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scenario-KD-PR-MSV-EN-CL-D2_data-en-massive_all_1_155

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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-cl-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-CL-D2_data-en-massive_all_1_155
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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-CL-D2_data-en-massive_all_1_155
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+
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+ This model is a fine-tuned version of [haryoaw/scenario-MDBT-TCR_data-cl-massive_all_1_1](https://huggingface.co/haryoaw/scenario-MDBT-TCR_data-cl-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: 3.1707
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+ - Accuracy: 0.4471
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+ - F1: 0.4385
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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: 55
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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 | 3.7123 | 0.2625 | 0.1148 |
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+ | No log | 0.56 | 200 | 3.5358 | 0.3390 | 0.2394 |
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+ | No log | 0.83 | 300 | 3.3412 | 0.3922 | 0.3123 |
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+ | No log | 1.11 | 400 | 3.4008 | 0.3772 | 0.3110 |
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+ | 2.4588 | 1.39 | 500 | 3.2536 | 0.4111 | 0.3405 |
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+ | 2.4588 | 1.67 | 600 | 3.2840 | 0.4097 | 0.3563 |
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+ | 2.4588 | 1.94 | 700 | 3.1644 | 0.4307 | 0.3710 |
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+ | 2.4588 | 2.22 | 800 | 3.1380 | 0.4364 | 0.3872 |
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+ | 2.4588 | 2.5 | 900 | 3.2618 | 0.4180 | 0.3833 |
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+ | 1.4472 | 2.78 | 1000 | 3.1488 | 0.4300 | 0.3861 |
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+ | 1.4472 | 3.06 | 1100 | 3.1174 | 0.4488 | 0.4097 |
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+ | 1.4472 | 3.33 | 1200 | 3.1442 | 0.4481 | 0.4091 |
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+ | 1.4472 | 3.61 | 1300 | 3.3004 | 0.4111 | 0.3841 |
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+ | 1.4472 | 3.89 | 1400 | 3.3097 | 0.4010 | 0.3802 |
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+ | 1.2115 | 4.17 | 1500 | 3.3768 | 0.4052 | 0.3846 |
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+ | 1.2115 | 4.44 | 1600 | 3.3732 | 0.4077 | 0.3919 |
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+ | 1.2115 | 4.72 | 1700 | 3.3767 | 0.4125 | 0.3980 |
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+ | 1.2115 | 5.0 | 1800 | 3.3720 | 0.4195 | 0.3896 |
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+ | 1.2115 | 5.28 | 1900 | 3.3548 | 0.4147 | 0.3941 |
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+ | 1.0796 | 5.56 | 2000 | 3.6313 | 0.3784 | 0.3743 |
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+ | 1.0796 | 5.83 | 2100 | 3.2951 | 0.4186 | 0.3930 |
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+ | 1.0796 | 6.11 | 2200 | 3.2913 | 0.4267 | 0.3988 |
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+ | 1.0796 | 6.39 | 2300 | 3.1985 | 0.4357 | 0.4107 |
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+ | 1.0796 | 6.67 | 2400 | 3.4566 | 0.3963 | 0.3932 |
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+ | 1.01 | 6.94 | 2500 | 3.3982 | 0.4094 | 0.4000 |
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+ | 1.01 | 7.22 | 2600 | 3.2082 | 0.4343 | 0.3960 |
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+ | 1.01 | 7.5 | 2700 | 3.3417 | 0.4153 | 0.4042 |
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+ | 1.01 | 7.78 | 2800 | 3.2235 | 0.4332 | 0.4025 |
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+ | 1.01 | 8.06 | 2900 | 3.2782 | 0.4264 | 0.4084 |
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+ | 0.9549 | 8.33 | 3000 | 3.3575 | 0.4120 | 0.3981 |
