---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT-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. -->

# BERT-full-finetuned-ner-pablo

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the n2c2 2018 dataset for the paper https://arxiv.org/abs/2409.19467.
It achieves the following results on the evaluation set:
- Loss: 0.0854
- Precision: 0.7857
- Recall: 0.7899
- F1: 0.7878
- Accuracy: 0.9747

## 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  | 0.1015          | 0.7485    | 0.7440 | 0.7462 | 0.9703   |
| No log        | 2.0   | 462  | 0.0878          | 0.7618    | 0.7750 | 0.7684 | 0.9728   |
| 0.2646        | 3.0   | 693  | 0.0859          | 0.7759    | 0.7912 | 0.7835 | 0.9737   |
| 0.2646        | 4.0   | 924  | 0.0854          | 0.7857    | 0.7899 | 0.7878 | 0.9747   |


### Framework versions

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