https://github.com/paulrinckens/han_for_doc_classification

Hierarchical Attention Networks for Document Classification
https://github.com/paulrinckens/han_for_doc_classification

document-classification hierarchical-attention-networks machine-learning natural-language-processing

Last synced: 2 months ago
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Hierarchical Attention Networks for Document Classification

• Host: GitHub
• URL: https://github.com/paulrinckens/han_for_doc_classification
• Owner: paulrinckens
• License: mit
• Created: 2020-04-19T12:28:46.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-03-25T00:56:08.000Z (almost 2 years ago)
• Last Synced: 2024-10-03T10:33:40.135Z (3 months ago)
• Topics: document-classification, hierarchical-attention-networks, machine-learning, natural-language-processing
• Language: Jupyter Notebook
• Size: 820 KB
• Stars: 4
• Watchers: 1
• Forks: 2
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Hierarchical Attention Networks for Document Classification

An implementation of Hierarchical Attention Networks for Document Classification from the following paper:

![Paper on Hierarchical Attention Networks for Document Classification](img/paper_screenshot_han_for_doc_class.png)

## Content

This repo contains the following:

- Implementation of the Network Architecture with the following technologies

    - Keras

    - Preprocessing with spaCy

    - Embedding Layer with pretrained Glove vectors

    - Tokenizing with Keras tokenizer

- Utilities to train the model on the *Ten Thousand German News Article Dataset* (https://github.com/tblock/10kGNAD)

- Jupyter Notebook used during initial development of the network

- REST API server developed with FastAPI for predicting on a trained model and display of the sentence and word attentions in html 

## Get started

- Download german word vectors pretrained by Deepset (https://deepset.ai/german-word-embeddings)

from here `https://int-emb-glove-de-wiki.s3.eu-central-1.amazonaws.com/vectors.txt` and place them in the directory ``embeddings/glove_german/``

- The dataset can be downloaded from ``https://github.com/tblock/10kGNAD/blob/master/articles.csv``. Place the articles.csv file in the directory ``data/``

## Train the model

Run the training script `train_script.py`

## Start the server

Running 

```

uvicorn app.app:app

```

starts the server under `http://127.0.0.1:8000` 

### Access the API docs

After running the server the API docs can be accessed via `http://127.0.0.1:8000/docs`

 

### Make predictions

Send a HTTP request to the running server, e.g.

```

curl --location --request GET 'http://127.0.0.1:8000/predict/?text='

```

### Visualize prediction and attentions

Send a HTTP request to the running server, e.g.

```

http://127.0.0.1:8000/visualize/?text=

```

The server responds with a static html which should look something like the following:

![Prediction and Attention Visualization Response](img/prediction_and_attention_visualization_response.png)