The directory contains code to run the CRF model used as baseline. Code to train, fine-tune and validate models are given.
For single tasks, one model for each of the three tasks will be computed by running the python script main_threeTasks.py: each model will be stored under the folder models. In order to fine-tune the two CRF model parameters c1 and c2, the python script main_finetune.py can be run: a plot gathering the results is saved in the folder plots and the best model will be save in the models folder. To compute the validation score, the script validation.py needs to have the models generated previously crf_t1.pkl, crf_t2.pkl and crf_t3.pkl and will print the classification scores.
The data is expected to be in a dataset folder, in the main repository directory, with three files inside it: clean_train.txt for the training dataset, clean_test.txt for the testing dataset, and clean_valid.txt for the validation dataset.
python main_finetune.py
python main_threeTasks.py
python validation.py
README.mdthis file.code/- feature_extraction_supporting_functions_words helper functions to extract features from words.
- feature_extraction_words functions to extract features from words.
- utils utility functions to load data and redirected log files.
- main_finetune python script to fine-tune two model parameters.
- main_threeTasks python script to train one CRF model for each task.
- validation python script to compute classification score on validation dataset for the three tasks.
- Numpy: 1.13.3
- Sklearn : 0.19.1
- Sklearn crfsuite Sklearn crfsuite : 0.3.6
- Python 3.5