Quora重复问题检测的交叉编码器

此模型是使用 SentenceTransformers Cross-Encoder 类进行训练的。

训练数据

给定一个问题和段落，问题能否通过段落回答？这些模型是在 GLUE QNLI 数据集上进行训练的，该数据集将 SQuAD dataset 转化为了一个NLI任务。

性能

有关此模型的性能结果，请参见[SBERT.net预训练交叉编码器] [ https://www.sbert.net/docs/pretrained_cross-encoders.html] ]。

用法

预训练模型可以像这样使用：

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.')])

使用Transformers AutoModel

您还可以直接使用Transformers库与模型一起使用（不使用SentenceTransformers库）：

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)