1. bart_rl_hyperparameter_search.yaml, t5_rl_hyperparameter_search.yaml, gpt2_rl_hyperparameter_search.yaml: YAML files to run H2 for the three different generators on Amulet. Warning: Amulet doesn't save outputs and networkx fails with PowerIterationFailedConvergence for a large horizon length, i.e., greater than 150 steps.

2. bart.py: Fine tunes facebook/bart-large model on given data, and/or generates candidate leakages from given leakages, by randomly masking spans in the sentence.

3. classification.py, classification_with_leakage_filtering.py: Almost same files, except find_leakages function is different; in the later one it returns the filtered candidates too (sentences with label 0). Has classifier utilities to fine-tune and run classification model.

4. classifier_train.py: Takes in training data and fine tunes Transformers classifier on the data

5. encoder_decoder.py: Fine tunes and trains an encoder decoder model for H3, using model cross attentions. Uses BERT as encoder and GPT2 as decoder. The model has not been trained yet as losses kept oscillating.

6. gpt2.py: Generates candidate leakages from gpt2-medum trained on queries or tweets.

7. graph_rl.py: Driver code for RL search on graph, using any algorithm from OpenAI stable_baselines3==1.0. Can use any transformer environment gpt2env.py, bartenv.py, t5env.py.

8. mixer_decoder_modified.py: Contains the mixer and classifier (encoder) - generator (decoder). Uses past_key_values as the decoder input after encoder context or hidden_states has been transformed through the mixer. Warning: Incomplete Code.

9. sampling.py: Does a sampling of hidden_states of the classifier using Gaussian Processes or assuming a pointwise Gaussian for every hidden_state. Gaussian Processes use pymc3 and the code runs quite slow.

10. t5.py: Fine tunes t5-base model on given data, and/or generates candidate leakages from given leakages, by masking Noun Phrases in the sentence.

11. utils.py: Utilities used for classification. Required by classification.py everywhere.

12. bart_masking.py: Takes in sentences, and masks important POS Tags (Verb, Noun, Adverb, Adjective, Proper Noun). Generates all possible combinations of masked templates, and fills the masks using fine tuned BART. The generated sentences are compared with the semantic similarity to the original sentence, and the masked templates are sorted in decreasing order of average semantic similarity to the original sentence. Higher ranked templates are better in preserving the intent of the query, and in generating queries close to the original query.

1. Create a conda virtual environment in GCR machine

source /anaconda/bin/activate
conda activate py37_default

2. Install requirements.txt and other required packages

pip install -r requirements.txt
pip install matplotlib sklearn networkx

1. First T5, BART, GPT2 need to be fine tuned. The files t5.py, bart.py have a flag --no_train which is to be used from next runs. GPT2 needs a different fine tuning script. To fine tune BART and then generate from it, using default parameters use,

python bart.py --queries_file /path/to/queries/file \
    --leakages_file /path/to/leakages/file \
    --output_file output-file-name.json \
    --output_dir /path/to/output/dir \
    --model_path /path/where/to/save/trained/model \
    --train_samples 50000 \
    --k 10

An output json file will be generated, which is to be annotated and evaluated.

Use --no_train to prevent training when the models have already been once fine tuned. t5.py is identical in behavior, and it also takes in --spacy_model, which can be set to a different language other than English, for example Japanese ja_core_web_lg. The model to be used then is mT5 (Multilingual T5).

For running RL search using T5, for example, use,

python graph_rl.py --input_file /path/to/input/file \
    --output_file output-file-name.json \
    --cls_model /path/to/finetuned/classifier \
    --gen_model /path/to/finetuned/generator-t5 \
    --batch_size 64 \
    --top_k 200 \
    --steps 5000 \
    --horizon 100 \
    --sim_threshold 0.6 \
    --gen_model_name t5-base

By default, output files will be generated in the current directory ., with an episode_id prefixed, such as 1_outputs.json. Keep --horizon less than 200, else, networkx fails with PowerIterationFailedConvergence.

To fine tune, save and generate from bert-gpt2 encoder-decoder, use,

python encoder_decoder.py --queries_file /path/to/queries/file \
    --leakage_file /path/to/leakages/file \
    --output_file /path/to/outputs.json \
    --model_dir /path/to/save/finetuned/model \
    --train_dir /path/to/log/dir 

To create a file containing all generated sentences to be annotated,

python data_handling_and_evaluation/process_json_outputs.py --input_dir /path/to/json/outputs \
    --output_file /path/to/file/to/be/annotated \
    --samples 500

This generates a file with 500 generated sentences to be annotated. If --input_dir is the name of a file, the script processes that file only, else, it processes all json files in the given directory.

After annotations are done, to find the offensive and leakage percentages, use,

python data_handling_and_evaluation/find_leakages.py --input_file /path/to/input/file \
    --output_leakage_file /path/to/true/leakages \
    --cls_model /path/to/finetuned/classifier

This prints to terminal the offensive, true leakages and filtered leakages percentages.

To compute automated metrics for H1 or H2, such as BLEU, Self-BLEU, Perplexity, Semantic Similarities, use compute_metrics_h1.py or compute_metrics_h2.py, and load finetuned GPT2 from its path. They take in the original json output files. For compute_metrics_h2.py,

python compute_metrics_h2.py --input_data /path/to/all/episodes/outputs \
    --lm_path /path/to/finetuned/gpt2

Details of data handling and evaluation are there in data_handling_and_evaluation README.md.