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README.md
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---
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base_model: dmis-lab/biobert-v1.1
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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: biobert-finetuned-ner1
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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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# biobert-finetuned-ner1
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This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6653
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- Precision: 0.6417
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- Recall: 0.6985
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- F1: 0.6689
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- Accuracy: 0.8611
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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: 8
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- eval_batch_size: 8
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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 | 305 | 0.4133 | 0.6172 | 0.6674 | 0.6413 | 0.8529 |
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| 0.4433 | 2.0 | 610 | 0.4058 | 0.6121 | 0.6868 | 0.6473 | 0.8568 |
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| 0.4433 | 3.0 | 915 | 0.4456 | 0.6323 | 0.7015 | 0.6651 | 0.8594 |
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| 0.2431 | 4.0 | 1220 | 0.4708 | 0.6323 | 0.6925 | 0.6610 | 0.8612 |
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| 0.1563 | 5.0 | 1525 | 0.5084 | 0.6434 | 0.6998 | 0.6704 | 0.8652 |
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| 0.1563 | 6.0 | 1830 | 0.5655 | 0.6438 | 0.6801 | 0.6615 | 0.8607 |
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| 0.1038 | 7.0 | 2135 | 0.6173 | 0.6385 | 0.6918 | 0.6641 | 0.8591 |
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| 0.1038 | 8.0 | 2440 | 0.6352 | 0.6410 | 0.7011 | 0.6697 | 0.8608 |
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| 0.0754 | 9.0 | 2745 | 0.6600 | 0.6406 | 0.6951 | 0.6668 | 0.8609 |
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| 0.0599 | 10.0 | 3050 | 0.6653 | 0.6417 | 0.6985 | 0.6689 | 0.8611 |
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### Framework versions
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- Transformers 4.40.1
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- Pytorch 2.2.1+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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