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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: ST_MAT |
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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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# ST_MAT |
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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: 0.1551 |
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- Precision: 0.8250 |
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- Recall: 0.8333 |
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- F1: 0.8291 |
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- Accuracy: 0.9766 |
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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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| 0.1259 | 1.0 | 569 | 0.0862 | 0.8117 | 0.7998 | 0.8057 | 0.9742 | |
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| 0.0476 | 2.0 | 1138 | 0.0909 | 0.8065 | 0.8154 | 0.8109 | 0.9741 | |
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| 0.0296 | 3.0 | 1707 | 0.1032 | 0.8039 | 0.8232 | 0.8134 | 0.9739 | |
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| 0.0196 | 4.0 | 2276 | 0.1157 | 0.8054 | 0.8203 | 0.8128 | 0.9745 | |
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| 0.0118 | 5.0 | 2845 | 0.1182 | 0.8300 | 0.8311 | 0.8305 | 0.9768 | |
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| 0.0074 | 6.0 | 3414 | 0.1399 | 0.8204 | 0.8151 | 0.8178 | 0.9753 | |
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| 0.0053 | 7.0 | 3983 | 0.1445 | 0.8334 | 0.8223 | 0.8278 | 0.9765 | |
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| 0.0025 | 8.0 | 4552 | 0.1521 | 0.8218 | 0.8288 | 0.8253 | 0.9758 | |
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| 0.0023 | 9.0 | 5121 | 0.1555 | 0.8215 | 0.8255 | 0.8235 | 0.9759 | |
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| 0.0016 | 10.0 | 5690 | 0.1551 | 0.8250 | 0.8333 | 0.8291 | 0.9766 | |
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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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