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library_name: transformers
tags: []
---

# Model Card for Model ID

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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

### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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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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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

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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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#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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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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