Developer: Aimu0999
Base Architecture: mDeBERTa-v3-base (Microsoft)
Task: Natural Language Inference (NLI) & Zero-Shot Text Classification
Language: Multilingual (Supports 100+ languages including English, Arabic, French, etc.)

🧠 What is this model?

This is a fine-tuned version of the powerful mDeBERTa-v3 model. It is specialized in Cross-Lingual NLI.

This means it can look at a piece of text (Premise) and a statement (Hypothesis) and determine if the statement is True (Entailment), False (Contradiction), or Unrelated (Neutral).

Because it is trained on NLI, it is extremely powerful for Zero-Shot Classification, allowing you to classify text into categories you haven't trained it on.

💻 How to Use (Python)

1. Zero-Shot Classification (Easiest)

Classify text into any custom labels without training.

python

from transformers import pipeline

classifier = pipeline("zero-shot-classification", model="Aimu0999/mDeBERTa-v3-base-xnli-multilingual-nli-finetuned")

text = "The galaxy is full of stars and mystery."
labels = ["science", "politics", "cooking"]

result = classifier(text, labels)
print(result)