cross-encoder/qnli-electra-base

Los modelos preentrenados se pueden utilizar de la siguiente manera:

from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Query1', 'Paragraph1'), ('Query2', 'Paragraph2')])

#e.g.
scores = model.predict([('How many people live in Berlin?', 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'), ('What is the size of New York?', 'New York City is famous for the Metropolitan Museum of Art.')])

Uso con Transformers AutoModel

Puedes usar el modelo también directamente con la biblioteca Transformers (sin la biblioteca SentenceTransformers):

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')

features = tokenizer(['How many people live in Berlin?', 'What is the size of New York?'], ['Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'],  padding=True, truncation=True, return_tensors="pt")

model.eval()
with torch.no_grad():
scores = torch.nn.functional.sigmoid(model(**features).logits)
print(scores)