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Squeezebert Mnli

Developed by typeform
SqueezeBERT is a lightweight version of BERT, optimized to reduce computational resource requirements while maintaining high performance in natural language understanding.
Large Language Model
Transformers
 English#Lightweight BERT#Natural Language Inference#Zero-shot Classification
Downloads 37
Release Time : 3/2/2022

Model Overview

SqueezeBERT is an efficient variant of BERT, designed for resource-constrained environments and suitable for natural language inference tasks.

Model Features

Lightweight and Efficient
Reduces computational resource requirements through optimized architecture, suitable for resource-constrained environments.
High Performance
Maintains high accuracy in natural language inference tasks.
Multi-type Natural Language Inference
Supports multi-type natural language inference tasks, applicable to various scenarios.

Model Capabilities

Natural Language Inference
Zero-shot Classification

Use Cases

Natural Language Processing
Text Classification
Performs zero-shot classification on text without requiring additional training data.
Performs well on the multi_nli dataset.
Natural Language Inference
Determines the logical relationship between two pieces of text (e.g., entailment, contradiction, or neutrality).
Achieves high accuracy on the multi_nli dataset.
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