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scenario-KD-PR-MSV-EN-EN-D2_data-en-massive_all_1_166

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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_166
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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_166
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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: 3.4492
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+ - Accuracy: 0.3818
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+ - F1: 0.3581
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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: 66
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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 | 3.8981 | 0.2168 | 0.1114 |
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+ | No log | 0.56 | 200 | 3.5590 | 0.3252 | 0.2326 |
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+ | No log | 0.83 | 300 | 3.6191 | 0.3248 | 0.2629 |
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+ | No log | 1.11 | 400 | 3.5005 | 0.3542 | 0.2804 |
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+ | 2.3711 | 1.39 | 500 | 3.4728 | 0.3569 | 0.2978 |
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+ | 2.3711 | 1.67 | 600 | 3.4235 | 0.3693 | 0.3204 |
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+ | 2.3711 | 1.94 | 700 | 3.5368 | 0.3430 | 0.3074 |
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+ | 2.3711 | 2.22 | 800 | 3.5023 | 0.3635 | 0.3122 |
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+ | 2.3711 | 2.5 | 900 | 3.3525 | 0.3822 | 0.3070 |
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+ | 1.4454 | 2.78 | 1000 | 3.6046 | 0.3420 | 0.3023 |
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+ | 1.4454 | 3.06 | 1100 | 3.4244 | 0.3775 | 0.3410 |
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+ | 1.4454 | 3.33 | 1200 | 3.6073 | 0.3529 | 0.3042 |
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+ | 1.4454 | 3.61 | 1300 | 3.6553 | 0.3534 | 0.3289 |
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+ | 1.4454 | 3.89 | 1400 | 3.6924 | 0.3411 | 0.3067 |
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+ | 1.2035 | 4.17 | 1500 | 3.4352 | 0.3786 | 0.3192 |
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+ | 1.2035 | 4.44 | 1600 | 3.4389 | 0.3710 | 0.3269 |
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+ | 1.2035 | 4.72 | 1700 | 3.5966 | 0.3514 | 0.3190 |
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+ | 1.2035 | 5.0 | 1800 | 3.5810 | 0.3547 | 0.3168 |
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+ | 1.2035 | 5.28 | 1900 | 3.3785 | 0.3915 | 0.3424 |
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+ | 1.0761 | 5.56 | 2000 | 3.4477 | 0.3808 | 0.3318 |
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+ | 1.0761 | 5.83 | 2100 | 3.3579 | 0.3894 | 0.3369 |
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+ | 1.0761 | 6.11 | 2200 | 3.8225 | 0.3286 | 0.3007 |
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+ | 1.0761 | 6.39 | 2300 | 3.6119 | 0.3583 | 0.3126 |
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+ | 1.0761 | 6.67 | 2400 | 3.5649 | 0.3672 | 0.3298 |
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+ | 1.0182 | 6.94 | 2500 | 3.9692 | 0.3115 | 0.3079 |
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+ | 1.0182 | 7.22 | 2600 | 3.8932 | 0.3314 | 0.3159 |
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+ | 1.0182 | 7.5 | 2700 | 3.7041 | 0.3464 | 0.3302 |
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+ | 1.0182 | 7.78 | 2800 | 3.6965 | 0.3461 | 0.3072 |
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+ | 1.0182 | 8.06 | 2900 | 3.8919 | 0.3247 | 0.3219 |
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+ | 0.9596 | 8.33 | 3000 | 3.8834 | 0.3147 | 0.3119 |
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+ | 0.9596 | 8.61 | 3100 | 3.6113 | 0.3597 | 0.3412 |
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+ | 0.9596 | 8.89 | 3200 | 3.8647 | 0.3239 | 0.3064 |
