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---
license: mit
base_model: emilyalsentzer/Bio_ClinicalBERT
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Bio_ClinicalBERT-medical-text-classification
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-medical-text-classification
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: 1.8941
- Accuracy: 0.273
- Precision: 0.2486
- Recall: 0.273
- F1: 0.2532
## 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: 5e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.4866 | 1.0 | 250 | 2.5436 | 0.355 | 0.1460 | 0.355 | 0.2036 |
| 1.9145 | 2.0 | 500 | 2.0555 | 0.369 | 0.2437 | 0.369 | 0.2406 |
| 1.849 | 3.0 | 750 | 1.8421 | 0.321 | 0.2862 | 0.321 | 0.2949 |
| 1.4025 | 4.0 | 1000 | 1.7678 | 0.325 | 0.2950 | 0.325 | 0.2957 |
| 1.311 | 5.0 | 1250 | 1.8007 | 0.312 | 0.2654 | 0.312 | 0.2743 |
| 1.2112 | 6.0 | 1500 | 1.8941 | 0.273 | 0.2486 | 0.273 | 0.2532 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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