Deberta-v3-large__sst2__train-8-1 Open-source Text Classification Model - Accurately Classify Texts Based on Dataset Training

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Deberta V3 Large Sst2 Train 8 1

Developed by SetFit

A text classification model fine-tuned based on microsoft/deberta-v3-large, trained on the SST-2 dataset

Text Classification

Transformers

Open Source License:MIT #Sentiment Analysis #High-Precision Fine-Tuning #Text Classification

Downloads 17

Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of the DeBERTa-v3-large architecture, primarily used for sentiment analysis tasks, especially binary classification problems

Model Features

High-Performance Architecture

Based on the DeBERTa-v3-large architecture, with powerful text understanding capabilities

Sentiment Analysis

Specifically optimized for sentiment analysis tasks, particularly binary classification problems

Fine-Tuning Optimization

Precisely fine-tuned on the SST-2 dataset to adapt to specific domain tasks

Model Capabilities

Text Classification

Sentiment Analysis

Natural Language Understanding

Use Cases

Sentiment Analysis

Review Sentiment Classification

Classify user reviews into positive/negative sentiments

Accuracy 50.08% (based on provided evaluation data)

Social Media Monitoring

Analyze sentiment tendencies in social media posts

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6773	1.0	3	0.7822	0.25
0.6587	2.0	6	0.8033	0.25
0.693	3.0	9	0.8101	0.25
0.5979	4.0	12	1.1235	0.25
0.4095	5.0	15	1.3563	0.25
0.2836	6.0	18	1.5325	0.5
0.1627	7.0	21	1.7786	0.25
0.0956	8.0	24	2.0067	0.5
0.0535	9.0	27	2.3351	0.5
0.0315	10.0	30	2.6204	0.5
0.0182	11.0	33	2.8483	0.5

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Deberta V3 Large Sst2 Train 8 1

Model Overview

Model Features

Model Capabilities

Use Cases