---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- biobert_json
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-finetuned-ner
  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: 0.937991068905093
    - name: Recall
      type: recall
      value: 0.9717163436200738
    - name: F1
      type: f1
      value: 0.9545559134836631
    - name: Accuracy
      type: accuracy
      value: 0.9784621223416512
---

<!-- 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. -->

# xlm-roberta-finetuned-ner

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the biobert_json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0853
- Precision: 0.9380
- Recall: 0.9717
- F1: 0.9546
- Accuracy: 0.9785

## 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: 32
- eval_batch_size: 32
- 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
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 306  | 0.1343          | 0.9035    | 0.9289 | 0.9160 | 0.9646   |
| 0.4365        | 2.0   | 612  | 0.0985          | 0.9254    | 0.9662 | 0.9453 | 0.9746   |
| 0.4365        | 3.0   | 918  | 0.0833          | 0.9413    | 0.9684 | 0.9547 | 0.9788   |
| 0.0949        | 4.0   | 1224 | 0.0853          | 0.9380    | 0.9717 | 0.9546 | 0.9785   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3