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--- |
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license: mit |
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base_model: microsoft/mdeberta-v3-base |
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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-MDBT-TCR_data-cl-massive_all_1_1 |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: massive |
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type: massive |
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config: all_1.1 |
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split: validation |
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args: all_1.1 |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7999125539705962 |
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- name: F1 |
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type: f1 |
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value: 0.7608456488954072 |
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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-MDBT-TCR_data-cl-massive_all_1_1 |
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the massive dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3792 |
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- Accuracy: 0.7999 |
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- F1: 0.7608 |
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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: 64 |
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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: 5 |
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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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| 0.4658 | 0.56 | 5000 | 0.9703 | 0.7825 | 0.7290 | |
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| 0.2748 | 1.11 | 10000 | 0.9829 | 0.7934 | 0.7386 | |
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| 0.237 | 1.67 | 15000 | 1.0459 | 0.7881 | 0.7348 | |
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| 0.1545 | 2.22 | 20000 | 1.1641 | 0.7920 | 0.7544 | |
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| 0.1482 | 2.78 | 25000 | 1.1840 | 0.7951 | 0.7528 | |
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| 0.1076 | 3.33 | 30000 | 1.2621 | 0.7933 | 0.7504 | |
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| 0.0974 | 3.89 | 35000 | 1.3127 | 0.7972 | 0.7566 | |
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| 0.0654 | 4.45 | 40000 | 1.3792 | 0.7999 | 0.7608 | |
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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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