---
library_name: transformers
base_model: medicalai/ClinicalBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ClinicalBERT-full-finetuned-ner-pablo
  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. -->

# ClinicalBERT-full-finetuned-ner-pablo

This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1104
- Precision: 0.8025
- Recall: 0.8001
- F1: 0.8013
- Accuracy: 0.9745

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.9970 | 252  | 0.0878          | 0.7903    | 0.7758 | 0.7830 | 0.9748   |
| 0.1581        | 1.9980 | 505  | 0.0857          | 0.8096    | 0.7836 | 0.7964 | 0.9755   |
| 0.1581        | 2.9990 | 758  | 0.0872          | 0.8010    | 0.7867 | 0.7938 | 0.9740   |
| 0.0417        | 4.0    | 1011 | 0.0976          | 0.7950    | 0.8012 | 0.7981 | 0.9736   |
| 0.0417        | 4.9852 | 1260 | 0.1104          | 0.8025    | 0.8001 | 0.8013 | 0.9745   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1