news_classifier_model

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Model Details

Model Description

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news_classifier_model is a text classification model built using the pre-trained distilbert-base-uncased architecture. It is designed to classify news excerpts into five categories: Business, Opinion, Political Gossip, Sports, and World News.

The model is based on DistilBERT, a lightweight and faster version of BERT that retains strong language understanding capabilities while using fewer computational resources. It was fine-tuned on a dataset of Sri Lankan news articles, allowing it to learn patterns and context specific to news content.

By leveraging contextual understanding of text, the model can accurately analyze a given news excerpt and assign it to the most relevant category.

Intended Uses & Limitations

Intended Uses

News article classification Content organization and tagging Integration into web applications for automated categorization

Limitations

May not generalize well to news outside the training domain Performance can drop on very short or unclear text Not suitable for text generation tasks

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Uses

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Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

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Factors

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Results

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Summary

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Environmental Impact

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Technical Specifications [optional]

Model Architecture and Objective

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Citation [optional]

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Glossary [optional]

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