distilbert-base-uncased-finetuned-ingredients Open-source Model

Distilbert Base Uncased Finetuned Ingredients

Developed by harr

This model is a fine-tuned version of distilbert-base-uncased on the ingredients_yes_no dataset, designed for token classification tasks.

Sequence Labeling

Transformers

Open Source License:Apache-2.0 #Ingredient Recognition #High-Precision Classification #Food Label Processing

Downloads 20

Release Time : 3/2/2022

Model Overview

The model is primarily used for token classification of food ingredients to determine whether they are ingredients. It performs exceptionally well on the evaluation set, with precision, recall, and F1 scores all close to 99%.

Model Features

High-Precision Classification

Achieved nearly 99% precision, recall, and F1 score on the evaluation set.

Lightweight Model

Based on the DistilBERT architecture, it is more lightweight and efficient compared to the original BERT model.

Domain-Specific Optimization

Specially fine-tuned for the task of ingredient recognition.

Model Capabilities

Ingredient Recognition

Text Token Classification

Use Cases

Food Industry

Ingredient Analysis

Automatically identifies ingredient information on food labels

Accuracy as high as 99.78%

Allergen Detection

Helps identify potential allergen ingredients in food

Property	Details
Model Type	Fine - tuned version of distilbert - base - uncased on ingredients_yes_no dataset
Training Data	ingredients_yes_no
Metrics	Precision, Recall, F1, Accuracy

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	47	0.2783	0.4	0.5492	0.4629	0.8910
No log	2.0	94	0.1089	0.8145	0.8780	0.8450	0.9718
No log	3.0	141	0.0273	0.9865	0.9932	0.9899	0.9973
No log	4.0	188	0.0168	0.9865	0.9932	0.9899	0.9973
No log	5.0	235	0.0156	0.9865	0.9898	0.9882	0.9957
No log	6.0	282	0.0129	0.9865	0.9932	0.9899	0.9973
No log	7.0	329	0.0121	0.9899	0.9932	0.9915	0.9978
No log	8.0	376	0.0115	0.9899	0.9932	0.9915	0.9978
No log	9.0	423	0.0108	0.9899	0.9932	0.9915	0.9978
No log	10.0	470	0.0105	0.9899	0.9932	0.9915	0.9978

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Distilbert Base Uncased Finetuned Ingredients

Model Overview

Model Features

Model Capabilities

Use Cases

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Model Information

Training Procedure

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