---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: finetuned-xlm-roberta-base-NER
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: ncbi_disease
      type: ncbi_disease
      config: ncbi_disease
      split: test
      args: ncbi_disease
    metrics:
    - name: Precision
      type: precision
      value: 0.7974434611602753
    - name: Recall
      type: recall
      value: 0.8447916666666667
    - name: F1
      type: f1
      value: 0.8204350025290845
    - name: Accuracy
      type: accuracy
      value: 0.9804874066212189
---

<!-- 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. -->

# finetuned-xlm-roberta-base-NER

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0589
- Precision: 0.7974
- Recall: 0.8448
- F1: 0.8204
- Accuracy: 0.9805

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 340  | 0.0809          | 0.6839    | 0.8698 | 0.7657 | 0.9723   |
| 0.1092        | 2.0   | 680  | 0.0589          | 0.7974    | 0.8448 | 0.8204 | 0.9805   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0