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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: 0.1894 |
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- Precision: 0.7360 |
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- Recall: 0.7770 |
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- F1: 0.7560 |
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- Accuracy: 0.9556 |
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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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| No log | 1.0 | 30 | 0.3205 | 0.4448 | 0.2093 | 0.2847 | 0.9095 | |
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| No log | 2.0 | 60 | 0.2400 | 0.6346 | 0.6075 | 0.6208 | 0.9376 | |
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| No log | 3.0 | 90 | 0.2236 | 0.6764 | 0.7010 | 0.6885 | 0.9450 | |
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| No log | 4.0 | 120 | 0.1959 | 0.6664 | 0.7127 | 0.6888 | 0.9453 | |
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| No log | 5.0 | 150 | 0.1958 | 0.7177 | 0.7514 | 0.7342 | 0.9519 | |
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| No log | 6.0 | 180 | 0.1802 | 0.7180 | 0.7666 | 0.7415 | 0.9541 | |
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| No log | 7.0 | 210 | 0.1911 | 0.7316 | 0.7668 | 0.7488 | 0.9546 | |
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| No log | 8.0 | 240 | 0.1914 | 0.7384 | 0.7711 | 0.7544 | 0.9554 | |
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| No log | 9.0 | 270 | 0.1873 | 0.7366 | 0.7745 | 0.7551 | 0.9556 | |
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| No log | 10.0 | 300 | 0.1894 | 0.7360 | 0.7770 | 0.7560 | 0.9556 | |
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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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