nli MiniLM2 L6 H768

提供商cross-encoder
分类natural-language-inference
许可证apache-2.0
下载量432
星标0

简介

nli MiniLM2 L6 H768 是一款轻量级的交叉编码器（Cross-Encoder），专门用于自然语言推理（NLI）任务。与常见的向量检索（Bi-Encoder）不同，它通过将两个句子同时输入模型来判断它们之间的逻辑关系（如蕴含、矛盾或中立），因此判断精度极高。由于参数量小且结构精简，它非常适合作为 RAG 架构中的“重排（Rerank）”环节，在初筛结果后进行精细化打分，以提升最终答案的准确率。对于开发者来说，该模型部署成本极低，是平衡推理速度与语义精度的高性价比选择。

核心亮点

  • 专注 NLI 推理，语义判断精度远超普通向量模型
  • 轻量级架构，极低延迟，适合端侧或实时推理
  • RAG 场景下的理想重排工具，显著提升检索质量
  • Apache-2.0 协议，商业友好且易于集成部署

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-MiniLM2-L6-H768")
tokenizer = AutoTokenizer.from_pretrained("cross-encoder/nli-MiniLM2-L6-H768")

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-MiniLM2-L6-H768

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-MiniLM2-L6-H768 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-MiniLM2-L6-H768')

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-MiniLM2-L6-H768

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-MiniLM2-L6-H768

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-MiniLM2-L6-H768')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-MiniLM2-L6-H768')

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-MiniLM2-L6-H768

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-MiniLM2-L6-H768 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-MiniLM2-L6-H768')

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-MiniLM2-L6-H768.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-MiniLM2-L6-H768.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-MiniLM2-L6-H768')

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:
  • nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large

library_name: sentence-transformers
---

Cross-Encoder for Natural Language Inference

This model was trained using SentenceTransformers Cross-Encoder class.

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

For 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-MiniLM2-L6-H768')
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-MiniLM2-L6-H768')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-MiniLM2-L6-H768')


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-MiniLM2-L6-H768')


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