mrm8488/TinyBERT-spanish-uncased-finetuned-ner

Spanish TinyBERT + NER

This model is a fine-tuned on NER-C of a Spanish Tiny Bert model I created using distillation for NER downstream task. The size of the model is 55MB

Details of the downstream task (NER) - Dataset

• Dataset: CONLL Corpora ES

I preprocessed the dataset and split it as train / dev (80/20)

Dataset	# Examples
Train	8.7 K
Dev	2.2 K

• Fine-tune on NER script provided by Huggingface

• 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	70.00
Precision	67.83
Recall	71.46

Comparison:

Model	# F1 score	Size(MB)
bert-base-spanish-wwm-cased (BETO)	88.43	421
bert-spanish-cased-finetuned-ner	90.17	420
Best Multilingual BERT	87.38	681
TinyBERT-spanish-uncased-finetuned-ner (this one)	70.00	55

Model in action

Example of usage:

import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer

id2label = {
    "0": "B-LOC",
    "1": "B-MISC",
    "2": "B-ORG",
    "3": "B-PER",
    "4": "I-LOC",
    "5": "I-MISC",
    "6": "I-ORG",
    "7": "I-PER",
    "8": "O"
}

tokenizer = AutoTokenizer.from_pretrained('mrm8488/TinyBERT-spanish-uncased-finetuned-ner')
model = AutoModelForTokenClassification.from_pretrained('mrm8488/TinyBERT-spanish-uncased-finetuned-ner')
text ="Mis amigos están pensando viajar a Londres este verano."
input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)

outputs = model(input_ids)
last_hidden_states = outputs[0]

for m in last_hidden_states:
  for index, n in enumerate(m):
    if(index > 0 and index <= len(text.split(" "))):
      print(text.split(" ")[index-1] + ": " + id2label[str(torch.argmax(n).item())])
      
'''
Output:
--------
Mis: O
amigos: O
están: O
pensando: O
viajar: O
a: O
Londres: B-LOC
este: O
verano.: O
'''

Created by Manuel Romero/@mrm8488

Made with ♥ in Spain