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
- Learning rate = na
- Epochs = 3
- Optimizer = AdamW
- Loss = na