---
library_name: transformers
base_model: allenai/biomed_roberta_base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BioMedRoBERTa-full-finetuned-ner-pablo
  results: []
---

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# BioMedRoBERTa-full-finetuned-ner-pablo

This model is a fine-tuned version of [allenai/biomed_roberta_base](https://huggingface.co/allenai/biomed_roberta_base) on the n2c2 2018 dataset for the paper https://arxiv.org/abs/2409.19467.
It achieves the following results on the evaluation set:
- Loss: 0.0739
- Precision: 0.8048
- Recall: 0.7953
- F1: 0.8000
- Accuracy: 0.9775

## 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  | 0.0877          | 0.7475    | 0.7719 | 0.7595 | 0.9733   |
| No log        | 2.0   | 462  | 0.0766          | 0.7797    | 0.7900 | 0.7848 | 0.9756   |
| 0.2598        | 3.0   | 693  | 0.0730          | 0.8042    | 0.7949 | 0.7995 | 0.9774   |
| 0.2598        | 4.0   | 924  | 0.0739          | 0.8048    | 0.7953 | 0.8000 | 0.9775   |


### Framework versions

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