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--- |
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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_BIOMAT_NER1800 |
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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_BIOMAT_NER1800 |
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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: 0.1788 |
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- Precision: 0.9841 |
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- Recall: 0.9758 |
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- F1: 0.9799 |
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- Accuracy: 0.9728 |
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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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| 0.186 | 1.0 | 869 | 0.0890 | 0.9831 | 0.9762 | 0.9796 | 0.9730 | |
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| 0.0519 | 2.0 | 1738 | 0.0944 | 0.9834 | 0.9773 | 0.9803 | 0.9744 | |
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| 0.0293 | 3.0 | 2607 | 0.1101 | 0.9832 | 0.9748 | 0.9790 | 0.9721 | |
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| 0.0185 | 4.0 | 3476 | 0.1348 | 0.9823 | 0.9752 | 0.9788 | 0.9721 | |
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| 0.0086 | 5.0 | 4345 | 0.1421 | 0.9823 | 0.9746 | 0.9785 | 0.9715 | |
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| 0.0054 | 6.0 | 5214 | 0.1755 | 0.9835 | 0.9719 | 0.9777 | 0.9693 | |
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| 0.0032 | 7.0 | 6083 | 0.1706 | 0.9831 | 0.9735 | 0.9783 | 0.9709 | |
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| 0.0027 | 8.0 | 6952 | 0.1774 | 0.9840 | 0.9756 | 0.9798 | 0.9729 | |
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| 0.0017 | 9.0 | 7821 | 0.1825 | 0.9841 | 0.9749 | 0.9795 | 0.9717 | |
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| 0.001 | 10.0 | 8690 | 0.1788 | 0.9841 | 0.9758 | 0.9799 | 0.9728 | |
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### Framework versions |
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- Transformers 4.42.4 |
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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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