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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-SCR-MSV-CL-D2_data-cl-massive_all_1_155 |
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results: [] |
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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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# scenario-KD-SCR-MSV-CL-D2_data-cl-massive_all_1_155 |
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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: nan |
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- Accuracy: 0.0315 |
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- F1: 0.0010 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:| |
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| 0.0 | 0.56 | 5000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 1.11 | 10000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 1.67 | 15000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 2.22 | 20000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 2.78 | 25000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 3.33 | 30000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 3.89 | 35000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 4.45 | 40000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 5.0 | 45000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 5.56 | 50000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 6.11 | 55000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 6.67 | 60000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 7.23 | 65000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 7.78 | 70000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 8.34 | 75000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 8.89 | 80000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 9.45 | 85000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 10.0 | 90000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 10.56 | 95000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 11.12 | 100000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 11.67 | 105000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 12.23 | 110000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 12.78 | 115000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 13.34 | 120000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 13.9 | 125000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 14.45 | 130000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 15.01 | 135000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 15.56 | 140000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 16.12 | 145000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 16.67 | 150000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 17.23 | 155000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 17.79 | 160000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 18.34 | 165000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 18.9 | 170000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 19.45 | 175000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 20.01 | 180000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 20.56 | 185000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 21.12 | 190000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 21.68 | 195000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 22.23 | 200000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 22.79 | 205000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 23.34 | 210000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 23.9 | 215000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 24.46 | 220000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 25.01 | 225000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 25.57 | 230000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 26.12 | 235000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 26.68 | 240000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 27.23 | 245000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 27.79 | 250000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 28.35 | 255000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 28.9 | 260000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 29.46 | 265000 | nan | 0.0315 | 0.0010 | |
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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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