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Distilbert Base Uncased Finetuned Clinc
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Distilbert Base Uncased Finetuned Clinc

Developed by arianpasquali
This model is a text classification model fine-tuned on the clinc_oos dataset based on DistilBERT, primarily used for intent recognition tasks.
Text Classification
Transformers
Open Source License:Apache-2.0#Intent Recognition#High Accuracy#Dialogue Systems
Downloads 19
Release Time : 3/2/2022

Model Overview

This is a fine-tuned DistilBERT model specifically designed for text classification tasks on the clinc_oos dataset, achieving an accuracy of 91.13% on the evaluation set.

Model Features

Efficient and Lightweight
Based on the DistilBERT architecture, it is more lightweight and efficient than standard BERT models
High Accuracy
Achieves 91.13% accuracy on the clinc_oos dataset
Fast Training
Requires only 5 epochs of training to achieve good performance

Model Capabilities

Text Classification
Intent Recognition
Natural Language Understanding

Use Cases

Dialogue Systems
Customer Service Bot Intent Recognition
Used to identify user intents in customer service dialogues
Can accurately classify 150 different user intents
Text Analysis
User Query Classification
Automatically classifies user inputs
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