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Distilbert Base Uncased Subj Train 8 0
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Distilbert Base Uncased Subj Train 8 0

Developed by SetFit
A text classification model fine-tuned based on distilbert-base-uncased, achieving 78.9% accuracy on the evaluation set
Large Language Model
Transformers
Open Source License:Apache-2.0#Text Classification#Lightweight BERT#High Accuracy
Downloads 26
Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of the DistilBERT base model, primarily used for text classification tasks, with no specific application scenario explicitly stated

Model Features

Efficient and Lightweight
Based on the DistilBERT architecture, it is more lightweight and efficient than the full BERT model
Fast Training
Uses mixed-precision training (native AMP) to accelerate the training process
Linear Learning Rate Scheduling
Adopts a linear learning rate scheduling strategy to optimize the training process

Model Capabilities

Text Classification
Natural Language Understanding

Use Cases

Text Analysis
Sentiment Analysis
Can be used to analyze the sentiment tendency of text
Topic Classification
Can be used to classify text into predefined topic categories
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