jpbburns / ling573

Repository for LING573 coursework.

LING573

Repository for LING573 coursework.

Deliverable 4

Command Pipeline: python evaluate_reddit.py # to generate humor scores for reddit data python normalize.py # to generate normalized upvote scores for reddit data python run_correlation.py # to generate output & results

Spearman correlations and plot figures will be output to: ../results/D4/ Outputs of reddit data with predicted humor scores will be sent to: ../outputs/D4/

Deliverable 3

To Run: python FFNN.py

To re-extract pretrained embeddings: python FFNN.py --extract_pretrained

Pretrained embeddings are saved in the 'src/saved' directory. Pretrained BERT files are here (must be placed in the 'src' directory for use): https://drive.google.com/drive/folders/1n2QVlrKonIDcNHAD6PlzopQ4EALdcP-z?usp=sharing

The output of our hyperparameter testing is directed to the file locations: ../results/D3_scores.out & ../outputs/D3/{outputfile_name_for_hyperparameter_test}.out

Best model RMSE & hyperparameters will be printed to the bottom of ../results/D3_scores.out

Deliveralbe 2

To Run:

Install dependencies: pip install -r requirements.txt

To rerun training: python main.py --train

To run to and generate output with scores: python main.py

The output is directed to the console and file locations: ../results/D2_scores.out & ../outputs/D2/output.csv

Due to github;s limitations on file size, we are not able to host the pretrained files in this repo.

The pretrained files can be found hosted on our google drive here: https://drive.google.com/drive/folders/1n2QVlrKonIDcNHAD6PlzopQ4EALdcP-z?usp=sharing

Alternatively, to generate the files yourself, you can rerun the training prcodeure as outlined above.