https://github.com/soumik12345/multi-label-text-classification
A multi-label text classifier to predict the subject areas of arXiv papers from their abstract bodies.
https://github.com/soumik12345/multi-label-text-classification
deep-learning text-classification
Last synced: 11 months ago
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A multi-label text classifier to predict the subject areas of arXiv papers from their abstract bodies.
- Host: GitHub
- URL: https://github.com/soumik12345/multi-label-text-classification
- Owner: soumik12345
- Created: 2021-09-22T07:49:56.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2021-10-06T15:37:37.000Z (almost 5 years ago)
- Last Synced: 2025-05-12T13:12:22.575Z (about 1 year ago)
- Topics: deep-learning, text-classification
- Language: Jupyter Notebook
- Homepage: https://keras.io/examples/nlp/multi_label_classification/
- Size: 239 KB
- Stars: 17
- Watchers: 1
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Multi-label Text Classification
Holds code for collecting data from arXiv to build a multi-label text classification dataset and a simpler classifier on
top of that. Our dataset is now [available on Kaggle](https://www.kaggle.com/spsayakpaul/arxiv-paper-abstracts). The dataset collection process
has been shown in [this notebook](https://github.com/soumik12345/multi-label-text-classification/blob/master/beam_arxiv_scrape.ipynb). We leverage
Apache Beam to design our data collection pipeline and our pipeline can be run on [Dataflow](https://cloud.google.com/dataflow) at scale. We hope
the data will be a useful benchmark for building multi-label text classification systems.
Here's an accompanying blog post on keras.io discussing the motivation behind this dataset, building a simple
baseline model, etc.: [Large-scale multi-label text classification](https://keras.io/examples/nlp/multi_label_classification/).
## Acknowledgements
We would like to thank [Matt Watson](https://github.com/mattdangerw) for helping us build the simple baseline classifier model. Thanks to
[Lukas Schwab](https://github.com/lukasschwab) (author of [`arxiv.py`](https://github.com/lukasschwab/arxiv.py)) for helping us build
our initial data collection utilities. Thanks to [Robert Bradshaw](https://www.linkedin.com/in/robert-bradshaw-1b48a07/) for his inputs
on the Apache Beam pipeline. Thanks to the [ML-GDE program](https://developers.google.com/programs/experts/) for providing GCP credits
that allowed us to run the Beam pipeline at scale on Dataflow.