Overview

This model is a specialized DeBERTa v3 large variant fine-tuned on a comprehensive suite of Natural Language Inference (NLI) datasets, including MNLI, FEVER, ANLI, and others. Unlike general-purpose LLMs, it is optimized specifically for zero-shot text classification. By leveraging the premise-hypothesis framework of NLI, developers can categorize text into arbitrary labels without needing task-specific training data. It is an ideal choice for production pipelines requiring high-precision sentiment analysis, topic labeling, or intent recognition where labels are dynamic or predefined. Integration is straightforward via the Hugging Face Transformers library, offering a computationally efficient alternative to generative models for discriminative classification tasks.

Highlights

High-accuracy zero-shot classification via NLI fine-tuning
Optimized DeBERTa v3 architecture for superior contextual embeddings
Eliminates need for labeled data for new categories
Efficient deployment compared to large generative models
Seamless integration with Hugging Face Transformers pipeline

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/DeBERTa-v3-large-mnli-fever-anli-ling-wanli")
tokenizer = AutoTokenizer.from_pretrained("MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli")

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/DeBERTa-v3-large-mnli-fever-anli-ling-wanli

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

huggingface-cli download MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli 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/DeBERTa-v3-large-mnli-fever-anli-ling-wanli')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli

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/DeBERTa-v3-large-mnli-fever-anli-ling-wanli')
tokenizer = AutoTokenizer.from_pretrained('MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli')

Full Documentation

来源: HuggingFace

---
language:

en

license: mit
tags:

text-classification

zero-shot-classification

datasets:

multi_nli

facebook/anli

fever

lingnli

alisawuffles/WANLI

metrics:

accuracy

pipeline_tag: zero-shot-classification
model-index:

name: DeBERTa-v3-large-mnli-fever-anli-ling-wanli

results:
- task:
type: text-classification
name: Natural Language Inference
dataset:
name: MultiNLI-matched
type: multi_nli
split: validation_matched
metrics:
- type: accuracy
value: 0,912
verified: false
- task:
type: text-classification
name: Natural Language Inference
dataset:
name: MultiNLI-mismatched
type: multi_nli
split: validation_mismatched
metrics:
- type: accuracy
value: 0,908
verified: false
- task:
type: text-classification
name: Natural Language Inference
dataset:
name: ANLI-all
type: anli
split: test_r1+test_r2+test_r3
metrics:
- type: accuracy
value: 0,702
verified: false
- task:
type: text-classification
name: Natural Language Inference
dataset:
name: ANLI-r3
type: anli
split: test_r3
metrics:
- type: accuracy
value: 0,64
verified: false
- task:
type: text-classification
name: Natural Language Inference
dataset:
name: WANLI
type: alisawuffles/WANLI
split: test
metrics:
- type: accuracy
value: 0,77
verified: false
- task:
type: text-classification
name: Natural Language Inference
dataset:
name: LingNLI
type: lingnli
split: test
metrics:
- type: accuracy
value: 0,87
verified: false
---

DeBERTa-v3-large-mnli-fever-anli-ling-wanli

Model description

This model was fine-tuned on the MultiNLI, Fever-NLI, Adversarial-NLI (ANLI), LingNLI and WANLI datasets, which comprise 885 242 NLI hypothesis-premise pairs. This model is the best performing NLI model on the Hugging Face Hub as of 06.06.22 and can be used for zero-shot classification. It significantly outperforms all other large models on the ANLI benchmark.

The foundation model is DeBERTa-v3-large from Microsoft. DeBERTa-v3 combines several recent innovations compared to classical Masked Language Models like BERT, RoBERTa etc., see the paper

How to use the model

#### Simple zero-shot classification pipeline

python

from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli")
sequence_to_classify = "Angela Merkel is a politician in Germany and leader of the CDU"
candidate_labels = ["politics", "economy", "entertainment", "environment"]
output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
print(output)

#### NLI use-case

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model_name = "MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing."
hypothesis = "The movie was not good."
input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
output = model(input["input_ids"].to(device))  # device = "cuda:0" or "cpu"
prediction = torch.softmax(output["logits"][0], -1).tolist()
label_names = ["entailment", "neutral", "contradiction"]
prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
print(prediction)

Training data

DeBERTa-v3-large-mnli-fever-anli-ling-wanli was trained on the MultiNLI, Fever-NLI, Adversarial-NLI (ANLI), LingNLI and WANLI datasets, which comprise 885 242 NLI hypothesis-premise pairs. Note that SNLI was explicitly excluded due to quality issues with the dataset. More data does not necessarily make for better NLI models.

Training procedure

DeBERTa-v3-large-mnli-fever-anli-ling-wanli was trained using the Hugging Face trainer with the following hyperparameters. Note that longer training with more epochs hurt performance in my tests (overfitting).

code

training_args = TrainingArguments(
    num_train_epochs=4,              # total number of training epochs
    learning_rate=5e-06,
    per_device_train_batch_size=16,   # batch size per device during training
    gradient_accumulation_steps=2,    # doubles the effective batch_size to 32, while decreasing memory requirements
    per_device_eval_batch_size=64,    # batch size for evaluation
    warmup_ratio=0.06,                # number of warmup steps for learning rate scheduler
    weight_decay=0.01,               # strength of weight decay
    fp16=True                        # mixed precision training
)

Eval results

The model was evaluated using the test sets for MultiNLI, ANLI, LingNLI, WANLI and the dev set for Fever-NLI. The metric used is accuracy. The model achieves state-of-the-art performance on each dataset. Surprisingly, it outperforms the previous state-of-the-art on ANLI (ALBERT-XXL) by 8,3%. I assume that this is because ANLI was created to fool masked language models like RoBERTa (or ALBERT), while DeBERTa-v3 uses a better pre-training objective (RTD), disentangled attention and I fine-tuned it on higher quality NLI data.

|Datasets|mnli_test_m|mnli_test_mm|anli_test|anli_test_r3|ling_test|wanli_test|
| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
|Accuracy|0.912|0.908|0.702|0.64|0.87|0.77|
|Speed (text/sec, A100 GPU)|696.0|697.0|488.0|425.0|828.0|980.0|

Limitations and bias

Please consult the original DeBERTa-v3 paper and literature on different NLI datasets for more information on the training data and potential biases. The model will reproduce statistical patterns in the training data.

Citation

If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. ‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’. Preprint, June. Open Science Framework. https://osf.io/74b8k.

Ideas for cooperation or questions?

If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or LinkedIn

Debugging and issues

Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues.

DeBERTa v3 large mnli fever anli ling wanli

Overview

Highlights

Usage

Hugging Face Download

CLI Download

SDK Download

Git Download

PyTorch / Transformers Usage

Full Documentation

DeBERTa-v3-large-mnli-fever-anli-ling-wanli

Model description

How to use the model

Training data

Training procedure

Eval results

Limitations and bias

Citation

Ideas for cooperation or questions?

Debugging and issues