pip install torch==1.3.1
pip install numpy==1.19.2
cd fairseq
pip install --editable .
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Change the encoder.json path to correct path in fairseq/fairseq/data/encoder/gpt2_bpe_utils.py line 131 -
Training and validation data for generator inside /fairseq/metaphor folder -
Training and validation data for discriminator inside /fairseq/glue_data/metaphor
All preprocessed versions shared as well
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Preprocessed data for generator is inside /fairseq/metaphor folder . You can see bpe and idx files -
Preprocessed data for discriminator is inside /fairseq/metaphor-bin folder .
If you want to use your own metaphor data for generator
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Create train and val source and target files for a finetuning seq2seq model. You can see my data format -
The input format has the TEXT portion to be replaced enclosed in <V>. You can emulate the same -
run sh preprocess1.sh and sh preprocess2.sh
If you want to use your own metaphor data for discriminator
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Create train.tsv and dev.tsv in same tab seperated format -
./examples/roberta/preprocess_GLUE_tasks.sh glue_data metaphor
Training
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To run bart model sh trainbart.sh -
To run roberta model sh roberta_train.sh
Inference using our finetuned model
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For inference for MERMAID use inference.py -
You have to change WP_scorers.tsv to reflect your own coefficients (can be positive or negative anything) and also directories -
You have to edit inference.py and WP_scorers.tsv to your checkpoint locations download checkpoints from the link https://drive.google.com/drive/folders/1j6HNNBc_Ess-FSSbZNwA_09WOvE2C1jf?usp=sharing