metadata
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", 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:
{'score': 0.9667195081710815, 'start': 101, 'end': 105, 'answer': '1820'}
How to use
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