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---
library_name: transformers
license: mit
base_model: emilyalsentzer/Bio_ClinicalBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Bio_ClinicalBERT-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. -->
# Bio_ClinicalBERT-finetuned-ner
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1920
- Precision: 0.7879
- Recall: 0.8752
- F1: 0.8292
- Accuracy: 0.9456
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1019 | 1.0 | 201 | 0.2103 | 0.7146 | 0.8483 | 0.7758 | 0.9310 |
| 0.0457 | 2.0 | 402 | 0.1856 | 0.7642 | 0.8627 | 0.8104 | 0.9405 |
| 0.0189 | 3.0 | 603 | 0.1830 | 0.7769 | 0.8708 | 0.8212 | 0.9431 |
| 0.0237 | 4.0 | 804 | 0.1893 | 0.7739 | 0.8722 | 0.8201 | 0.9449 |
| 0.0703 | 5.0 | 1005 | 0.1920 | 0.7879 | 0.8752 | 0.8292 | 0.9456 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Tokenizers 0.20.3
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