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--- |
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library_name: transformers |
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license: mit |
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base_model: m3rg-iitd/matscibert |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: VF_MatSciBERT_ST_1800 |
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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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# VF_MatSciBERT_ST_1800 |
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This model is a fine-tuned version of [m3rg-iitd/matscibert](https://huggingface.co/m3rg-iitd/matscibert) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1571 |
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- Precision: 0.9763 |
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- Recall: 0.9819 |
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- F1: 0.9791 |
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- Accuracy: 0.9755 |
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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: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.1492 | 1.0 | 569 | 0.0954 | 0.9709 | 0.9754 | 0.9732 | 0.9704 | |
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| 0.0548 | 2.0 | 1138 | 0.0934 | 0.9726 | 0.9785 | 0.9756 | 0.9726 | |
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| 0.0348 | 3.0 | 1707 | 0.1098 | 0.9749 | 0.9801 | 0.9775 | 0.9738 | |
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| 0.0213 | 4.0 | 2276 | 0.1268 | 0.9739 | 0.9813 | 0.9776 | 0.9735 | |
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| 0.0141 | 5.0 | 2845 | 0.1326 | 0.9748 | 0.9806 | 0.9777 | 0.9740 | |
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| 0.0093 | 6.0 | 3414 | 0.1402 | 0.9750 | 0.9808 | 0.9779 | 0.9743 | |
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| 0.0062 | 7.0 | 3983 | 0.1541 | 0.9741 | 0.9805 | 0.9773 | 0.9733 | |
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| 0.0033 | 8.0 | 4552 | 0.1682 | 0.9741 | 0.9814 | 0.9777 | 0.9732 | |
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| 0.0026 | 9.0 | 5121 | 0.1638 | 0.9749 | 0.9821 | 0.9785 | 0.9743 | |
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| 0.0021 | 10.0 | 5690 | 0.1571 | 0.9763 | 0.9819 | 0.9791 | 0.9755 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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