---
library_name: transformers
base_model: raulgdp/xml-roberta-large-finetuned-ner
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xml-roberta-large-finetuned-ner-biobert
  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. -->

# xml-roberta-large-finetuned-ner-biobert

This model is a fine-tuned version of [raulgdp/xml-roberta-large-finetuned-ner](https://huggingface.co/raulgdp/xml-roberta-large-finetuned-ner) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0847
- Precision: 0.9493
- Recall: 0.9728
- F1: 0.9609
- Accuracy: 0.9815

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5713        | 1.0   | 612  | 0.1009          | 0.9281    | 0.9603 | 0.9439 | 0.9729   |
| 0.1048        | 2.0   | 1224 | 0.0903          | 0.9350    | 0.9730 | 0.9536 | 0.9779   |
| 0.0743        | 3.0   | 1836 | 0.0783          | 0.9520    | 0.9745 | 0.9631 | 0.9823   |
| 0.0568        | 4.0   | 2448 | 0.0855          | 0.9474    | 0.9712 | 0.9591 | 0.9802   |
| 0.0361        | 5.0   | 3060 | 0.0847          | 0.9493    | 0.9728 | 0.9609 | 0.9815   |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3