distilbert-base-cased-finetuned-chunk Open Source Model - Free to Use for Text Classification Tasks

Distilbert Base Cased Finetuned Chunk

Developed by RobW

This model is a fine-tuned version of distilbert-base-cased on an unknown dataset, primarily used for text classification tasks.

Downloads 15

Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of distilbert-base-cased, suitable for text classification tasks with high precision, recall, and F1 scores.

Efficient Fine-tuning

Based on the DistilBERT architecture, the model reduces computational resource requirements while maintaining high performance.

High Performance

Performs excellently on the evaluation set, achieving an F1 score of 0.8845 and an accuracy of 0.8239.

Lightweight

DistilBERT is a lightweight version of BERT, suitable for resource-constrained environments.

Text Classification

Natural Language Processing

Text Classification

Sentiment Analysis

Can be used to analyze the sentiment tendency of text, such as positive or negative reviews.

Topic Classification

Can be used to classify text into predefined topic categories.

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.8391	1.0	878	0.5871	0.8453	0.9035	0.8734	0.8054
0.6134	2.0	1756	0.5447	0.8555	0.8983	0.8764	0.8142
0.5565	3.0	2634	0.5180	0.8615	0.9088	0.8845	0.8239

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