---
library_name: transformers
base_model: raulgdp/xml-roberta-large-finetuned-ner
tags:
- generated_from_trainer
datasets:
- biobert_json
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER-finetuning-XML-RoBERTa-BIOBERT
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: biobert_json
      type: biobert_json
      config: Biobert_json
      split: validation
      args: Biobert_json
    metrics:
    - name: Precision
      type: precision
      value: 0.9497881598534296
    - name: Recall
      type: recall
      value: 0.9714235521461615
    - name: F1
      type: f1
      value: 0.9604840343919173
    - name: Accuracy
      type: accuracy
      value: 0.981362755330252
---

<!-- 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. -->

# NER-finetuning-XML-RoBERTa-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 the biobert_json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0946
- Precision: 0.9498
- Recall: 0.9714
- F1: 0.9605
- Accuracy: 0.9814

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1306        | 1.0   | 1224 | 0.1013          | 0.9299    | 0.9609 | 0.9451 | 0.9735   |
| 0.0996        | 2.0   | 2448 | 0.0932          | 0.9383    | 0.9656 | 0.9517 | 0.9777   |
| 0.0608        | 3.0   | 3672 | 0.0865          | 0.9493    | 0.9720 | 0.9605 | 0.9813   |
| 0.0445        | 4.0   | 4896 | 0.0927          | 0.9531    | 0.9729 | 0.9629 | 0.9819   |
| 0.0327        | 5.0   | 6120 | 0.0946          | 0.9498    | 0.9714 | 0.9605 | 0.9814   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1