---
language: es
license: gpl-3.0
tags:
- PyTorch
- Transformers
- Token Classification
- roberta
- roberta-base-bne
widget:
- text: "Fue antes de llegar a Sigüeiro, en el Camino de Santiago."
- text: "Si te metes en el Franco desde la Alameda, vas hacia la Catedral."
- text: "Y allí precisamente es Santiago el patrón del pueblo."
model-index:
- name: es_trf_ner_cds_bne-base
  results: []
---

# Introduction

This model is a fine-tuned version of [roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) for Named-Entity Recognition, in the domain of tourism related to the Way of Saint Jacques. It recognizes four types of entities: location (LOC), organizations (ORG), person (PER) and miscellaneous (MISC).

## Usage

You can use this model with Transformers *pipeline* for NER.

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

tokenizer = AutoTokenizer.from_pretrained("es_trf_ner_cds_bne-base")
model = AutoModelForTokenClassification.from_pretrained("es_trf_ner_cds_bne-base")

example = "Fue antes de llegar a Sigüeiro, en el Camino de Santiago. Si te metes en el Franco desde la Alameda, vas hacia la Catedral. Y allí precisamente es Santiago el patrón del pueblo."
ner_pipe = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")

for ent in ner_pipe(example):
    print(ent)
```

## Dataset

ToDo

## Model performance

entity|precision|recall|f1
-|-|-|-
LOC|0.986|0.982|0.984
MISC|0.800|0.911|0.852
ORG|0.896|0.779|0.833
PER|0.953|0.937|0.945
micro avg|0.967|0.971|0.969
macro avg|0.909|0.902|0.903
weighted avg|0.968|0.971|0.969

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3