| --- |
| language: en |
| tags: |
| - summarization |
| - bart |
| - nlp |
| pipeline_tag: summarization |
| model_name: Automated-Text-Summarizer |
| --- |
| |
| # Automated Text Summarizer |
|
|
| This model is a fine-tuned version of **BART-large-cnn**, specifically designed to generate high-quality, abstractive summaries of long-form text. |
|
|
| ## Model Description |
| - **Developed by:** Aditya Prasad Sahu |
| - **Model type:** Transformer-based Encoder-Decoder (BART) |
| - **Language(s):** English |
| - **Task:** Text Summarization |
|
|
| ## Project Context |
| This project was developed as part of my focus on **Natural Language Processing (NLP)** and **Deep Learning**. My experience includes a 20-day internship covering CNNs, RNNs, and Transformer models (BERT/GPT), and this summarizer is a practical application of those concepts. |
|
|
| ## Intended Use |
| This model is intended for: |
| - Summarizing news articles. |
| - Condensing research papers or long reports. |
| - Integrating into personal portfolio projects as a microservice. |
|
|
| ## Performance |
| - **Framework:** PyTorch & Transformers |
| - **Precision:** Float32 |
| - **Base Model:** facebook/bart-large-cnn |
