---
library_name: transformers
license: mit
base_model: emilyalsentzer/Bio_ClinicalBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BioClinicalBERT-full-finetuned-ner-pablo
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# BioClinicalBERT-full-finetuned-ner-pablo

This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the n2c2 2018 dataset for the paper https://arxiv.org/abs/2409.19467.
It achieves the following results on the evaluation set:
- Loss: 0.0834
- Precision: 0.7938
- Recall: 0.7935
- F1: 0.7936
- Accuracy: 0.9750

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 231  | 0.0943          | 0.7464    | 0.7612 | 0.7537 | 0.9720   |
| No log        | 2.0   | 462  | 0.0801          | 0.7861    | 0.7821 | 0.7841 | 0.9750   |
| 0.2571        | 3.0   | 693  | 0.0806          | 0.7900    | 0.7911 | 0.7906 | 0.9748   |
| 0.2571        | 4.0   | 924  | 0.0834          | 0.7938    | 0.7935 | 0.7936 | 0.9750   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1