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scenario-KD-PR-MSV-EN-EN-D2_data-en-massive_all_1_144

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deberta-v2
Generated from Trainer
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  1. README.md +171 -0
  2. config.json +159 -0
  3. eval_results_ml.json +1 -0
  4. pytorch_model.bin +3 -0
  5. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: mit
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+ base_model: haryoaw/scenario-MDBT-TCR_data-en-massive_all_1_1
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - massive
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+ metrics:
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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: scenario-KD-PR-MSV-EN-EN-D2_data-en-massive_all_1_144
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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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+ # scenario-KD-PR-MSV-EN-EN-D2_data-en-massive_all_1_144
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+
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+ This model is a fine-tuned version of [haryoaw/scenario-MDBT-TCR_data-en-massive_all_1_1](https://huggingface.co/haryoaw/scenario-MDBT-TCR_data-en-massive_all_1_1) on the massive dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 3.3870
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+ - Accuracy: 0.3917
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+ - F1: 0.3631
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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: 32
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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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+ - lr_scheduler_type: linear
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+ - num_epochs: 30
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
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+ | No log | 0.28 | 100 | 3.7985 | 0.2429 | 0.1191 |
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+ | No log | 0.56 | 200 | 3.5797 | 0.3350 | 0.2393 |
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+ | No log | 0.83 | 300 | 3.4614 | 0.3511 | 0.2584 |
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+ | No log | 1.11 | 400 | 3.4068 | 0.3689 | 0.2947 |
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+ | 2.3847 | 1.39 | 500 | 3.5361 | 0.3480 | 0.3101 |
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+ | 2.3847 | 1.67 | 600 | 3.8981 | 0.2962 | 0.2743 |
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+ | 2.3847 | 1.94 | 700 | 3.5978 | 0.3348 | 0.3009 |
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+ | 2.3847 | 2.22 | 800 | 3.4251 | 0.3693 | 0.3189 |
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+ | 2.3847 | 2.5 | 900 | 3.6238 | 0.3387 | 0.2955 |
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+ | 1.4359 | 2.78 | 1000 | 3.4170 | 0.3725 | 0.3228 |
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+ | 1.4359 | 3.06 | 1100 | 3.4919 | 0.3577 | 0.3094 |
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+ | 1.4359 | 3.33 | 1200 | 3.5121 | 0.3529 | 0.3200 |
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+ | 1.4359 | 3.61 | 1300 | 3.5243 | 0.3552 | 0.3181 |
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+ | 1.4359 | 3.89 | 1400 | 3.5490 | 0.3579 | 0.3271 |
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+ | 1.2213 | 4.17 | 1500 | 3.7359 | 0.3382 | 0.3141 |
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+ | 1.2213 | 4.44 | 1600 | 3.4488 | 0.3750 | 0.3190 |
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+ | 1.2213 | 4.72 | 1700 | 3.8128 | 0.3207 | 0.3010 |
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+ | 1.2213 | 5.0 | 1800 | 3.6438 | 0.3436 | 0.3157 |
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+ | 1.2213 | 5.28 | 1900 | 3.6529 | 0.3533 | 0.3232 |
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+ | 1.085 | 5.56 | 2000 | 3.7020 | 0.3460 | 0.3180 |
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+ | 1.085 | 5.83 | 2100 | 3.5656 | 0.3617 | 0.3212 |
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+ | 1.085 | 6.11 | 2200 | 3.7196 | 0.3451 | 0.3331 |
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+ | 1.085 | 6.39 | 2300 | 3.4895 | 0.3783 | 0.3449 |
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+ | 1.085 | 6.67 | 2400 | 3.4481 | 0.3827 | 0.3461 |
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+ | 1.0193 | 6.94 | 2500 | 3.5108 | 0.3743 | 0.3371 |
