nli deberta v3 large

提供商cross-encoder
分类zero-shot-classification
许可证apache-2.0
下载量536
星标0

简介

nli-deberta-v3-large 是一款基于 DeBERTa-v3 架构的高性能交叉编码器（Cross-Encoder），专门用于自然语言推理（NLI）和零样本分类。与常见的向量检索模型不同，它通过将文本对同时输入模型来判断逻辑关系，因此在判断语义矛盾或蕴含时精度极高。对于开发者而言，它最实用的场景是作为“精排”模型，在初筛出候选结果后，用它来做最终的准确性校验。由于其计算开销高于双编码器，建议在小规模数据集或对精度要求极高的分类任务中使用。

核心亮点

  • 基于 DeBERTa-v3，语义理解能力处于顶尖水平
  • 支持零样本分类，无需标注数据即可快速上手
  • 交叉编码器架构，判别精度远超常规向量模型
  • 适用于文本精排、逻辑校验及复杂分类场景

使用方法

安装依赖
# 安装 Hugging Face transformers
pip install transformers torch
SDK 使用
# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("cross-encoder/nli-deberta-v3-large")
tokenizer = AutoTokenizer.from_pretrained("cross-encoder/nli-deberta-v3-large")

Hugging Face 下载

我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 huggingface_hub：

操作指引 
pip install -U huggingface_hub

命令行下载

下载完整模型库

下载完整模型库 
huggingface-cli download cross-encoder/nli-deberta-v3-large

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例） 
huggingface-cli download cross-encoder/nli-deberta-v3-large config.json --local-dir ./dir

更多命令行下载选项，可参见官方文档

SDK 下载

SDK 下载 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('cross-encoder/nli-deberta-v3-large')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://huggingface.co/cross-encoder/nli-deberta-v3-large

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/cross-encoder/nli-deberta-v3-large

模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。

PyTorch / Transformers 使用

安装 Transformers

安装 Transformers 
pip install -U transformers torch

模型加载和推理

模型加载和推理 
from transformers import AutoModelForCausalLM, AutoTokenizer

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

模型下载

我们推荐使用命令行或者 ModelScope SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 ModelScope：

操作指引 
pip install modelscope

命令行下载

下载完整模型库

下载完整模型库 
modelscope download --model cross-encoder/nli-deberta-v3-large

下载单个文件到指定本地文件夹（以下载 README.md 到当前路径下 dir 目录为例）

下载单个文件到指定本地文件夹（以下载 README.md 到当前路径下 dir 目录为例） 
modelscope download --model cross-encoder/nli-deberta-v3-large README.md --local_dir ./dir

更多更丰富的命令行下载选项，可参见具体文档

SDK 下载

SDK 下载 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('cross-encoder/nli-deberta-v3-large')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://www.modelscope.cn/cross-encoder/nli-deberta-v3-large.git

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/cross-encoder/nli-deberta-v3-large.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook 快速开发

下载并安装 ModelScope library

下载并安装 ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

模型加载和推理

模型加载和推理 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'cross-encoder/nli-deberta-v3-large')

完整文档

来源: HuggingFace

---
language: en
pipeline_tag: zero-shot-classification
tags:

  • transformers

datasets:
  • nyu-mll/multi_nli

  • stanfordnlp/snli

metrics:
  • accuracy

license: apache-2.0
base_model:
  • microsoft/deberta-v3-large

library_name: sentence-transformers
---

Cross-Encoder for Natural Language Inference


This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-large

Training Data


The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.

Performance


  • Accuracy on SNLI-test dataset: 92.20

  • Accuracy on MNLI mismatched set: 90.49


For futher evaluation results, see SBERT.net - Pretrained Cross-Encoder.

Usage



Pre-trained models can be used like this:
``python
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/nli-deberta-v3-large')
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.')])

#Convert scores to labels
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
`

Usage with Transformers AutoModel


You can use the model also directly with Transformers library (without SentenceTransformers library):
`python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

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

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)
`

Zero-Shot Classification


This model can also be used for zero-shot-classification:
`python
from transformers import pipeline

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

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

查看官方来源