---
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-es-en
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      config: es
      split: validation
      args: es
    metrics:
    - name: Precision
      type: precision
      value: 0.8694376140239549
    - name: Recall
      type: recall
      value: 0.8933170334148329
    - name: F1
      type: f1
      value: 0.8812155806568316
    - name: Accuracy
      type: accuracy
      value: 0.9448179020644737
---

<!-- 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-es-en

This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2564
- Precision: 0.8694
- Recall: 0.8933
- F1: 0.8812
- Accuracy: 0.9448

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2459        | 1.0   | 2500 | 0.2378          | 0.8169    | 0.8606 | 0.8382 | 0.9304   |
| 0.1441        | 2.0   | 5000 | 0.2468          | 0.8618    | 0.8876 | 0.8745 | 0.9429   |
| 0.0972        | 3.0   | 7500 | 0.2564          | 0.8694    | 0.8933 | 0.8812 | 0.9448   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2