DistilBERT Open-Source Text Classification Model - Easily Classify Texts with Free Deployment Based on BERT Fine-Tuning

Distilbert Token Itr0 0.0001 Editorials 01 03 2022 15 20 12

Developed by ali2066

DistilBERT model fine-tuned based on bert-base-uncased, primarily used for text classification tasks

Downloads 22

Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version based on bert-base-uncased on an unspecified dataset, suitable for text classification tasks with high accuracy.

Efficient Fine-tuning

Fine-tuned based on bert-base-uncased, retaining the high performance of the BERT model while reducing computational resource requirements.

High Accuracy

Achieved an accuracy of 0.9707 on the evaluation set, demonstrating excellent performance.

Lightweight

As a DistilBERT model, it is more lightweight compared to the original BERT model, suitable for resource-constrained environments.

Text Classification

Natural Language Processing

Text Analysis

Editorial Classification

Can be used to classify editorial articles, identifying different topics or sentiment tendencies.

Accuracy 0.9707

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	15	0.0733	0.04	0.0055	0.0097	0.9861
No log	2.0	30	0.0732	0.04	0.0055	0.0097	0.9861
No log	3.0	45	0.0731	0.04	0.0055	0.0097	0.9861
No log	4.0	60	0.0716	0.04	0.0055	0.0097	0.9861
No log	5.0	75	0.0635	0.04	0.0055	0.0097	0.9861

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