Nli Deberta V3 Large

🚀 Cross-Encoder for Natural Language Inference

A cross-encoder model for natural language inference, trained on SNLI and MultiNLI datasets.

🚀 Quick Start

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

✨ Features

• Trained on SNLI and MultiNLI datasets.
• Outputs three scores corresponding to the labels: contradiction, entailment, neutral for a given sentence pair.
• Can be used for natural language inference and zero-shot classification.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

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)]

Advanced Usage

Usage with Transformers AutoModel

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

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)

📚 Documentation

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 further evaluation results, see SBERT.net - Pretrained Cross-Encoder.

📄 License

This project is licensed under the apache-2.0 license.

📦 Dataset Information