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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_CEMS |
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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_CEMS |
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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: 0.0598 |
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- Precision: 0.9368 |
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- Recall: 0.9226 |
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- F1: 0.9296 |
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- Accuracy: 0.9898 |
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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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| 0.0492 | 1.0 | 569 | 0.0347 | 0.9181 | 0.9091 | 0.9136 | 0.9881 | |
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| 0.0177 | 2.0 | 1138 | 0.0331 | 0.9406 | 0.9177 | 0.9290 | 0.9905 | |
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| 0.0109 | 3.0 | 1707 | 0.0454 | 0.9116 | 0.9122 | 0.9119 | 0.9876 | |
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| 0.0066 | 4.0 | 2276 | 0.0454 | 0.9596 | 0.8970 | 0.9272 | 0.9896 | |
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| 0.0042 | 5.0 | 2845 | 0.0477 | 0.9352 | 0.9061 | 0.9204 | 0.9889 | |
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| 0.0027 | 6.0 | 3414 | 0.0525 | 0.9352 | 0.9146 | 0.9248 | 0.9896 | |
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| 0.0018 | 7.0 | 3983 | 0.0498 | 0.9405 | 0.9159 | 0.9280 | 0.9899 | |
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| 0.0008 | 8.0 | 4552 | 0.0555 | 0.9312 | 0.9238 | 0.9275 | 0.9896 | |
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| 0.0007 | 9.0 | 5121 | 0.0602 | 0.9406 | 0.9165 | 0.9284 | 0.9897 | |
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| 0.0006 | 10.0 | 5690 | 0.0598 | 0.9368 | 0.9226 | 0.9296 | 0.9898 | |
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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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