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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_NER3600 |
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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_NER3600 |
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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: 0.4022 |
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- Precision: 0.9708 |
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- Recall: 0.9629 |
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- F1: 0.9669 |
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- Accuracy: 0.9638 |
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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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| 0.1486 | 1.0 | 601 | 0.2452 | 0.9584 | 0.9499 | 0.9541 | 0.9494 | |
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| 0.0464 | 2.0 | 1202 | 0.2348 | 0.9658 | 0.9590 | 0.9624 | 0.9589 | |
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| 0.0265 | 3.0 | 1803 | 0.2845 | 0.9659 | 0.9599 | 0.9629 | 0.9592 | |
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| 0.0164 | 4.0 | 2404 | 0.3016 | 0.9689 | 0.9613 | 0.9650 | 0.9619 | |
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| 0.0063 | 5.0 | 3005 | 0.3531 | 0.9699 | 0.9623 | 0.9661 | 0.9631 | |
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| 0.0043 | 6.0 | 3606 | 0.3540 | 0.9701 | 0.9620 | 0.9660 | 0.9628 | |
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| 0.0033 | 7.0 | 4207 | 0.3730 | 0.9708 | 0.9630 | 0.9669 | 0.9638 | |
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| 0.0023 | 8.0 | 4808 | 0.3796 | 0.9710 | 0.9631 | 0.9670 | 0.9640 | |
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| 0.0019 | 9.0 | 5409 | 0.3892 | 0.9712 | 0.9634 | 0.9673 | 0.9642 | |
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| 0.0011 | 10.0 | 6010 | 0.4022 | 0.9708 | 0.9629 | 0.9669 | 0.9638 | |
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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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