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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_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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# MatSciBERT_ST_DA_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: 0.1671 |
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- Precision: 0.8613 |
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- Recall: 0.8455 |
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- F1: 0.8533 |
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- Accuracy: 0.9746 |
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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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| 0.0809 | 1.0 | 1050 | 0.1024 | 0.8455 | 0.8230 | 0.8341 | 0.9719 | |
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| 0.0424 | 2.0 | 2100 | 0.1023 | 0.8371 | 0.8365 | 0.8368 | 0.9714 | |
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| 0.025 | 3.0 | 3150 | 0.1397 | 0.8397 | 0.8223 | 0.8309 | 0.9702 | |
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| 0.0142 | 4.0 | 4200 | 0.1403 | 0.8592 | 0.8379 | 0.8484 | 0.9736 | |
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| 0.0074 | 5.0 | 5250 | 0.1271 | 0.8727 | 0.8641 | 0.8684 | 0.9771 | |
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| 0.0047 | 6.0 | 6300 | 0.1430 | 0.8742 | 0.8581 | 0.8661 | 0.9769 | |
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| 0.0023 | 7.0 | 7350 | 0.1777 | 0.8578 | 0.8310 | 0.8442 | 0.9733 | |
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| 0.0015 | 8.0 | 8400 | 0.1524 | 0.8637 | 0.8581 | 0.8609 | 0.9760 | |
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| 0.0013 | 9.0 | 9450 | 0.1683 | 0.8609 | 0.8441 | 0.8524 | 0.9745 | |
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| 0.0008 | 10.0 | 10500 | 0.1671 | 0.8613 | 0.8455 | 0.8533 | 0.9746 | |
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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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