End of training
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README.md
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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_MAT
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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_MAT
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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: 0.1551
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- Precision: 0.8250
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- Recall: 0.8333
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- F1: 0.8291
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- Accuracy: 0.9766
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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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| 0.1259 | 1.0 | 569 | 0.0862 | 0.8117 | 0.7998 | 0.8057 | 0.9742 |
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| 0.0476 | 2.0 | 1138 | 0.0909 | 0.8065 | 0.8154 | 0.8109 | 0.9741 |
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| 0.0296 | 3.0 | 1707 | 0.1032 | 0.8039 | 0.8232 | 0.8134 | 0.9739 |
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| 0.0196 | 4.0 | 2276 | 0.1157 | 0.8054 | 0.8203 | 0.8128 | 0.9745 |
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| 0.0118 | 5.0 | 2845 | 0.1182 | 0.8300 | 0.8311 | 0.8305 | 0.9768 |
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| 0.0074 | 6.0 | 3414 | 0.1399 | 0.8204 | 0.8151 | 0.8178 | 0.9753 |
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| 0.0053 | 7.0 | 3983 | 0.1445 | 0.8334 | 0.8223 | 0.8278 | 0.9765 |
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| 0.0025 | 8.0 | 4552 | 0.1521 | 0.8218 | 0.8288 | 0.8253 | 0.9758 |
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| 0.0023 | 9.0 | 5121 | 0.1555 | 0.8215 | 0.8255 | 0.8235 | 0.9759 |
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| 0.0016 | 10.0 | 5690 | 0.1551 | 0.8250 | 0.8333 | 0.8291 | 0.9766 |
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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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model.safetensors
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