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+ | 0.9596 | 9.17 | 3300 | 3.5087 | 0.3684 | 0.3281 |
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+ | 0.9596 | 9.44 | 3400 | 3.7126 | 0.3427 | 0.3211 |
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+ | 0.9317 | 9.72 | 3500 | 3.5315 | 0.3774 | 0.3439 |
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+ | 0.9317 | 10.0 | 3600 | 3.7289 | 0.3479 | 0.3339 |
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+ | 0.9317 | 10.28 | 3700 | 3.6699 | 0.3538 | 0.3343 |
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+ | 0.9317 | 10.56 | 3800 | 3.4747 | 0.3796 | 0.3378 |
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+ | 0.9317 | 10.83 | 3900 | 3.6562 | 0.3548 | 0.3186 |
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+ | 0.91 | 11.11 | 4000 | 3.4031 | 0.3896 | 0.3478 |
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+ | 0.91 | 11.39 | 4100 | 3.5768 | 0.3557 | 0.3296 |
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+ | 0.91 | 11.67 | 4200 | 3.5617 | 0.3642 | 0.3370 |
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+ | 0.91 | 11.94 | 4300 | 3.7519 | 0.3351 | 0.3163 |
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+ | 0.91 | 12.22 | 4400 | 3.5107 | 0.3755 | 0.3402 |
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+ | 0.892 | 12.5 | 4500 | 3.6299 | 0.3625 | 0.3322 |
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+ | 0.892 | 12.78 | 4600 | 3.5303 | 0.3773 | 0.3394 |
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+ | 0.892 | 13.06 | 4700 | 3.6772 | 0.3513 | 0.3279 |
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+ | 0.892 | 13.33 | 4800 | 3.6287 | 0.3550 | 0.3322 |
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+ | 0.892 | 13.61 | 4900 | 3.6046 | 0.3608 | 0.3363 |
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+ | 0.8782 | 13.89 | 5000 | 3.5809 | 0.3728 | 0.3401 |
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+ | 0.8782 | 14.17 | 5100 | 3.6615 | 0.3544 | 0.3327 |
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+ | 0.8782 | 14.44 | 5200 | 3.4584 | 0.3782 | 0.3471 |
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+ | 0.8782 | 14.72 | 5300 | 3.6412 | 0.3705 | 0.3456 |
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+ | 0.8782 | 15.0 | 5400 | 3.6166 | 0.3647 | 0.3482 |
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+ | 0.8675 | 15.28 | 5500 | 3.7989 | 0.3411 | 0.3259 |
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+ | 0.8675 | 15.56 | 5600 | 3.5574 | 0.3703 | 0.3332 |
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+ | 0.8675 | 15.83 | 5700 | 3.5888 | 0.3649 | 0.3332 |
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+ | 0.8675 | 16.11 | 5800 | 3.3744 | 0.3900 | 0.3450 |
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+ | 0.8675 | 16.39 | 5900 | 3.6122 | 0.3645 | 0.3442 |
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+ | 0.862 | 16.67 | 6000 | 3.3953 | 0.3876 | 0.3457 |
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+ | 0.862 | 16.94 | 6100 | 3.3995 | 0.3945 | 0.3594 |
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+ | 0.862 | 17.22 | 6200 | 3.4168 | 0.3880 | 0.3463 |
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+ | 0.862 | 17.5 | 6300 | 3.6119 | 0.3668 | 0.3461 |
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+ | 0.862 | 17.78 | 6400 | 3.5063 | 0.3735 | 0.3325 |
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+ | 0.8544 | 18.06 | 6500 | 3.6581 | 0.3539 | 0.3344 |
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+ | 0.8544 | 18.33 | 6600 | 3.5380 | 0.3673 | 0.3352 |
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+ | 0.8544 | 18.61 | 6700 | 3.5699 | 0.3613 | 0.3399 |
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+ | 0.8544 | 18.89 | 6800 | 3.4977 | 0.3703 | 0.3441 |
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+ | 0.8544 | 19.17 | 6900 | 3.5746 | 0.3664 | 0.3401 |
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+ | 0.8494 | 19.44 | 7000 | 3.3279 | 0.4027 | 0.3671 |
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+ | 0.8494 | 19.72 | 7100 | 3.6689 | 0.3596 | 0.3504 |
