mgulati3
/

news-classifier-model

@@ -1,199 +1,139 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags: [text-classification, llm, huggingface, nlp, news, fine-tuning, gradio]
 ---
+# 📰 NewsSense AI: LLM News Classifier with Web Scraping & Fine-Tuning
+A fine-tuned transformer-based model that classifies news articles into five functional categories: Politics, Business, Health, Science, and Climate. The dataset was scraped from NPR using Decodo and processed with BeautifulSoup.
+---
 ## Model Details
 ### Model Description
+This model is fine-tuned using Hugging Face Transformers on a custom dataset of 5,000 news articles scraped directly from [NPR](https://www.npr.org/). The goal is to classify real-world news into practical categories for use in filtering, organizing, and summarizing large-scale news streams.
+- **Developed by:** Manan Gulati
+- **Model type:** Transformer (text classification)
+- **Language(s):** English
+- **License:** MIT
+- **Fine-tuned from model:** distilbert-base-uncased
+### Model Sources
+- **Repository:** https://github.com/mgulati3/Fine-Tune
+- **Demo:** https://huggingface.co/spaces/mgulati3/news-classifier-ui
+- **Model Hub:** https://huggingface.co/mgulati3/news-classifier-model
+---
 ## Uses
 ### Direct Use
+This model can be used to classify any English-language news article or paragraph into one of five categories. It's useful for content filtering, feed curation, and auto-tagging of articles.
 ### Out-of-Scope Use
+- Not suitable for multi-label classification.
+- Not recommended for non-news or informal text.
+- May not perform well on non-English content.
+---
 ## Bias, Risks, and Limitations
+- The model is trained only on NPR articles, which may carry source-specific bias.
+- Categories are limited to five; nuanced topics may not be accurately captured.
+- Misclassifications may occur for ambiguous or mixed-topic content.
 ### Recommendations
+Use prediction confidence scores to interpret results. Consider human review for sensitive applications.
+---
+## How to Get Started
+```python
+from transformers import pipeline
+classifier = pipeline("text-classification", model="mgulati3/news-classifier-model")
+classifier("NASA's new moon mission will use AI to optimize fuel consumption.")
+```
+---
 ## Training Details
 ### Training Data
+Scraped 5,000 articles from NPR using Decodo (with proxy rotation and JS rendering). Articles were cleaned and labeled across five categories using Python and pandas.
 ### Training Procedure
+- Tokenizer: LLaMA-compatible tokenizer
+- Preprocessing: Lowercasing, truncation, padding
+- Epochs: 4
+- Optimizer: AdamW
+- Batch size: 16
+---
 ## Evaluation
+### Testing Data
+20% of the dataset was reserved for testing. Random stratified split was used.
+### Metrics
+- Accuracy (Train): 85%
+- Accuracy (Test): 60%
+- Metric: Accuracy (single-label, top-1)
 ### Results
+The model performs well on domain-specific, labeled news content with distinguishable category patterns.
+---
 ## Environmental Impact
+- **Hardware Type:** Google Colab GPU (T4)
+- **Hours used:** ~2.5
+- **Cloud Provider:** Google
+- **Compute Region:** US
+- **Carbon Emitted:** Estimated ~0.2 kgCO2eq
+---
+## Technical Specifications
+### Model Architecture
+DistilBERT architecture fine-tuned for single-label text classification using a softmax output layer over 5 categories.
 ### Compute Infrastructure
+- Google Colab Pro
+- Python 3.10
+- Hugging Face Transformers 4.x
+- PyTorch backend
+---
+## Citation
 **APA:**
+Gulati, M. (2025). NewsSense AI: Fine-tuned LLM for News Classification. https://huggingface.co/mgulati3/news-classifier-model
+**BibTeX:**
+@misc{gulati2025newssense,
+  author = {Gulati, Manan},
+  title = {NewsSense AI: Fine-tuned LLM for News Classification},
+  year = {2025},
+  url = {https://huggingface.co/mgulati3/news-classifier-model}
+}
+---
 ## Model Card Contact
+For questions or collaborations: mgulati3@asu.edu