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---
base_model: dmis-lab/biobert-base-cased-v1.2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: biobert-base-cased-v1.2-finetuned-NER
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# biobert-base-cased-v1.2-finetuned-NER
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0905
- Accuracy: 0.9765
- Precision (macro): 0.8596
- Recall (macro): 0.8601
- F1 (macro): 0.8563
- Precision (micro): 0.9765
- Recall (micro): 0.9765
- F1 (micro): 0.9765
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision (macro) | Recall (macro) | F1 (macro) | Precision (micro) | Recall (micro) | F1 (micro) |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:----------:|:-----------------:|:--------------:|:----------:|
| No log | 1.0 | 152 | 0.1382 | 0.9621 | 0.8016 | 0.6594 | 0.6754 | 0.9621 | 0.9621 | 0.9621 |
| No log | 2.0 | 304 | 0.0907 | 0.9740 | 0.8363 | 0.7827 | 0.7993 | 0.9740 | 0.9740 | 0.9740 |
| No log | 3.0 | 456 | 0.0811 | 0.9750 | 0.8661 | 0.8285 | 0.8261 | 0.9750 | 0.9750 | 0.9750 |
| 0.1768 | 4.0 | 608 | 0.0829 | 0.9738 | 0.8322 | 0.8581 | 0.8410 | 0.9738 | 0.9738 | 0.9738 |
| 0.1768 | 5.0 | 760 | 0.0786 | 0.9755 | 0.8350 | 0.8737 | 0.8526 | 0.9755 | 0.9755 | 0.9755 |
| 0.1768 | 6.0 | 912 | 0.0866 | 0.9766 | 0.8539 | 0.8491 | 0.8490 | 0.9766 | 0.9766 | 0.9766 |
| 0.0496 | 7.0 | 1064 | 0.0828 | 0.9757 | 0.8454 | 0.8563 | 0.8494 | 0.9757 | 0.9757 | 0.9757 |
| 0.0496 | 8.0 | 1216 | 0.0932 | 0.9754 | 0.8416 | 0.8622 | 0.8511 | 0.9754 | 0.9754 | 0.9754 |
| 0.0496 | 9.0 | 1368 | 0.0939 | 0.9752 | 0.8368 | 0.8617 | 0.8483 | 0.9752 | 0.9752 | 0.9752 |
| 0.0297 | 10.0 | 1520 | 0.0955 | 0.9759 | 0.8426 | 0.8597 | 0.8500 | 0.9759 | 0.9759 | 0.9759 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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