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mrm8488/bert-spanish-cased-finetuned-ner mrm8488/bert-spanish-cased-finetuned-ner
915 downloads
last 30 days

pytorch

tf

Contributed by

mrm8488 Manuel Romero
88 models

How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mrm8488/bert-spanish-cased-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("mrm8488/bert-spanish-cased-finetuned-ner")

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 splitted it as train / dev (80/20)

Dataset # Examples
Train 8.7 K
Dev 2.2 K
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 in Spain