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+ | 1.0193 | 7.22 | 2600 | 3.6085 | 0.3680 | 0.3401 |
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+ | 1.0193 | 7.5 | 2700 | 3.7560 | 0.3461 | 0.3396 |
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+ | 1.0193 | 7.78 | 2800 | 3.6117 | 0.3654 | 0.3430 |
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+ | 1.0193 | 8.06 | 2900 | 3.8823 | 0.3372 | 0.3342 |
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+ | 0.9642 | 8.33 | 3000 | 4.1240 | 0.2905 | 0.3077 |
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+ | 0.9642 | 8.61 | 3100 | 3.5464 | 0.3624 | 0.3257 |
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+ | 0.9642 | 8.89 | 3200 | 3.7347 | 0.3436 | 0.3277 |
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+ | 0.9642 | 9.17 | 3300 | 3.7061 | 0.3393 | 0.3172 |
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+ | 0.9642 | 9.44 | 3400 | 3.7392 | 0.3448 | 0.3316 |
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+ | 0.9379 | 9.72 | 3500 | 3.7291 | 0.3382 | 0.3217 |
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+ | 0.9379 | 10.0 | 3600 | 3.4839 | 0.3661 | 0.3376 |
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+ | 0.9379 | 10.28 | 3700 | 3.5460 | 0.3703 | 0.3383 |
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+ | 0.9379 | 10.56 | 3800 | 3.5424 | 0.3719 | 0.3402 |
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+ | 0.9379 | 10.83 | 3900 | 3.7746 | 0.3507 | 0.3373 |
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+ | 0.9141 | 11.11 | 4000 | 3.6570 | 0.3653 | 0.3369 |
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+ | 0.9141 | 11.39 | 4100 | 3.6878 | 0.3567 | 0.3366 |
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+ | 0.9141 | 11.67 | 4200 | 3.4917 | 0.3786 | 0.3503 |
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+ | 0.9141 | 11.94 | 4300 | 3.6285 | 0.3568 | 0.3375 |
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+ | 0.9141 | 12.22 | 4400 | 3.7634 | 0.3416 | 0.3232 |
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+ | 0.8926 | 12.5 | 4500 | 3.6110 | 0.3640 | 0.3335 |
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+ | 0.8926 | 12.78 | 4600 | 3.7520 | 0.3365 | 0.3206 |
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+ | 0.8926 | 13.06 | 4700 | 3.6192 | 0.3649 | 0.3343 |
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+ | 0.8926 | 13.33 | 4800 | 3.6111 | 0.3648 | 0.3258 |
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+ | 0.8926 | 13.61 | 4900 | 3.6608 | 0.3553 | 0.3316 |
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+ | 0.881 | 13.89 | 5000 | 3.6331 | 0.3596 | 0.3414 |
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+ | 0.881 | 14.17 | 5100 | 3.5635 | 0.3697 | 0.3486 |
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+ | 0.881 | 14.44 | 5200 | 3.5596 | 0.3728 | 0.3476 |
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+ | 0.881 | 14.72 | 5300 | 3.4594 | 0.3890 | 0.3505 |
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+ | 0.881 | 15.0 | 5400 | 3.5156 | 0.3752 | 0.3387 |
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+ | 0.8711 | 15.28 | 5500 | 3.7477 | 0.3417 | 0.3220 |
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+ | 0.8711 | 15.56 | 5600 | 3.4787 | 0.3726 | 0.3433 |
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+ | 0.8711 | 15.83 | 5700 | 3.3340 | 0.4009 | 0.3567 |
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+ | 0.8711 | 16.11 | 5800 | 3.5768 | 0.3636 | 0.3398 |
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+ | 0.8711 | 16.39 | 5900 | 3.5530 | 0.3682 | 0.3436 |
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+ | 0.8624 | 16.67 | 6000 | 3.5606 | 0.3622 | 0.3428 |
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+ | 0.8624 | 16.94 | 6100 | 3.5734 | 0.3639 | 0.3428 |
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+ | 0.8624 | 17.22 | 6200 | 3.6723 | 0.3560 | 0.3326 |
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+ | 0.8624 | 17.5 | 6300 | 3.4305 | 0.3926 | 0.3590 |
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+ | 0.8624 | 17.78 | 6400 | 3.5705 | 0.3697 | 0.3485 |
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+ | 0.8568 | 18.06 | 6500 | 3.5787 | 0.3717 | 0.3562 |
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+ | 0.8568 | 18.33 | 6600 | 3.5437 | 0.3682 | 0.3459 |
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+ | 0.8568 | 18.61 | 6700 | 3.4142 | 0.3933 | 0.3551 |
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+ | 0.8568 | 18.89 | 6800 | 3.5347 | 0.3757 | 0.3533 |
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+ | 0.8568 | 19.17 | 6900 | 3.4827 | 0.3751 | 0.3474 |