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+ | 0.8494 | 20.0 | 7200 | 3.5632 | 0.3626 | 0.3439 |
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+ | 0.8494 | 20.28 | 7300 | 3.5577 | 0.3693 | 0.3394 |
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+ | 0.8494 | 20.56 | 7400 | 3.5795 | 0.3634 | 0.3458 |
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+ | 0.8452 | 20.83 | 7500 | 3.4764 | 0.3766 | 0.3444 |
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+ | 0.8452 | 21.11 | 7600 | 3.3944 | 0.3893 | 0.3569 |
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+ | 0.8452 | 21.39 | 7700 | 3.4161 | 0.3913 | 0.3588 |
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+ | 0.8452 | 21.67 | 7800 | 3.5015 | 0.3791 | 0.3527 |
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+ | 0.8452 | 21.94 | 7900 | 3.5177 | 0.3766 | 0.3493 |
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+ | 0.8413 | 22.22 | 8000 | 3.4390 | 0.3803 | 0.3547 |
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+ | 0.8413 | 22.5 | 8100 | 3.4736 | 0.3765 | 0.3537 |
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+ | 0.8413 | 22.78 | 8200 | 3.6093 | 0.3602 | 0.3438 |
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+ | 0.8413 | 23.06 | 8300 | 3.3350 | 0.3965 | 0.3576 |
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+ | 0.8413 | 23.33 | 8400 | 3.5186 | 0.3725 | 0.3478 |
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+ | 0.8393 | 23.61 | 8500 | 3.4701 | 0.3836 | 0.3639 |
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+ | 0.8393 | 23.89 | 8600 | 3.5562 | 0.3667 | 0.3485 |
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+ | 0.8393 | 24.17 | 8700 | 3.5092 | 0.3765 | 0.3536 |
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+ | 0.8393 | 24.44 | 8800 | 3.5955 | 0.3642 | 0.3451 |
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+ | 0.8393 | 24.72 | 8900 | 3.5135 | 0.3728 | 0.3542 |
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+ | 0.8363 | 25.0 | 9000 | 3.4253 | 0.3870 | 0.3583 |
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+ | 0.8363 | 25.28 | 9100 | 3.4027 | 0.3868 | 0.3585 |
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+ | 0.8363 | 25.56 | 9200 | 3.4741 | 0.3803 | 0.3568 |
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+ | 0.8363 | 25.83 | 9300 | 3.4929 | 0.3790 | 0.3585 |
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+ | 0.8363 | 26.11 | 9400 | 3.4803 | 0.3782 | 0.3549 |
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+ | 0.8328 | 26.39 | 9500 | 3.4915 | 0.3757 | 0.3557 |
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+ | 0.8328 | 26.67 | 9600 | 3.4388 | 0.3839 | 0.3603 |
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+ | 0.8328 | 26.94 | 9700 | 3.5595 | 0.3679 | 0.3510 |
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+ | 0.8328 | 27.22 | 9800 | 3.5496 | 0.3679 | 0.3530 |
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+ | 0.8328 | 27.5 | 9900 | 3.4764 | 0.3767 | 0.3540 |
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+ | 0.833 | 27.78 | 10000 | 3.5036 | 0.3759 | 0.3553 |
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+ | 0.833 | 28.06 | 10100 | 3.5016 | 0.3769 | 0.3546 |
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+ | 0.833 | 28.33 | 10200 | 3.5109 | 0.3752 | 0.3561 |
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+ | 0.833 | 28.61 | 10300 | 3.4405 | 0.3844 | 0.3602 |
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+ | 0.833 | 28.89 | 10400 | 3.4607 | 0.3835 | 0.3616 |
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+ | 0.8304 | 29.17 | 10500 | 3.4678 | 0.3791 | 0.3591 |
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+ | 0.8304 | 29.44 | 10600 | 3.4306 | 0.3858 | 0.3608 |
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+ | 0.8304 | 29.72 | 10700 | 3.4509 | 0.3818 | 0.3604 |
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+ | 0.8304 | 30.0 | 10800 | 3.4492 | 0.3818 | 0.3581 |
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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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