distilbert-base-uncased__sst2__train-32-1 Open-source Model

Distilbert Base Uncased Sst2 Train 32 1

Developed by SetFit

This model is a fine-tuned version of distilbert-base-uncased on the SST2 dataset, primarily used for sentiment analysis tasks.

Text Classification

Transformers

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

Downloads 16

Release Time : 3/2/2022

Model Overview

This is a lightweight model based on the DistilBERT architecture, fine-tuned for sentiment analysis tasks, particularly suitable for classifying emotions in English text.

Model Features

Lightweight Architecture

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

Sentiment Analysis

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

Efficient Training

Uses mixed-precision training techniques to improve training efficiency.

Model Capabilities

Text Classification

Sentiment Analysis

Use Cases

Social Media Analysis

User Comment Sentiment Analysis

Analyze the sentiment tendencies of user comments on social media

Accuracy 65.51%

Product Feedback Analysis

Product Review Classification

Classify product reviews as positive/negative

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7106	1.0	13	0.6850	0.6154
0.631	2.0	26	0.6632	0.6923
0.5643	3.0	39	0.6247	0.7692
0.3992	4.0	52	0.5948	0.7692
0.1928	5.0	65	0.5803	0.7692
0.0821	6.0	78	0.6404	0.6923
0.0294	7.0	91	0.7387	0.6923
0.0141	8.0	104	0.8270	0.6923
0.0082	9.0	117	0.8496	0.6923
0.0064	10.0	130	0.8679	0.6923
0.005	11.0	143	0.8914	0.6923
0.0036	12.0	156	0.9278	0.6923
0.0031	13.0	169	0.9552	0.6923
0.0029	14.0	182	0.9745	0.6923
0.0028	15.0	195	0.9785	0.6923

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Distilbert Base Uncased Sst2 Train 32 1

Model Overview

Model Features

Model Capabilities

Use Cases