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Bert Base Cased Finetuned Sst2

Developed by gchhablani
A text classification model fine-tuned on the GLUE SST2 dataset based on the bert-base-cased model, with an accuracy of 92.3%.
Downloads 236
Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of the BERT base version for sentiment analysis tasks, mainly used to compare the performance differences between FNet and BERT architectures.

Model Features

High accuracy
Achieved an accuracy of 92.3% on the SST2 sentiment analysis dataset.
Comparative study
Specifically used for performance comparison studies with the FNet architecture.
Standardized training
Uses the Hugging Face standard training process and hyperparameter configuration.

Model Capabilities

Text classification
Sentiment analysis
Sentence-level prediction

Use Cases

Sentiment analysis
Movie review sentiment analysis
Analyze the sentiment tendency (positive/negative) of movie reviews.
Achieved an accuracy of 92.3% on the standard test set.
Product review classification
Perform sentiment classification on product reviews on e-commerce platforms.
Model research
Architecture comparison
Compare performance with lightweight architectures such as FNet.
Serves as a BERT baseline comparison model.
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