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--- |
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title: Openalex Topic Classification |
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emoji: 🏢 |
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colorFrom: pink |
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colorTo: red |
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sdk: gradio |
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sdk_version: 5.23.1 |
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app_file: app.py |
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pinned: false |
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license: mit |
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short_description: OpenAlex/bert-base-multilingual-cased-finetuned-openalex-top |
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--- |
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# OpenAlex Topic Classification |
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This application allows you to classify academic texts into different topics using machine learning models trained with OpenAlex data. |
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## Features |
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- Classification of academic texts into multiple topics |
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- Uses two different models for more robust classification |
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- Easy-to-use web interface |
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- Support for structured title and abstract format |
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## Requirements |
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- Python 3.7+ |
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- Gradio 5.23.1 |
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- Transformers (Hugging Face) |
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## Installation |
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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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1. Run the application: |
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```bash |
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python app.py |
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``` |
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2. Open your browser at the address shown in the console (usually http://localhost:7860) |
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3. Enter your text in the format: |
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``` |
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<TITLE> Your title here |
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<ABSTRACT> Your abstract here |
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``` |
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4. Select the number of classifications you want to see (top_k) |
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5. Click "Submit" to get the results |
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## Models |
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The application uses two different models: |
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1. [OpenAlex/bert-base-multilingual-cased-finetuned-openalex-topic-classification-title-abstract](https://huggingface.co/OpenAlex/bert-base-multilingual-cased-finetuned-openalex-topic-classification-title-abstract) |
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- Based on BERT multilingual model |
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- Fine-tuned on OpenAlex data |
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- Supports multiple languages |
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2. [albertmartinez/openalex-topic-classification-title-abstract](https://huggingface.co/albertmartinez/openalex-topic-classification-title-abstract) |
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- Based on BERT multilingual model |
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- Fine-tuned on OpenAlex data (https://huggingface.co/datasets/albertmartinez/openalex-topic-title-abstract) |
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- Supports multiple languages |
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## License |
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MIT |
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## References |
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- [OpenAlex](https://openalex.org/) |
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- [Hugging Face Spaces](https://huggingface.co/docs/hub/spaces-config-reference) |
