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---
language: es
license: gpl-3.0
tags:
- PyTorch
- Transformers
- Token Classification
- xlm-roberta
- xlm-roberta-large
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_xlm-large
results: []
---
# Introduction
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) 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_xlm-large")
model = AutoModelForTokenClassification.from_pretrained("es_trf_ner_cds_xlm-large")
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|0.973|0.983|0.978
MISC|0.760|0.788|0.773
ORG|0.885|0.701|0.783
PER|0.937|0.878|0.906
micro avg|0.953|0.958|0.955
macro avg|0.889|0.838|0.860
weighted avg|0.953|0.958|0.955
## 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
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