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

# Description

This is a basic implementation of the multilingual model ["ixambert-base-cased"](https://huggingface.co/ixa-ehu/ixambert-base-cased), fine-tuned on SQuAD version 1.1, that is able to answer basic factual questions in English, Spanish and Basque. It extracts the span of text in which the answer is found.


### 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:

* Toxic: the tweet has at least some degree of toxicity.
* Very Toxic: the tweet has a strong degree of toxicity.


### How to use

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

```python
>>> 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, which were selected from a validation set:

%* Batch size = 32
%* Learning rate = 2e-5
%* Epochs = 3

%The optimizer used was AdamW and the loss optimized was binary cross-entropy with class weights proportional to the class imbalance.