bart large mnli

Providerfacebook
Categoryzero-shot-classification
Licensemit
Downloads835
Stars0

Overview

BART-large-MNLI is a specialized encoder-decoder model fine-tuned on the Multi-Genre Natural Language Inference (MNLI) dataset. Unlike standard classifiers that require a fixed set of labels during training, this model treats zero-shot classification as an entailment problem. By phrasing labels as hypotheses (e.g., 'This text is about politics'), developers can categorize unstructured text into arbitrary classes on the fly without retraining. It is particularly effective for dynamic tagging systems and content routing where categories evolve frequently. Integration is straightforward via the Hugging Face Transformers library, offering a flexible alternative to training custom BERT-based classifiers for niche datasets.

Highlights

  • Zero-shot classification without requiring label-specific training data
  • Handles dynamic categories via natural language hypothesis testing
  • Seamless integration with Hugging Face Transformers pipeline
  • Strong performance on diverse, multi-genre text corpora
  • Permissive MIT license for commercial and open-source 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("facebook/bart-large-mnli")
tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-mnli")

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

Download the full repository 
huggingface-cli download facebook/bart-large-mnli

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

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download facebook/bart-large-mnli config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('facebook/bart-large-mnli')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/facebook/bart-large-mnli

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/facebook/bart-large-mnli

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

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('facebook/bart-large-mnli')
tokenizer = AutoTokenizer.from_pretrained('facebook/bart-large-mnli')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance 
pip install modelscope

CLI Download

Download the full repository

Download the full repository 
modelscope download --model facebook/bart-large-mnli

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model facebook/bart-large-mnli README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('facebook/bart-large-mnli')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/facebook/bart-large-mnli.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/facebook/bart-large-mnli.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 Quickstart

Install the ModelScope library

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

Load the model and run inference

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'facebook/bart-large-mnli')

Full Documentation

来源: HuggingFace

---
license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
pipeline_tag: zero-shot-classification
datasets:

  • multi_nli

---

bart-large-mnli

This is the checkpoint for bart-large after being trained on the MultiNLI (MNLI) dataset.

Additional information about this model:




NLI-based Zero Shot Text Classification

Yin et al. proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class "politics", we could construct a hypothesis of This text is about politics.. The probabilities for entailment and contradiction are then converted to label probabilities.

This method is surprisingly effective in many cases, particularly when used with larger pre-trained models like BART and Roberta. See this blog post for a more expansive introduction to this and other zero shot methods, and see the code snippets below for examples of using this model for zero-shot classification both with Hugging Face's built-in pipeline and with native Transformers/PyTorch code.

#### With the zero-shot classification pipeline

The model can be loaded with the zero-shot-classification pipeline like so:

python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
                      model="facebook/bart-large-mnli")

You can then use this pipeline to classify sequences into any of the class names you specify.

python
sequence_to_classify = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(sequence_to_classify, candidate_labels)
#{'labels': ['travel', 'dancing', 'cooking'],

'scores': [0.9938651323318481, 0.0032737774308770895, 0.002861034357920289],


'sequence': 'one day I will see the world'}

If more than one candidate label can be correct, pass multi_label=True to calculate each class independently:

python
candidate_labels = ['travel', 'cooking', 'dancing', 'exploration']
classifier(sequence_to_classify, candidate_labels, multi_label=True)
#{'labels': ['travel', 'exploration', 'dancing', 'cooking'],

'scores': [0.9945111274719238,


 0.9383890628814697,


 0.0057061901316046715,


 0.0018193122232332826],


'sequence': 'one day I will see the world'}

#### With manual PyTorch

python
# pose sequence as a NLI premise and label as a hypothesis
from transformers import AutoModelForSequenceClassification, AutoTokenizer
nli_model = AutoModelForSequenceClassification.from_pretrained('facebook/bart-large-mnli')
tokenizer = AutoTokenizer.from_pretrained('facebook/bart-large-mnli')

premise = sequence
hypothesis = f'This example is {label}.'


run through model pre-trained on MNLI

x = tokenizer.encode(premise, hypothesis, return_tensors='pt',
                     truncation_strategy='only_first')
logits = nli_model(x.to(device))[0]

we throw away "neutral" (dim 1) and take the probability of


"entailment" (2) as the probability of the label being true 

entail_contradiction_logits = logits[:,[0,2]]
probs = entail_contradiction_logits.softmax(dim=1)
prob_label_is_true = probs[:,1]
View official source
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