The 1st place solution for AutoNLP 2019.
Download the competition's starting kit and run
python run_local_test.py -dataset_dir=./AutoDL_sample_data/DEMO -code_dir=./AutoDL_sample_code_submission
You can change the argument dataset_dir to other datasets, and change the argument dataset_dir to the directory containing this code (model.py).
And we use the embedding model provided by the competition, which is saved in /app/embedding.
The file ac.cpython-36m-x86_64-linux-gnu.so is compiled by Cython, and its source code is ac.pyx .
This challenge focuses on the problem of multi-class text categorization collected from real-world businesses. The datasets consist of content file, label file and meta file, where content file and label file are split into train parts and test parts:
- Content file ({train, test}.data) contains the content of the instances. Each row in the content file represents the content of an instance.
- Label file ({train, dataset_name}.solution) consists of the labels of the instances in one-hot format. Note that each of its lines corresponds to the corresponding line number in the content file.
- Meta file (meta.json) is a json file consisted of the meta information about the dataset, including language, train instance number, test instance number, category number.
The following figure illustrates the form of the datasets:
DeepBlueAI: [email protected]