metadata

language: es
thumbnail: https://i.imgur.com/DUlT077.jpg
widget:
  - text: España es un país muy <mask> en la UE

RuPERTa: the Spanish RoBERTa 🎃

RuPERTa-base (uncased) is a RoBERTa model trained on a uncased verison of big Spanish corpus. RoBERTa iterates on BERT's pretraining procedure, including training the model longer, with bigger batches over more data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the masking pattern applied to the training data. The architecture is the same as roberta-base:

roberta.base: RoBERTa using the BERT-base architecture 125M params

Benchmarks 🧾

WIP (I continue working on it) 🚧

Task/Dataset	F1	Precision	Recall	Fine-tuned model
POS	97.39	97.47	97.32	RuPERTa-base-finetuned-pos
NER	77.55	75.53	79.68	RuPERTa-base-finetuned-ner
SQUAD-es v1	to-do			RuPERTa-base-finetuned-squadv1
SQUAD-es v2	to-do			RuPERTa-base-finetuned-squadv2

Model in action 🔨

Usage for POS and NER 🏷

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/RuPERTa-base-finetuned-ner')
model = AutoModelForTokenClassification.from_pretrained('mrm8488/RuPERTa-base-finetuned-ner')

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
'''

For POS just change the id2label dictionary and the model path to mrm8488/RuPERTa-base-finetuned-pos

Fast usage for LM with `pipelines` 🧪

from transformers import AutoModelWithLMHead, AutoTokenizer
model = AutoModelWithLMHead.from_pretrained('mrm8488/RuPERTa-base')
tokenizer = AutoTokenizer.from_pretrained("mrm8488/RuPERTa-base", do_lower_case=True)

from transformers import pipeline

pipeline_fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)

pipeline_fill_mask("España es un país muy <mask> en la UE")

[
  {
    "score": 0.1814306527376175,
    "sequence": "<s> españa es un país muy importante en la ue</s>",
    "token": 1560
  },
  {
    "score": 0.024842597544193268,
    "sequence": "<s> españa es un país muy fuerte en la ue</s>",
    "token": 2854
  },
  {
    "score": 0.02473250962793827,
    "sequence": "<s> españa es un país muy pequeño en la ue</s>",
    "token": 2948
  },
  {
    "score": 0.023991240188479424,
    "sequence": "<s> españa es un país muy antiguo en la ue</s>",
    "token": 5240
  },
  {
    "score": 0.0215945765376091,
    "sequence": "<s> españa es un país muy popular en la ue</s>",
    "token": 5782
  }
]

Acknowledgments

I thank 🤗/transformers team for answering my doubts and Google for helping me with the TensorFlow Research Cloud program.

Created by Manuel Romero/@mrm8488

Made with ♥ in Spain