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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_NER2 |
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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_NER2 |
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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: 0.5316 |
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- Precision: 0.9596 |
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- Recall: 0.9442 |
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- F1: 0.9518 |
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- Accuracy: 0.9428 |
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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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| 0.1601 | 1.0 | 793 | 0.2740 | 0.9603 | 0.9459 | 0.9530 | 0.9444 | |
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| 0.0276 | 2.0 | 1586 | 0.3631 | 0.9599 | 0.9447 | 0.9522 | 0.9431 | |
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| 0.0139 | 3.0 | 2379 | 0.3745 | 0.9602 | 0.9445 | 0.9523 | 0.9430 | |
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| 0.0054 | 4.0 | 3172 | 0.4634 | 0.9601 | 0.9441 | 0.9521 | 0.9429 | |
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| 0.0039 | 5.0 | 3965 | 0.4709 | 0.9594 | 0.9440 | 0.9517 | 0.9423 | |
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| 0.0018 | 6.0 | 4758 | 0.5042 | 0.9587 | 0.9440 | 0.9513 | 0.9426 | |
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| 0.0011 | 7.0 | 5551 | 0.5223 | 0.9598 | 0.9439 | 0.9518 | 0.9425 | |
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| 0.0009 | 8.0 | 6344 | 0.5241 | 0.9594 | 0.9438 | 0.9515 | 0.9424 | |
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| 0.0004 | 9.0 | 7137 | 0.5277 | 0.9595 | 0.9441 | 0.9517 | 0.9428 | |
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| 0.0004 | 10.0 | 7930 | 0.5316 | 0.9596 | 0.9442 | 0.9518 | 0.9428 | |
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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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