Zamachi/albert-for-question-answering
Zamachi
Pregunta y respuesta
Este es un modelo de Albert optimizado para tareas de respuesta a preguntas. Utiliza la biblioteca Transformers y PyTorch. No está suficientemente activo para ser desplegado en la Inference API, pero se puede desplegar en Inference Endpoints.
Como usar
Para usar este modelo, puedes hacer uso de las siguientes estructuras de entrada y contexto en tu código:
{
"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
- Respuesta a preguntas basado en Albert
- Compatible con Inference Endpoints
- Compatible con PyTorch
- Enfocado en la región de EE.UU.
Casos de uso
- Uso en chatbots para proporcionar respuestas precisas a preguntas específicas.
- Automatización de servicios de atención al cliente.
- Sistemas de búsqueda y recuperación de información.
- Apoyo en la educación para responder a preguntas de estudiantes.