kt_punc Open-source Chinese Sentiment Analysis Model - Free Deployment for Precise Analysis of Chinese Sentiment Tendencies

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Kt Punc

Developed by kktoto

A Chinese sentiment analysis model fine-tuned based on bert-base-chinese, performing well on Chinese sentiment corpora.

Text Classification

Transformers

#Chinese Sentiment Analysis #BERT Fine-tuning #High Accuracy

Downloads 23

Release Time : 5/15/2022

Model Overview

This model is a fine-tuned version of bert-base-chinese on Chinese sentiment corpus datasets, primarily used for Chinese text sentiment analysis tasks.

Model Features

High Accuracy

Achieves 95.73% accuracy on Chinese sentiment corpora.

Balanced Performance

Balanced precision (70.79%) and recall (73.14%) with an F1 score of 71.94%.

BERT-based

Fine-tuned from bert-base-chinese, inheriting BERT's powerful semantic understanding capabilities.

Model Capabilities

Chinese text sentiment analysis

Sentiment polarity classification

Text label classification

Use Cases

Sentiment Analysis

Product Review Analysis

Analyze sentiment tendencies in user reviews on e-commerce platforms.

Accurately identifies positive/negative reviews.

Social Media Monitoring

Monitor public sentiment changes on social media.

Provides real-time sentiment trend analysis.

🚀 kt_punc

This model is a fine - tuned version of [bert - base - chinese](https://huggingface.co/bert - base - chinese) on the chn_senti_corp dataset, which can achieve good performance in token classification tasks.

🚀 Quick Start

This model is a fine - tuned version of [bert - base - chinese](https://huggingface.co/bert - base - chinese) on the chn_senti_corp dataset. It achieves the following results on the evaluation set:

Loss: 0.1703
Precision: 0.7079
Recall: 0.7314
F1: 0.7194
Accuracy: 0.9573

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e - 05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1661	1.0	600	0.1351	0.6566	0.6833	0.6697	0.9498
0.1246	2.0	1200	0.1330	0.6854	0.6665	0.6758	0.9521
0.1121	3.0	1800	0.1303	0.6885	0.6994	0.6939	0.9537
0.1008	4.0	2400	0.1359	0.6836	0.7248	0.7036	0.9543
0.0809	5.0	3000	0.1404	0.7035	0.7082	0.7059	0.9559
0.0696	6.0	3600	0.1449	0.6986	0.7224	0.7103	0.9560
0.0628	7.0	4200	0.1563	0.7063	0.7214	0.7138	0.9567
0.0561	8.0	4800	0.1618	0.7024	0.7333	0.7175	0.9568
0.0525	9.0	5400	0.1669	0.7083	0.7335	0.7207	0.9574
0.0453	10.0	6000	0.1703	0.7079	0.7314	0.7194	0.9573

Framework versions

Transformers 4.19.1
Pytorch 1.11.0+cu113
Datasets 2.2.1
Tokenizers 0.12.1

📄 Information Table

Property	Details
Tags	generated_from_trainer
Datasets	chn_senti_corp
Metrics	precision, recall, f1, accuracy
Model Name	kt_punc
Task	Token Classification
Dataset Name	chn_senti_corp
Precision	0.7078651685393258
Recall	0.7313662547821116
F1	0.7194238380517767
Accuracy	0.957316742326961

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