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+ | 0.9549 | 8.61 | 3100 | 3.2973 | 0.4231 | 0.4094 |
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+ | 0.9549 | 8.89 | 3200 | 3.3885 | 0.4086 | 0.3964 |
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+ | 0.9549 | 9.17 | 3300 | 3.3343 | 0.4185 | 0.4105 |
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+ | 0.9549 | 9.44 | 3400 | 3.3463 | 0.4177 | 0.4090 |
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+ | 0.9242 | 9.72 | 3500 | 3.2792 | 0.4287 | 0.4131 |
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+ | 0.9242 | 10.0 | 3600 | 3.3775 | 0.4110 | 0.4032 |
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+ | 0.9242 | 10.28 | 3700 | 3.3542 | 0.4210 | 0.4144 |
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+ | 0.9242 | 10.56 | 3800 | 3.2521 | 0.4345 | 0.4194 |
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+ | 0.9242 | 10.83 | 3900 | 3.3305 | 0.4234 | 0.4080 |
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+ | 0.9048 | 11.11 | 4000 | 3.5624 | 0.3953 | 0.4027 |
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+ | 0.9048 | 11.39 | 4100 | 3.5235 | 0.3909 | 0.3993 |
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+ | 0.9048 | 11.67 | 4200 | 3.3855 | 0.4153 | 0.3993 |
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+ | 0.9048 | 11.94 | 4300 | 3.4062 | 0.4026 | 0.3931 |
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+ | 0.9048 | 12.22 | 4400 | 3.3170 | 0.4241 | 0.4154 |
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+ | 0.8863 | 12.5 | 4500 | 3.2977 | 0.4287 | 0.4153 |
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+ | 0.8863 | 12.78 | 4600 | 3.5425 | 0.3886 | 0.3976 |
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+ | 0.8863 | 13.06 | 4700 | 3.4107 | 0.4081 | 0.3997 |
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+ | 0.8863 | 13.33 | 4800 | 3.4859 | 0.4068 | 0.4001 |
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+ | 0.8863 | 13.61 | 4900 | 3.4532 | 0.4104 | 0.4136 |
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+ | 0.8764 | 13.89 | 5000 | 3.4220 | 0.4105 | 0.4108 |
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+ | 0.8764 | 14.17 | 5100 | 3.4623 | 0.4121 | 0.4171 |
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+ | 0.8764 | 14.44 | 5200 | 3.4474 | 0.4026 | 0.4045 |
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+ | 0.8764 | 14.72 | 5300 | 3.3895 | 0.4177 | 0.4161 |
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+ | 0.8764 | 15.0 | 5400 | 3.2330 | 0.4416 | 0.4290 |
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+ | 0.8673 | 15.28 | 5500 | 3.3441 | 0.4180 | 0.4133 |
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+ | 0.8673 | 15.56 | 5600 | 3.3918 | 0.4172 | 0.4166 |
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+ | 0.8673 | 15.83 | 5700 | 3.2297 | 0.4393 | 0.4187 |
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+ | 0.8673 | 16.11 | 5800 | 3.2193 | 0.4418 | 0.4287 |
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+ | 0.8673 | 16.39 | 5900 | 3.4330 | 0.4124 | 0.4190 |
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+ | 0.8595 | 16.67 | 6000 | 3.2666 | 0.4351 | 0.4289 |
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+ | 0.8595 | 16.94 | 6100 | 3.1744 | 0.4529 | 0.4413 |
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+ | 0.8595 | 17.22 | 6200 | 3.4892 | 0.4036 | 0.4129 |
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+ | 0.8595 | 17.5 | 6300 | 3.3720 | 0.4189 | 0.4215 |
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+ | 0.8595 | 17.78 | 6400 | 3.3287 | 0.4213 | 0.4179 |
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+ | 0.8523 | 18.06 | 6500 | 3.4352 | 0.4089 | 0.4124 |
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+ | 0.8523 | 18.33 | 6600 | 3.2985 | 0.4255 | 0.4233 |
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+ | 0.8523 | 18.61 | 6700 | 3.2437 | 0.4355 | 0.4274 |
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+ | 0.8523 | 18.89 | 6800 | 3.3418 | 0.4200 | 0.4139 |
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+ | 0.8523 | 19.17 | 6900 | 3.2395 | 0.4346 | 0.4342 |
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+ | 0.8491 | 19.44 | 7000 | 3.2704 | 0.4283 | 0.4115 |
