deepset/minilm-uncased-squad2

En Haystack

Para realizar preguntas y respuestas a gran escala (es decir, muchos documentos en lugar de un único párrafo), también puede cargar el modelo en Haystack:

reader = FARMReader(model_name_or_path="deepset/minilm-uncased-squad2")
# o
reader = TransformersReader(model="deepset/minilm-uncased-squad2",tokenizer="deepset/minilm-uncased-squad2")

En Transformers

from transformers import AutoModelForQuestionAnswering,  AutoTokenizer, pipeline

model_name = "deepset/minilm-uncased-squad2"

# a) Obtener predicciones
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'Why is model conversion important?',
'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
}
res = nlp(QA_input)

# b) Cargar modelo y tokenizador
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)