albert-base-v2-finetuned-squad

knlu1016
Pregunta y respuesta

Este modelo es una versión ajustada de albert-base-v2 en el conjunto de datos squad. Es capaz de responder preguntas basadas en el contexto proporcionado.

Como usar

Procedimiento de uso

from transformers import pipeline
model = pipeline('question-answering', model='knlu1016/albert-base-v2-finetuned-squad')
context = 'My name is Wolfgang and I live in Berlin'
result = model(question='Where do I live?', context=context)
print(result)

Preguntas y contextos de ejemplo

[
  {"text":"Where do I live?","context":"My name is Wolfgang and I live in Berlin"},
  {"text":"Where do I live?","context":"My name is Sarah and I live in London"},
  {"text":"What's my name?","context":"My name is Clara and I live in Berkeley."},
  {"text":"Which name is also used to describe the Amazon rainforest in English?","context":"The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain 'Amazonas' in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."}
]

Funcionalidades

Capacidad para responder preguntas
Utiliza la biblioteca de Transformers
Implementado en PyTorch
Compatible con TensorBoard
Modelo ajustado utilizando el conjunto de datos SQuAD
Licencia: Apache-2.0
Optimizado con Adam con betas=(0.9,0.999) y epsilon=1e-08
Planificador de tasa de aprendizaje: lineal
Número de épocas de entrenamiento: 4

Casos de uso

Responder preguntas basadas en contexto
Expandir las funcionalidades de chatbots
Integración en sistemas automatizados de atención al cliente
Utilización en aplicaciones educativas para proporcionar respuestas a preguntas comunes