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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: MatSciBERT_ST_DA_100 |
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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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# MatSciBERT_ST_DA_100 |
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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: 0.2043 |
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- Precision: 0.9627 |
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- Recall: 0.9693 |
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- F1: 0.9660 |
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- Accuracy: 0.9561 |
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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: 16 |
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- eval_batch_size: 16 |
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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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| No log | 1.0 | 59 | 0.2685 | 0.9263 | 0.9420 | 0.9341 | 0.9213 | |
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| No log | 2.0 | 118 | 0.1935 | 0.9477 | 0.9573 | 0.9524 | 0.9429 | |
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| No log | 3.0 | 177 | 0.2043 | 0.9558 | 0.9669 | 0.9613 | 0.9506 | |
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| No log | 4.0 | 236 | 0.1769 | 0.9596 | 0.9701 | 0.9648 | 0.9554 | |
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| No log | 5.0 | 295 | 0.1789 | 0.9619 | 0.9686 | 0.9652 | 0.9561 | |
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| No log | 6.0 | 354 | 0.1916 | 0.9620 | 0.9683 | 0.9651 | 0.9557 | |
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| No log | 7.0 | 413 | 0.1955 | 0.9623 | 0.9685 | 0.9654 | 0.9559 | |
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| No log | 8.0 | 472 | 0.2002 | 0.9627 | 0.9713 | 0.9670 | 0.9575 | |
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| 0.1044 | 9.0 | 531 | 0.2033 | 0.9632 | 0.9698 | 0.9665 | 0.9566 | |
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| 0.1044 | 10.0 | 590 | 0.2043 | 0.9627 | 0.9693 | 0.9660 | 0.9561 | |
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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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