BERT-Practice-Classifier Open-Source Text Classification Model

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Bert Practice Classifier

Developed by RayenLLM

A text classification model fine-tuned based on distilbert-base-uncased, trained for specific tasks.

Text Classification

Transformers

Open Source License:Apache-2.0 #Text Classification #Lightweight BERT #Low-resource Training

Downloads 181

Release Time : 3/12/2025

Model Overview

This model is a fine-tuned version based on distilbert-base-uncased, primarily used for text classification tasks. Specific applications and limitations require further details.

Model Features

Lightweight Model

Based on the DistilBERT architecture, it is more lightweight than the original BERT model, offering faster inference speed.

Fine-tuning Capability

Can be fine-tuned for specific text classification tasks to adapt to different domain requirements.

Model Capabilities

Text Classification

Natural Language Processing

Use Cases

Text Analysis

Sentiment Analysis

Can be used to analyze the sentiment tendency of text (positive/negative)

Topic Classification

Can classify text into predefined topic categories

Training Loss	Epoch	Step	Validation Loss	Accuracy	Auc	Precision
0.6963	1.0	4	0.7382	0.375	0.133	0.375
0.6877	2.0	8	0.7270	0.375	0.133	0.375
0.6984	3.0	12	0.7126	0.25	0.067	0.2
0.6871	4.0	16	0.7091	0.375	0.133	0.0
0.6912	5.0	20	0.7012	0.5	0.133	0.0
0.6867	6.0	24	0.7062	0.5	0.133	0.0
0.6862	7.0	28	0.7095	0.375	0.133	0.0
0.6639	8.0	32	0.7177	0.25	0.133	0.0
0.67	9.0	36	0.7239	0.125	0.133	0.0
0.6597	10.0	40	0.7264	0.375	0.133	0.333

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Bert Practice Classifier

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 bert-practice-classifier

🚀 Quick Start

📚 Documentation

Training procedure

Training hyperparameters

Training results

Framework versions

📄 License