自然语言推断的交叉编码器

该模型使用 SentenceTransformers Cross-Encoder 类进行训练。该模型基于 microsoft/deberta-v3-base

训练数据

该模型在 SNLI 和 MultiNLI 数据集上进行训练。对于给定的句子对，它将输出三个对应于标签的分数：矛盾（contradiction）、蕴含（entailment）、中性（neutral）。

性能

• SNLI-test 数据集上的准确率：92.38
• MNLI 不匹配集上的准确率：90.04

更多评估结果，请参阅 SBERT.net - 预训练交叉编码器。

使用方法

预训练模型可以这样使用：

from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/nli-deberta-v3-base')
scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])

#将分数转换为标签
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]

与 Transformers AutoModel 配合使用

您也可以直接使用 Transformers 库（不使用 SentenceTransformers 库）：

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-deberta-v3-base')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-v3-base')

features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'],  padding=True, truncation=True, return_tensors="pt")

model.eval()
with torch.no_grad():
    scores = model(**features).logits
    label_mapping = ['contradiction', 'entailment', 'neutral']
    labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
    print(labels)

零样本分类

该模型也可用于零样本分类：

from transformers import pipeline

classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-deberta-v3-base')

sent = "Apple just announced the newest iPhone X"
candidate_labels = ["technology", "sports", "politics"]
res = classifier(sent, candidate_labels)
print(res)