README.md

metadata

language:
  - en
  - es
  - eu
  - multilingual
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 an experimental version of SQuAD1.1 in Basque (1/3 size of original SQuAD1.1), that is able to answer basic factual questions.

Overview

• Language model: ixambert-base-cased
• Languages: English, Spanish and Basque
• Downstream task: Extractive QA
• Training data: Experimental SQuAD1.1 in Basque
• Eval data: Experimental SQuAD1.1 in Basque
• 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-eu"

# 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