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+ | 0.8485 | 19.44 | 7000 | 3.5962 | 0.3686 | 0.3475 |
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+ | 0.8485 | 19.72 | 7100 | 3.6892 | 0.3526 | 0.3444 |
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+ | 0.8485 | 20.0 | 7200 | 3.7340 | 0.3527 | 0.3421 |
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+ | 0.8485 | 20.28 | 7300 | 3.6498 | 0.3529 | 0.3388 |
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+ | 0.8485 | 20.56 | 7400 | 3.5198 | 0.3712 | 0.3440 |
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+ | 0.8454 | 20.83 | 7500 | 3.5547 | 0.3731 | 0.3460 |
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+ | 0.8454 | 21.11 | 7600 | 3.4824 | 0.3827 | 0.3530 |
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+ | 0.8454 | 21.39 | 7700 | 3.7520 | 0.3479 | 0.3489 |
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+ | 0.8454 | 21.67 | 7800 | 3.4160 | 0.3927 | 0.3530 |
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+ | 0.8454 | 21.94 | 7900 | 3.4024 | 0.3916 | 0.3555 |
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+ | 0.8442 | 22.22 | 8000 | 3.5260 | 0.3766 | 0.3571 |
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+ | 0.8442 | 22.5 | 8100 | 3.7724 | 0.3411 | 0.3307 |
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+ | 0.8442 | 22.78 | 8200 | 3.4421 | 0.3906 | 0.3611 |
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+ | 0.8442 | 23.06 | 8300 | 3.5752 | 0.3697 | 0.3521 |
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+ | 0.8442 | 23.33 | 8400 | 3.6166 | 0.3607 | 0.3474 |
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+ | 0.8387 | 23.61 | 8500 | 3.4849 | 0.3772 | 0.3468 |
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+ | 0.8387 | 23.89 | 8600 | 3.6369 | 0.3550 | 0.3435 |
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+ | 0.8387 | 24.17 | 8700 | 3.5332 | 0.3731 | 0.3564 |
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+ | 0.8387 | 24.44 | 8800 | 3.4314 | 0.3856 | 0.3612 |
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+ | 0.8387 | 24.72 | 8900 | 3.5849 | 0.3646 | 0.3489 |
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+ | 0.8373 | 25.0 | 9000 | 3.4793 | 0.3775 | 0.3532 |
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+ | 0.8373 | 25.28 | 9100 | 3.4012 | 0.3874 | 0.3601 |
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+ | 0.8373 | 25.56 | 9200 | 3.5138 | 0.3746 | 0.3531 |
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+ | 0.8373 | 25.83 | 9300 | 3.3756 | 0.3955 | 0.3663 |
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+ | 0.8373 | 26.11 | 9400 | 3.4281 | 0.3847 | 0.3546 |
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+ | 0.8357 | 26.39 | 9500 | 3.3819 | 0.3928 | 0.3576 |
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+ | 0.8357 | 26.67 | 9600 | 3.3574 | 0.3965 | 0.3640 |
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+ | 0.8357 | 26.94 | 9700 | 3.3550 | 0.3962 | 0.3621 |
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+ | 0.8357 | 27.22 | 9800 | 3.4785 | 0.3769 | 0.3571 |
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+ | 0.8357 | 27.5 | 9900 | 3.5116 | 0.3717 | 0.3495 |
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+ | 0.8341 | 27.78 | 10000 | 3.4470 | 0.3797 | 0.3562 |
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+ | 0.8341 | 28.06 | 10100 | 3.4118 | 0.3878 | 0.3642 |
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+ | 0.8341 | 28.33 | 10200 | 3.3945 | 0.3910 | 0.3637 |
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+ | 0.8341 | 28.61 | 10300 | 3.4078 | 0.3854 | 0.3591 |
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+ | 0.8341 | 28.89 | 10400 | 3.5367 | 0.3678 | 0.3548 |
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+ | 0.8325 | 29.17 | 10500 | 3.4340 | 0.3825 | 0.3605 |
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+ | 0.8325 | 29.44 | 10600 | 3.4028 | 0.3875 | 0.3604 |
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+ | 0.8325 | 29.72 | 10700 | 3.3913 | 0.3904 | 0.3635 |
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+ | 0.8325 | 30.0 | 10800 | 3.3870 | 0.3917 | 0.3631 |
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+
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+
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+ ### Framework versions
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+
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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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