Overview

The bge-m3-zeroshot-v2.0 is a specialized classification model designed for high-efficiency zero-shot tasks, enabling developers to categorize text into arbitrary labels without requiring task-specific fine-tuning. Built upon the robust BGE-M3 embedding architecture, it excels at semantic matching between input text and candidate labels, making it ideal for dynamic labeling systems where categories change frequently. For developers, this removes the bottleneck of collecting labeled datasets for every new class. It integrates easily into existing NLP pipelines via standard transformer libraries and provides a scalable alternative to traditional supervised classifiers, particularly when dealing with multi-lingual inputs or open-domain classification needs.

Highlights

Zero-shot classification without requiring labeled training data
Dynamic label assignment for flexible category management
Strong multi-lingual support inherited from BGE-M3 architecture
Efficient integration via standard Hugging Face transformers
MIT licensed for unrestricted commercial and private use

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("MoritzLaurer/bge-m3-zeroshot-v2.0")
tokenizer = AutoTokenizer.from_pretrained("MoritzLaurer/bge-m3-zeroshot-v2.0")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download MoritzLaurer/bge-m3-zeroshot-v2.0

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download MoritzLaurer/bge-m3-zeroshot-v2.0 config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('MoritzLaurer/bge-m3-zeroshot-v2.0')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/MoritzLaurer/bge-m3-zeroshot-v2.0

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/MoritzLaurer/bge-m3-zeroshot-v2.0

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('MoritzLaurer/bge-m3-zeroshot-v2.0')
tokenizer = AutoTokenizer.from_pretrained('MoritzLaurer/bge-m3-zeroshot-v2.0')

Full Documentation

来源: HuggingFace

---
language:

multilingual

tags:

text-classification

zero-shot-classification

base_model: BAAI/bge-m3-retromae
pipeline_tag: zero-shot-classification
library_name: transformers
license: mit
---

Model description: bge-m3-zeroshot-v2.0

zeroshot-v2.0 series of models

Models in this series are designed for efficient zeroshot classification with the Hugging Face pipeline. These models can do classification without training data and run on both GPUs and CPUs. An overview of the latest zeroshot classifiers is available in my Zeroshot Classifier Collection.

The main update of this zeroshot-v2.0 series of models is that several models are trained on fully commercially-friendly data for users with strict license requirements.

These models can do one universal classification task: determine whether a hypothesis is "true" or "not true" given a text
(entailment vs. not_entailment).
This task format is based on the Natural Language Inference task (NLI).
The task is so universal that any classification task can be reformulated into this task by the Hugging Face pipeline.

Training data

Models with a "-c" in the name are trained on two types of fully commercially-friendly data: 1. Synthetic data generated with Mixtral-8x7B-Instruct-v0.1. I first created a list of 500+ diverse text classification tasks for 25 professions in conversations with Mistral-large. The data was manually curated. I then used this as seed data to generate several hundred thousand texts for these tasks with Mixtral-8x7B-Instruct-v0.1. The final dataset used is available in the synthetic_zeroshot_mixtral_v0.1 dataset in the subset mixtral_written_text_for_tasks_v4. Data curation was done in multiple iterations and will be improved in future iterations. 2. Two commercially-friendly NLI datasets: (MNLI, FEVER-NLI). These datasets were added to increase generalization. 3. Models without a "-c" in the name also included a broader mix of training data with a broader mix of licenses: ANLI, WANLI, LingNLI, and all datasets in this list where used_in_v1.1==True.

How to use the models

python

#!pip install transformers[sentencepiece]
from transformers import pipeline
text = "Angela Merkel is a politician in Germany and leader of the CDU"
hypothesis_template = "This text is about {}"
classes_verbalized = ["politics", "economy", "entertainment", "environment"]
zeroshot_classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v2.0")  # change the model identifier here
output = zeroshot_classifier(text, classes_verbalized, hypothesis_template=hypothesis_template, multi_label=False)
print(output)

multi_label=False forces the model to decide on only one class. multi_label=True enables the model to choose multiple classes.

Metrics

The models were evaluated on 28 different text classification tasks with the f1_macro metric.
The main reference point is facebook/bart-large-mnli which is, at the time of writing (03.04.24), the most used commercially-friendly 0-shot classifier.

!results_aggreg_v2.0

| | facebook/bart-large-mnli | roberta-base-zeroshot-v2.0-c | roberta-large-zeroshot-v2.0-c | deberta-v3-base-zeroshot-v2.0-c | deberta-v3-base-zeroshot-v2.0 (fewshot) | deberta-v3-large-zeroshot-v2.0-c | deberta-v3-large-zeroshot-v2.0 (fewshot) | bge-m3-zeroshot-v2.0-c | bge-m3-zeroshot-v2.0 (fewshot) |
|:---------------------------|---------------------------:|-----------------------------:|------------------------------:|--------------------------------:|-----------------------------------:|---------------------------------:|------------------------------------:|-----------------------:|--------------------------:|
| all datasets mean | 0.497 | 0.587 | 0.622 | 0.619 | 0.643 (0.834) | 0.676 | 0.673 (0.846) | 0.59 | (0.803) |
| amazonpolarity (2) | 0.937 | 0.924 | 0.951 | 0.937 | 0.943 (0.961) | 0.952 | 0.956 (0.968) | 0.942 | (0.951) |
| imdb (2) | 0.892 | 0.871 | 0.904 | 0.893 | 0.899 (0.936) | 0.923 | 0.918 (0.958) | 0.873 | (0.917) |
| appreviews (2) | 0.934 | 0.913 | 0.937 | 0.938 | 0.945 (0.948) | 0.943 | 0.949 (0.962) | 0.932 | (0.954) |
| yelpreviews (2) | 0.948 | 0.953 | 0.977 | 0.979 | 0.975 (0.989) | 0.988 | 0.985 (0.994) | 0.973 | (0.978) |
| rottentomatoes (2) | 0.83 | 0.802 | 0.841 | 0.84 | 0.86 (0.902) | 0.869 | 0.868 (0.908) | 0.813 | (0.866) |
| emotiondair (6) | 0.455 | 0.482 | 0.486 | 0.459 | 0.495 (0.748) | 0.499 | 0.484 (0.688) | 0.453 | (0.697) |
| emocontext (4) | 0.497 | 0.555 | 0.63 | 0.59 | 0.592 (0.799) | 0.699 | 0.676 (0.81) | 0.61 | (0.798) |
| empathetic (32) | 0.371 | 0.374 | 0.404 | 0.378 | 0.405 (0.53) | 0.447 | 0.478 (0.555) | 0.387 | (0.455) |
| financialphrasebank (3) | 0.465 | 0.562 | 0.455 | 0.714 | 0.669 (0.906) | 0.691 | 0.582 (0.913) | 0.504 | (0.895) |
| banking77 (7

bge m3 zeroshot v2.0

Overview

Highlights

Usage

Hugging Face Download

CLI Download

SDK Download

Git Download

PyTorch / Transformers Usage

Full Documentation

Model description: bge-m3-zeroshot-v2.0

zeroshot-v2.0 series of models

Training data

How to use the models

Metrics