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--- |
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license: mit |
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base_model: haryoaw/scenario-MDBT-TCR_data-cl-cardiff_cl_only |
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tags: |
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- generated_from_trainer |
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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-CDF-CL-D2_data-cl-cardiff_cl_only44 |
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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-CDF-CL-D2_data-cl-cardiff_cl_only44 |
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This model is a fine-tuned version of [haryoaw/scenario-MDBT-TCR_data-cl-cardiff_cl_only](https://huggingface.co/haryoaw/scenario-MDBT-TCR_data-cl-cardiff_cl_only) on the None 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: 0.3333 |
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- F1: 0.1667 |
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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: 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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| No log | 1.09 | 250 | nan | 0.3333 | 0.1667 | |
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| 1.0787 | 2.17 | 500 | nan | 0.3333 | 0.1667 | |
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| 1.0787 | 3.26 | 750 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 4.35 | 1000 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 5.43 | 1250 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 6.52 | 1500 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 7.61 | 1750 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 8.7 | 2000 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 9.78 | 2250 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 10.87 | 2500 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 11.96 | 2750 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 13.04 | 3000 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 14.13 | 3250 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 15.22 | 3500 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 16.3 | 3750 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 17.39 | 4000 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 18.48 | 4250 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 19.57 | 4500 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 20.65 | 4750 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 21.74 | 5000 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 22.83 | 5250 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 23.91 | 5500 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 25.0 | 5750 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 26.09 | 6000 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 27.17 | 6250 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 28.26 | 6500 | nan | 0.3333 | 0.1667 | |
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| 0.0 | 29.35 | 6750 | nan | 0.3333 | 0.1667 | |
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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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