---
license: mit
base_model: emilyalsentzer/Bio_ClinicalBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NLP-HIBA_DisTEMIST_fine_tuned_ClinicalBERT-pretrained-model
  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. -->

# NLP-HIBA_DisTEMIST_fine_tuned_ClinicalBERT-pretrained-model

This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2634
- Precision: 0.4559
- Recall: 0.4616
- F1: 0.4587
- Accuracy: 0.9334

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 71   | 0.2011          | 0.3859    | 0.3371 | 0.3598 | 0.9297   |
| No log        | 2.0   | 142  | 0.1970          | 0.3956    | 0.3894 | 0.3925 | 0.9283   |
| No log        | 3.0   | 213  | 0.2041          | 0.4140    | 0.3977 | 0.4057 | 0.9307   |
| No log        | 4.0   | 284  | 0.2167          | 0.4379    | 0.4110 | 0.4240 | 0.9348   |
| No log        | 5.0   | 355  | 0.2293          | 0.4430    | 0.4159 | 0.4290 | 0.9338   |
| No log        | 6.0   | 426  | 0.2447          | 0.4615    | 0.4375 | 0.4492 | 0.9356   |
| No log        | 7.0   | 497  | 0.2587          | 0.4601    | 0.4500 | 0.4550 | 0.9342   |
| 0.0861        | 8.0   | 568  | 0.2634          | 0.4559    | 0.4616 | 0.4587 | 0.9334   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1