distilbert-base-uncased__sst2__train-8-8 Open Source Model - Free Deployment to Boost Sentiment Analysis Tasks

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Distilbert Base Uncased Sst2 Train 8 8

Developed by SetFit

This model is a fine-tuned version of distilbert-base-uncased on the SST-2 dataset for sentiment analysis tasks.

Text Classification

Transformers

Open Source License:Apache-2.0 #Sentiment Analysis #Text Classification #Lightweight BERT

Downloads 17

Release Time : 3/2/2022

Model Overview

This is a fine-tuned DistilBERT model specifically designed for sentiment analysis tasks, trained on the SST-2 (Stanford Sentiment Treebank) dataset.

Model Features

Lightweight Model

Based on the DistilBERT architecture, it is smaller and faster than the standard BERT model while maintaining good performance.

Sentiment Analysis

Fine-tuned specifically for sentiment analysis tasks, capable of identifying emotional tendencies in text.

Model Capabilities

Text Classification

Sentiment Analysis

Use Cases

Social Media Analysis

Comment Sentiment Analysis

Analyze the sentiment tendencies of social media comments to gauge user attitudes toward products or services.

Market Research

Product Feedback Analysis

Analyze customer feedback texts to understand overall satisfaction levels.

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7061	1.0	3	0.6899	0.75
0.6627	2.0	6	0.7026	0.25
0.644	3.0	9	0.7158	0.25
0.6087	4.0	12	0.7325	0.25
0.5602	5.0	15	0.7555	0.25
0.5034	6.0	18	0.7725	0.25
0.4672	7.0	21	0.7983	0.25
0.403	8.0	24	0.8314	0.25
0.3571	9.0	27	0.8555	0.25
0.2792	10.0	30	0.9065	0.25
0.2373	11.0	33	0.9286	0.25

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Distilbert Base Uncased Sst2 Train 8 8

Model Overview

Model Features

Model Capabilities

Use Cases