alon-albalak/xlm-roberta-base-xquad

En Transformers

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "alon-albalak/xlm-roberta-base-xquad"

# 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)

En FARM

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

model_name = "alon-albalak/xlm-roberta-base-xquad"

# a) Obtener predicciones
nlp = QAInferencer.load(model_name)
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, rest_api_schema=True)

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

En Haystack

reader = FARMReader(model_name_or_path="alon-albalak/xlm-roberta-base-xquad")
# o
reader = TransformersReader(model="alon-albalak/xlm-roberta-base-xquad",tokenizer="alon-albalak/xlm-roberta-base-xquad")