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Distilbert Base Uncased Mnli
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Distilbert Base Uncased Mnli

Developed by Xenova
A lightweight version of DistilBERT specifically designed for Multi-Genre Natural Language Inference (MNLI) tasks, based on the BERT architecture but with fewer parameters and higher efficiency.
Large Language Model
Transformers
#Zero-shot classification#Web-based inference#Lightweight BERT
Downloads 1,215
Release Time : 5/2/2023

Model Overview

This model is a lightweight natural language processing model based on the DistilBERT architecture, optimized for Multi-Genre Natural Language Inference (MNLI) tasks and suitable for zero-shot classification scenarios.

Model Features

Lightweight and efficient
Reduces parameters by 40% compared to the original BERT model while retaining 97% of its performance.
Zero-shot classification
Can be directly applied to text classification tasks without fine-tuning.
ONNX compatible
Provides ONNX format weights for optimized web-based deployment.

Model Capabilities

Text classification
Natural language inference
Zero-shot learning

Use Cases

Text analysis
Sentiment analysis
Classifies text as positive/negative sentiment without training
Topic classification
Automatically categorizes documents based on custom labels
Customer support
Ticket classification
Automatically categorizes customer inquiries into predefined categories
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