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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_1000 |
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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_1000 |
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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: 0.1669 |
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- Precision: 0.8484 |
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- Recall: 0.8572 |
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- F1: 0.8528 |
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- Accuracy: 0.9724 |
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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 | 495 | 0.1097 | 0.8373 | 0.8310 | 0.8341 | 0.9692 | |
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| 0.1746 | 2.0 | 990 | 0.0968 | 0.8355 | 0.8550 | 0.8452 | 0.9720 | |
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| 0.0592 | 3.0 | 1485 | 0.1072 | 0.8405 | 0.8497 | 0.8451 | 0.9711 | |
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| 0.0316 | 4.0 | 1980 | 0.1302 | 0.8451 | 0.8468 | 0.8459 | 0.9709 | |
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| 0.017 | 5.0 | 2475 | 0.1426 | 0.8381 | 0.8448 | 0.8415 | 0.9702 | |
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| 0.0102 | 6.0 | 2970 | 0.1503 | 0.8456 | 0.8470 | 0.8463 | 0.9711 | |
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| 0.0058 | 7.0 | 3465 | 0.1528 | 0.8466 | 0.8509 | 0.8487 | 0.9721 | |
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| 0.0035 | 8.0 | 3960 | 0.1565 | 0.8459 | 0.8521 | 0.8490 | 0.9719 | |
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| 0.0027 | 9.0 | 4455 | 0.1592 | 0.8531 | 0.8562 | 0.8547 | 0.9728 | |
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| 0.0017 | 10.0 | 4950 | 0.1669 | 0.8484 | 0.8572 | 0.8528 | 0.9724 | |
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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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