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bert-spanish-cased-finetuned-ner
mrm8488/bert-spanish-cased-finetuned-ner
bert-spanish-cased-finetuned-ner is a token classification model from mrm8488. Use it when you need labels on individual words, such as names. It is set up for transformers.
This model is a fine-tuned on NER-C version of the Spanish BERT cased (BETO) for NER downstream task.
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3.6M
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Repo size
1.3 GB
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28
Public
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At a glance
- Task
- Token Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- May 20, 2021
- SHA
- b11721d4
Try a prompt
Model card
From the Hugging Face model README
Spanish BERT (BETO) + NER
This model is a fine-tuned on NER-C version of the Spanish BERT cased (BETO) for NER downstream task.
Details of the downstream task (NER) - Dataset
I preprocessed the dataset and split it as train / dev (80/20)
| Dataset | # Examples |
|---|---|
| Train | 8.7 K |
| Dev | 2.2 K |
-
Labels covered:
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
Metrics on evaluation set:
| Metric | # score |
|---|---|
| F1 | 90.17 |
| Precision | 89.86 |
| Recall | 90.47 |
Comparison:
| Model | # F1 score | Size(MB) |
|---|---|---|
| bert-base-spanish-wwm-cased (BETO) | 88.43 | 421 |
| bert-spanish-cased-finetuned-ner (this one) | 90.17 | 420 |
| Best Multilingual BERT | 87.38 | 681 |
| TinyBERT-spanish-uncased-finetuned-ner | 70.00 | 55 |
Model in action
Fast usage with pipelines:
from transformers import pipeline
nlp_ner = pipeline(
"ner",
model="mrm8488/bert-spanish-cased-finetuned-ner",
tokenizer=(
'mrm8488/bert-spanish-cased-finetuned-ner',
{"use_fast": False}
))
text = 'Mis amigos están pensando viajar a Londres este verano'
nlp_ner(text)
#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
Created by Manuel Romero/@mrm8488
Made with <span style="color: #e25555;">♥</span> in Spain
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- Languages
- es
- Created
- Mar 2, 2022
- Updated
- May 20, 2021