AutoNLP-WAIC2019
AutoDL competition introduction:NeurIPS 2019 AutoDL Challenges
Team: Upwind_flys Rank: Second place
Our algorithm process data and select models automatically, model lib contains Character-based model, word-based model, which can be selected according to data meta-feature. Then algorithm automatically select early stop strategy and restore weights based on the Information of feedback simulation.
Code Framework is AutoNlp-WAIC2019 starting kit
AutoDL_ingestion_program/: The code and libraries used on Codalab to run your submission.
AutoDL_scoring_program/: The code and libraries used on Codalab to score your submission.
AutoDL_sample_code_submission/: An example of code submission you can use as template.
AutoDL_sample_data/: Some sample data to test your code before you submit it.
run_local_test.py: A python script to simulate the runtime in codalab
model.py: Implementation of our algorithm and logics
data_manager.py: Data processing related module
model_manager.py: Automatic model generation from model library
Run the project locally:
python run_local_test.py -dataset_dir=./AutoDL_sample_data/DEMO -code_dir=./AutoDL_sample_code_submission
| metrics | O1 | O2 | O3 | O4 | O5 |
|---|---|---|---|---|---|
| ALC | 0.8139 | 0.9277 | 0.8053 | 0.9758 | 0.8870 |
| 2AUC-1 | 0.8168 | 0.9723 | 0.8345 | 0.9966 | 0.9447 |
Our work in AutoML and meta-learning fields: Efficient Automatic Meta Optimization Search for Few-Shot Learning
The project is developed at Lenovo Inc,It is distributed under MIT LICENSE