---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xglue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: xglue
      type: xglue
      config: ner
      split: validation.es
      args: ner
    metrics:
    - name: Precision
      type: precision
      value: 0.5184780231795321
    - name: Recall
      type: recall
      value: 0.5445567294441892
    - name: F1
      type: f1
      value: 0.5311974907583735
    - name: Accuracy
      type: accuracy
      value: 0.8905679788803479
---

<!-- 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. -->

# bert-finetuned-ner

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the xglue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8429
- Precision: 0.5185
- Recall: 0.5446
- F1: 0.5312
- Accuracy: 0.8906

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0782        | 1.0   | 1756 | 0.6432          | 0.4982    | 0.5420 | 0.5192 | 0.8972   |
| 0.039         | 2.0   | 3512 | 0.7474          | 0.4994    | 0.5496 | 0.5233 | 0.8908   |
| 0.0189        | 3.0   | 5268 | 0.8429          | 0.5185    | 0.5446 | 0.5312 | 0.8906   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2