metadata

language: es
thumbnail: null

RuPERTa-base (Spanish RoBERTa) + NER 🎃🏷

This model is a fine-tuned on NER-C version of RuPERTa-base for NER downstream task.

Details of the downstream task (NER) - Dataset

Dataset: CONLL Corpora ES 📚

Dataset	# Examples
Train	329 K
Dev	40 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	77.55
Precision	75.53
Recall	79.68

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"
}

text ="Julien, CEO de HF, nació en Francia."
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:
--------
Julien,: I-PER
CEO: O
de: O
HF,: B-ORG
nació: I-PER
en: I-PER
Francia.: I-LOC
'''

Yeah! Not too bad 🎉

Created by Manuel Romero/@mrm8488

Made with ♥ in Spain