metadata

language:
  - en
  - es
  - eu
datasets:
  - squad

Description

This is a basic implementation of the multilingual model "ixambert-base-cased", fine-tuned on SQuAD version 1.1, that is able to answer basic factual questions in English, Spanish and Basque.

Outputs

The model predicts a span of text from the context and a score for the probability for that span to be the correct answer.

How to use

The model can be used directly with a question-answering pipeline:

>>> from transformers import pipeline
>>> context = "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820"
>>> question = "When was Florence Nightingale born?"
>>> qa = pipeline("question-answering", model="MarcBrun/ixambert-finetuned-squad")
>>> qa(question=question,context=context)
{'score': 0.9667195081710815, 'start': 101, 'end': 105, 'answer': '1820'}

Training procedure

The pre-trained model was fine-tuned for question answering using the following hyperparameters:

batch_size = na
n_epochs = 3
base_LM_model = "ixambert-base-cased"
max_seq_len = na
learning_rate = na
lr_schedule = na
warmup_proportion = na
doc_stride = na
max_query_length= na