README.md

---
language:
- en
- es
- eu
- multilingual
datasets:
- squad
widget:
- text: When was Florence Nightingale born?
  context: Florence Nightingale, known for being the founder of modern nursing, was
    born in Florence, Italy, in 1820.
  example_title: English
- text: �Por qu� provincias pasa el Tajo?
  context: 'El Tajo es el r�o m�s largo de la pen�nsula ib�rica, a la que atraviesa
    en su parte central, siguiendo un rumbo este-oeste, con una leve inclinaci�n hacia
    el suroeste, que se acent�a cuando llega a Portugal, donde recibe el nombre de
    Tejo.

    Nace en los montes Universales, en la sierra de Albarrac�n, sobre la rama occidental
    del sistema Ib�rico y, despu�s de recorrer 1007 km, llega al oc�ano Atl�ntico
    en la ciudad de Lisboa. En su desembocadura forma el estuario del mar de la Paja,
    en el que vierte un caudal medio de 456 m�/s. En sus primeros 816 km atraviesa
    Espa�a, donde discurre por cuatro comunidades aut�nomas (Arag�n, Castilla-La Mancha,
    Madrid y Extremadura) y un total de seis provincias (Teruel, Guadalajara, Cuenca,
    Madrid, Toledo y C�ceres).'
  example_title: Espa�ol
- text: Zer beste izenak ditu Tartalo?
  context: 'Tartalo euskal mitologiako izaki begibakar artzain erraldoia da. Tartalo
    izena zenbait euskal hizkeratan herskari-bustidurarekin ahoskatu ohi denez, horrelaxe
    ere idazten da batzuetan: Ttarttalo. Euskal Herriko zenbait tokitan, Torto edo
    Anxo ere esaten diote.'
  example_title: Euskara
---

# ixambert-base-cased finetuned for QA

This is a basic implementation of the multilingual model ["ixambert-base-cased"](https://huggingface.co/ixa-ehu/ixambert-base-cased), fine-tuned on SQuAD v1.1, that is able to answer basic factual questions in English, Spanish and Basque.

## Overview

* **Language model:** ixambert-base-cased
* **Languages:** English, Spanish and Basque
* **Downstream task:** Extractive QA
* **Training data:** SQuAD v1.1
* **Eval data:** SQuAD v1.1
* **Infrastructure:** 1x GeForce RTX 2080

## Outputs

The model outputs the answer to the question, the start and end positions of the answer in the original context, and a score for the probability for that span of text to be the correct answer. For example:

```python
{'score': 0.9667195081710815, 'start': 101, 'end': 105, 'answer': '1820'}
```

## How to use

```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "MarcBrun/ixambert-finetuned-squad"

# To get predictions
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=model_name, tokenizer=model_name)
pred = qa(question=question,context=context)

# To load the model and tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```

## Hyperparameters

```
batch_size = 8
n_epochs = 3
learning_rate = 2e-5
optimizer = AdamW
lr_schedule = linear
max_seq_len = 384
doc_stride = 128
```