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+ | 0.8491 | 19.72 | 7100 | 3.2447 | 0.4378 | 0.4256 |
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+ | 0.8491 | 20.0 | 7200 | 3.2999 | 0.4281 | 0.4272 |
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+ | 0.8491 | 20.28 | 7300 | 3.2139 | 0.4346 | 0.4371 |
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+ | 0.8491 | 20.56 | 7400 | 3.3605 | 0.4190 | 0.4138 |
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+ | 0.8447 | 20.83 | 7500 | 3.3631 | 0.4216 | 0.4171 |
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+ | 0.8447 | 21.11 | 7600 | 3.2030 | 0.4422 | 0.4276 |
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+ | 0.8447 | 21.39 | 7700 | 3.3002 | 0.4257 | 0.4256 |
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+ | 0.8447 | 21.67 | 7800 | 3.3028 | 0.4275 | 0.4296 |
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+ | 0.8447 | 21.94 | 7900 | 3.2922 | 0.4281 | 0.4225 |
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+ | 0.8412 | 22.22 | 8000 | 3.1588 | 0.4464 | 0.4339 |
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+ | 0.8412 | 22.5 | 8100 | 3.2553 | 0.4367 | 0.4307 |
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+ | 0.8412 | 22.78 | 8200 | 3.1886 | 0.4433 | 0.4365 |
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+ | 0.8412 | 23.06 | 8300 | 3.3312 | 0.4245 | 0.4270 |
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+ | 0.8412 | 23.33 | 8400 | 3.2022 | 0.4447 | 0.4338 |
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+ | 0.8389 | 23.61 | 8500 | 3.3122 | 0.4214 | 0.4179 |
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+ | 0.8389 | 23.89 | 8600 | 3.0719 | 0.4621 | 0.4380 |
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+ | 0.8389 | 24.17 | 8700 | 3.2395 | 0.4386 | 0.4262 |
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+ | 0.8389 | 24.44 | 8800 | 3.2242 | 0.4364 | 0.4339 |
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+ | 0.8389 | 24.72 | 8900 | 3.3582 | 0.4201 | 0.4197 |
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+ | 0.8333 | 25.0 | 9000 | 3.1279 | 0.4537 | 0.4377 |
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+ | 0.8333 | 25.28 | 9100 | 3.1643 | 0.4458 | 0.4361 |
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+ | 0.8333 | 25.56 | 9200 | 3.1543 | 0.4503 | 0.4358 |
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+ | 0.8333 | 25.83 | 9300 | 3.2963 | 0.4251 | 0.4243 |
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+ | 0.8333 | 26.11 | 9400 | 3.0952 | 0.4567 | 0.4375 |
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+ | 0.8326 | 26.39 | 9500 | 3.2282 | 0.4385 | 0.4347 |
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+ | 0.8326 | 26.67 | 9600 | 3.1402 | 0.4512 | 0.4411 |
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+ | 0.8326 | 26.94 | 9700 | 3.2730 | 0.4321 | 0.4331 |
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+ | 0.8326 | 27.22 | 9800 | 3.2324 | 0.4393 | 0.4374 |
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+ | 0.8326 | 27.5 | 9900 | 3.2203 | 0.4418 | 0.4380 |
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+ | 0.8304 | 27.78 | 10000 | 3.1916 | 0.4444 | 0.4388 |
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+ | 0.8304 | 28.06 | 10100 | 3.2167 | 0.4395 | 0.4370 |
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+ | 0.8304 | 28.33 | 10200 | 3.1614 | 0.4477 | 0.4386 |
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+ | 0.8304 | 28.61 | 10300 | 3.1688 | 0.4483 | 0.4361 |
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+ | 0.8304 | 28.89 | 10400 | 3.1459 | 0.4511 | 0.4427 |
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+ | 0.8306 | 29.17 | 10500 | 3.1191 | 0.4546 | 0.4433 |
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+ | 0.8306 | 29.44 | 10600 | 3.1557 | 0.4493 | 0.4417 |
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+ | 0.8306 | 29.72 | 10700 | 3.1777 | 0.4456 | 0.4385 |
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+ | 0.8306 | 30.0 | 10800 | 3.1707 | 0.4471 | 0.4385 |
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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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