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--- |
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title: Paper Classifier |
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emoji: 📚 |
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colorFrom: blue |
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colorTo: indigo |
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sdk: streamlit |
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sdk_version: "1.32.0" |
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app_file: app.py |
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pinned: false |
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--- |
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# 📚 Academic Paper Classifier |
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[link](https://huggingface.co/spaces/ssbars/ysdaml4) |
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This Streamlit application helps classify academic papers into different categories using a BERT-based model. |
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## Features |
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- **Text Classification**: Paste any paper text directly |
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- **PDF Support**: Upload PDF files for classification |
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- **Real-time Analysis**: Get instant classification results |
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- **Probability Distribution**: See confidence scores for each category |
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- **Multiple Categories**: Supports various academic fields |
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## How to Use |
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1. **Text Input** |
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- Paste your paper's text (abstract or full content) |
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- Click "Classify Text" |
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- View results and probability distribution |
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2. **PDF Upload** |
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- Upload a PDF file of your paper |
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- Click "Classify PDF" |
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- Get classification results |
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## Categories |
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The model classifies papers into the following categories: |
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- Computer Science |
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- Mathematics |
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- Physics |
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- Biology |
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- Economics |
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## Technical Details |
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- Built with Streamlit |
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- Uses BERT-based model for classification |
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- Supports PDF file processing |
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- Real-time classification |
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## About |
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This application is designed to help researchers, students, and academics quickly identify the primary field of academic papers. It uses state-of-the-art natural language processing to analyze paper content and provide accurate classifications. |
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--- |
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Created with ❤️ using Streamlit and Transformers |
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## Setup |
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1. Install `uv` (if not already installed): |
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```bash |
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# Using pip |
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pip install uv |
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# Or using Homebrew on macOS |
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brew install uv |
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``` |
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2. Create and activate a virtual environment: |
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```bash |
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uv venv |
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source .venv/bin/activate # On Unix/macOS |
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# OR |
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.venv\Scripts\activate # On Windows |
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``` |
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3. Install the dependencies using uv: |
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```bash |
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uv pip install -r requirements.lock |
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``` |
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4. Run the Streamlit application: |
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```bash |
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streamlit run app.py |
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``` |
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## Usage |
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1. **Text Classification** |
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- Paste the paper's text (abstract or content) into the text area |
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- Click "Classify Text" to get results |
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2. **PDF Classification** |
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- Upload a PDF file using the file uploader |
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- Click "Classify PDF" to process and classify the document |
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## Model Information |
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The service uses a BERT-based model for classification with the following categories: |
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- Computer Science |
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- Mathematics |
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- Physics |
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- Biology |
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- Economics |
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## Note |
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The current implementation uses a base BERT model. For production use, you should: |
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1. Fine-tune the model on a dataset of academic papers |
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2. Adjust the categories based on your specific needs |
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3. Implement proper error handling and validation |
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4. Add authentication if needed |
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## Package Management |
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This project uses `uv` as the package manager for faster and more reliable dependency management. The dependencies are locked in `requirements.lock` for reproducible installations. |
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To update dependencies: |
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```bash |
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# Update a single package |
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uv pip install --upgrade package_name |
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# Update all packages and regenerate lock file |
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uv pip compile requirements.txt -o requirements.lock |
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uv pip install -r requirements.lock |
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``` |
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## Requirements |
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See `requirements.txt` for a complete list of dependencies. |
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# ArXiv Paper Classifier |
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This project implements a machine learning system for classifying academic papers into ArXiv categories using state-of-the-art transformer models. |
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## Project Overview |
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The system uses pre-trained transformer models to classify academic papers into one of the main ArXiv categories: |
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- Computer Science (cs) |
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- Mathematics (math) |
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- Physics (physics) |
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- Quantitative Biology (q-bio) |
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- Quantitative Finance (q-fin) |
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- Statistics (stat) |
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- Electrical Engineering and Systems Science (eess) |
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- Economics (econ) |
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## Features |
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- Multiple model support: |
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- DistilBERT: Lightweight and fast model, good for testing |
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- DeBERTa-v3: Advanced model with better performance |
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- RoBERTa: Advanced model with strong performance |
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- SciBERT: Specialized for scientific text |
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- BERT: Classic model with good all-round performance |
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- Flexible input handling: |
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- Can process both title and abstract |
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- Handles text preprocessing and tokenization |
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- Supports different maximum sequence lengths |
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- Robust error handling: |
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- Multiple fallback mechanisms for tokenizer initialization |
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- Graceful degradation to simpler models if needed |
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- Detailed error messages and logging |
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## Installation |
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1. Clone the repository |
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2. Install dependencies: |
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```bash |
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pip install -r requirements.txt |
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``` |
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## Usage |
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### Basic Usage |
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```python |
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from model import PaperClassifier |
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# Initialize classifier with default model (DistilBERT) |
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classifier = PaperClassifier() |
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# Classify a paper |
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result = classifier.classify_paper( |
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title="Your paper title", |
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abstract="Your paper abstract" |
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) |
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# Print results |
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print(result) |
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``` |
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### Using Different Models |
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```python |
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# Initialize with DeBERTa-v3 |
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classifier = PaperClassifier(model_type='deberta-v3') |
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# Initialize with RoBERTa |
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classifier = PaperClassifier(model_type='roberta') |
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# Initialize with SciBERT |
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classifier = PaperClassifier(model_type='scibert') |
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# Initialize with BERT |
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classifier = PaperClassifier(model_type='bert') |
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``` |
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### Training on Custom Data |
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```python |
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# Prepare your training data |
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train_texts = ["paper1 title and abstract", "paper2 title and abstract", ...] |
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train_labels = ["cs", "math", ...] |
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# Train the model |
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classifier.train_on_arxiv( |
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train_texts=train_texts, |
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train_labels=train_labels, |
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epochs=3, |
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batch_size=16, |
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learning_rate=2e-5 |
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) |
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``` |
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## Model Details |
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### Available Models |
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1. **DistilBERT** (`distilbert`) |
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- Model: `distilbert-base-cased` |
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- Max length: 512 tokens |
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- Fast tokenizer |
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- Good for testing and quick results |
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2. **DeBERTa-v3** (`deberta-v3`) |
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- Model: `microsoft/deberta-v3-base` |
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- Max length: 512 tokens |
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- Uses DebertaV2TokenizerFast |
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- Advanced performance |
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3. **RoBERTa** (`roberta`) |
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- Model: `roberta-base` |
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- Max length: 512 tokens |
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- Strong performance on various tasks |
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4. **SciBERT** (`scibert`) |
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- Model: `allenai/scibert_scivocab_uncased` |
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- Max length: 512 tokens |
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- Specialized for scientific text |
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5. **BERT** (`bert`) |
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- Model: `bert-base-uncased` |
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- Max length: 512 tokens |
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- Classic model with good all-round performance |
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## Error Handling |
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The system includes robust error handling mechanisms: |
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- Multiple fallback levels for tokenizer initialization |
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- Graceful degradation to simpler models |
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- Detailed error messages and logging |
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- Automatic fallback to BERT tokenizer if needed |
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## Requirements |
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- Python 3.7+ |
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- PyTorch |
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- Transformers library |
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- NumPy |
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- Sacremoses (for tokenization support) |
