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---
language: es
license: cc-by-4.0
---
# NER-fine-tuned-BETO: model fine-tuned from BETO for NER task.
---
Language: es
Datasets:
- conll2002
- Babelscape/wikineural
## Introduction
[NER-fine-tuned-BETO] is a NER model that was fine-tuned from BETO on the 2002 Conll and the WikiNEuRal spanish datasets.
Model was trained on the Conll 2002 train dataset (~8320 sentences) and a bootstrapped dataset of WikiNEuRal, where we re-evaluate the dataset and only keep the sentences where all the labels matched the predictions made.
Model was evaluated on the test dataset of Conll2002.
## Training data
Training data was classified as follow:
|Abbreviation| Description |
|:----------:|:-------------:|
| O | Outside of NE |
| PER | Person’s name |
| ORG | Organization |
| LOC | Location |
| MISC | Miscellaneous |
Alongside the IOB formatting, this is:
- B-LABEL if the word is at the beggining of the entity.
- I-LABEL if the word is part of the entity name, but not the first word.
## How to use NER-fine-tuned-BETO with HuggingFace
Load the model and its tokenizer :
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("NazaGara/NER-fine-tuned-BETO", use_auth_token=True)
model = AutoModelForTokenClassification.from_pretrained("NazaGara/NER-fine-tuned-BETO", use_auth_token=True)
nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp('Ignacio se fue de viaje por Buenos aires')
[{'entity_group': 'PER',
'score': 0.9997764,
'word': 'Ignacio',
'start': 0,
'end': 7},
{'entity_group': 'LOC',
'score': 0.9997932,
'word': 'Buenos aires',
'start': 28,
'end': 40}]
```
## Model Performance
Overall
| precision | recall | f1-score |
|:---------:|:------:|:--------:|
| 0.9833 | 0.8950 | 0.8998 |
By classes
| class | precision | recall | f1-score |
|:------:|:---------:|:------:|:--------:|
| O | 0.9958 | 0.9965 | 0.990 |
| B-PER | 0.9572 | 0.9741 | 0.9654 |
| I-PER | 0.9487 | 0.9921 | 0.9699 |
| B-ORG | 0.8823 | 0.9264 | 0.9038 |
| I-ORG | 0.9253 | 0.9264 | 0.9117 |
| B-LOC | 0.8967 | 0.8736 | 0.8850 |
| I-LOC | 0.8870 | 0.8215 | 0.8530 |
| B-MISC | 0.7541 | 0.7964 | 0.7747 |
| I-MISC | 0.9026 | 0.7827 | 0.8384 |
|