electra-base-squad2

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "deepset/electra-base-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)

from farm.modeling.adaptive_model import AdaptiveModel
from farm.modeling.tokenization import Tokenizer
from farm.infer import Inferencer

model_name = "deepset/electra-base-squad2"

# a) Obtener predicciones
nlp = Inferencer.load(model_name, task_type="question_answering")
QA_input = [{"questions": ["Why is model conversion important?"],
"text": "The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks."}]
res = nlp.inference_from_dicts(dicts=QA_input)

# b) Cargar modelo y tokenizador
model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering")
tokenizer = Tokenizer.load(model_name)

from haystack import FARMReader, TransformersReader

# Para hacer QA a escala (es decir, muchos documentos en lugar de un solo párrafo), también puedes cargar el modelo en haystack:
reader = FARMReader(model_name_or_path="deepset/electra-base-squad2")
# o
reader = TransformersReader(model="deepset/electra-base-squad2",tokenizer="deepset/electra-base-squad2")