |
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| 123 |
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| 0.2608 | 18.17 | 340000 | 1.6083 | 0.8593 | 0.8406 |
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| 124 |
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| 0.2587 | 18.44 | 345000 | 1.6272 | 0.8589 | 0.8401 |
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| 125 |
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| 0.2688 | 18.71 | 350000 | 1.6189 | 0.8599 | 0.8412 |
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| 126 |
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| 0.2651 | 18.97 | 355000 | 1.6063 | 0.8602 | 0.8427 |
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| 127 |
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| 0.2548 | 19.24 | 360000 | 1.6051 | 0.8608 | 0.8431 |
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| 128 |
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| 0.2512 | 19.51 | 365000 | 1.6080 | 0.8616 | 0.8423 |
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| 129 |
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| 0.2524 | 19.77 | 370000 | 1.5972 | 0.8606 | 0.8435 |
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| 130 |
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| 0.2463 | 20.04 | 375000 | 1.6055 | 0.8603 | 0.8427 |
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| 131 |
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| 0.2413 | 20.31 | 380000 | 1.5884 | 0.8605 | 0.8428 |
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| 132 |
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| 0.2376 | 20.58 | 385000 | 1.5853 | 0.8618 | 0.8451 |
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| 133 |
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| 0.2467 | 20.84 | 390000 | 1.5844 | 0.8617 | 0.8428 |
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| 134 |
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| 0.2427 | 21.11 | 395000 | 1.5759 | 0.8614 | 0.8438 |
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| 135 |
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| 0.2427 | 21.38 | 400000 | 1.5848 | 0.8623 | 0.8440 |
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| 136 |
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| 0.2367 | 21.65 | 405000 | 1.5765 | 0.8609 | 0.8439 |
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| 137 |
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| 0.237 | 21.91 | 410000 | 1.5633 | 0.8623 | 0.8434 |
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| 138 |
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| 0.2323 | 22.18 | 415000 | 1.5769 | 0.8616 | 0.8430 |
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| 139 |
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| 0.2327 | 22.45 | 420000 | 1.5757 | 0.8622 | 0.8429 |
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| 140 |
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| 0.2334 | 22.71 | 425000 | 1.5629 | 0.8612 | 0.8432 |
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| 141 |
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| 0.2302 | 22.98 | 430000 | 1.5771 | 0.8609 | 0.8432 |
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| 142 |
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| 0.2275 | 23.25 | 435000 | 1.5643 | 0.8621 | 0.8438 |
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| 143 |
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| 0.2283 | 23.52 | 440000 | 1.5670 | 0.8619 | 0.8442 |
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| 144 |
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| 0.2273 | 23.78 | 445000 | 1.5637 | 0.8620 | 0.8435 |
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| 145 |
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| 0.2192 | 24.05 | 450000 | 1.5571 | 0.8616 | 0.8433 |
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| 146 |
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| 0.2254 | 24.32 | 455000 | 1.5624 | 0.8610 | 0.8417 |
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| 147 |
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| 0.2239 | 24.58 | 460000 | 1.5502 | 0.8615 | 0.8432 |
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| 148 |
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| 0.226 | 24.85 | 465000 | 1.5507 | 0.8628 | 0.8448 |
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| 149 |
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| 0.2231 | 25.12 | 470000 | 1.5507 | 0.8622 | 0.8447 |
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| 150 |
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| 0.2188 | 25.39 | 475000 | 1.5468 | 0.8625 | 0.8450 |
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| 151 |
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| 0.219 | 25.65 | 480000 | 1.5514 | 0.8622 | 0.8444 |
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| 152 |
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| 0.2238 | 25.92 | 485000 | 1.5513 | 0.8624 | 0.8447 |
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| 153 |
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| 0.2183 | 26.19 | 490000 | 1.5414 | 0.8622 | 0.8447 |
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| 154 |
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| 0.2142 | 26.46 | 495000 | 1.5383 | 0.8627 | 0.8438 |
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| 155 |
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| 0.2225 | 26.72 | 500000 | 1.5409 | 0.8628 | 0.8449 |
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| 156 |
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| 0.2183 | 26.99 | 505000 | 1.5375 | 0.8624 | 0.8447 |
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| 157 |
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| 0.2163 | 27.26 | 510000 | 1.5429 | 0.8622 | 0.8444 |
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| 158 |
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| 0.214 | 27.52 | 515000 | 1.5422 | 0.8617 | 0.8432 |
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| 159 |
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| 0.2116 | 27.79 | 520000 | 1.5351 | 0.8626 | 0.8437 |
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| 160 |
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| 0.2132 | 28.06 | 525000 | 1.5360 | 0.8626 | 0.8446 |
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| 161 |
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| 0.2089 | 28.33 | 530000 | 1.5372 | 0.8625 | 0.8440 |
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| 162 |
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| 0.2095 | 28.59 | 535000 | 1.5375 | 0.8625 | 0.8443 |
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| 163 |
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| 0.2097 | 28.86 | 540000 | 1.5329 | 0.8632 | 0.8460 |
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| 164 |
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| 0.2141 | 29.13 | 545000 | 1.5331 | 0.8626 | 0.8455 |
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| 165 |
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| 0.2078 | 29.39 | 550000 | 1.5318 | 0.8624 | 0.8450 |
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| 166 |
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| 0.2051 | 29.66 | 555000 | 1.5388 | 0.8627 | 0.8446 |
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| 167 |
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| 0.2068 | 29.93 | 560000 | 1.5372 | 0.8628 | 0.8443 |
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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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| 55 |
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| 56 |
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| 57 |
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| 59 |
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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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|
| 74 |
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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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|
| 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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|
| 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 sha256:5ac681f0dd3dd0e2572ca29a108bbd996f2a758d22dba62274209337b6e6ddd3
|
| 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 sha256:4480a5f318e0d7def38dd7b6d2334ff5a67af49e10717b2abf4a693574e964ba
|
| 3 |
+
size 4600
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