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Setfit All MiniLM L6 V2 Sst2 32 Shot

Developed by tomaarsen
This is a SetFit model trained on the sst2 dataset for English text classification tasks, utilizing efficient few-shot learning techniques.
Downloads 23
Release Time : 11/30/2023

Model Overview

The model uses sentence-transformers/all-MiniLM-L6-v2 as the sentence transformer embedding model and employs logistic regression for classification, suitable for text classification tasks such as sentiment analysis.

Model Features

Efficient few-shot learning
Uses contrastive learning to fine-tune the sentence transformer, achieving good performance with only a few samples.
Lightweight model
Based on the small MiniLM-L6-v2 transformer, requiring lower computational resources.
Fast inference
Combines a lightweight sentence transformer with a logistic regression classifier for quick inference.

Model Capabilities

English text classification
Sentiment analysis
Few-shot learning

Use Cases

Sentiment analysis
Movie review sentiment classification
Analyzes the sentiment tendency (positive/negative) of movie reviews.
Achieved 75.13% accuracy on the sst2 test set.
Product review analysis
Performs sentiment classification on product reviews from e-commerce platforms.
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