usage: python iterate.py [-h] [-l LANG] [-d DATASET] [-p PROMPT] [-td TARGET_DATASET] [-sd SOURCE_DATASET] [-m MODEL]
[-tr TARGET_RETRIEVE] [-sr SOURCE_RETRIEVE] [-y] [-r RESULT_DIR] [-rl] [--slow] [-cf] [-ssim SOURCE_SIM]
[-tsim TARGET_SIM] [-s SPLIT_START] [-e SPLIT_END] [-i INTERM] [-t TEMPERATURE]
Prompt benchmarking utility
options:
-h, --help show this help message and exit
-l LANG, --lang LANG
-d DATASET, --dataset DATASET
-p PROMPT, --prompt PROMPT
-td TARGET_DATASET, --target-dataset TARGET_DATASET
-sd SOURCE_DATASET, --source-dataset SOURCE_DATASET
-m MODEL, --model MODEL
model
-tr TARGET_RETRIEVE, --target-retrieve TARGET_RETRIEVE
no. examples to retrieve from target
-sr SOURCE_RETRIEVE, --source-retrieve SOURCE_RETRIEVE
no. examples to retrieve from source
-y, --yes Say yes to any conditionals
-r RESULT_DIR, --result-dir RESULT_DIR
-rl, --randomize-labels
randomize labels (for ablation)
--slow slow down API calls
-cf, --content-filter
ignore content filter (save all egs)
-ssim SOURCE_SIM, --source-sim SOURCE_SIM
Source similarity matrix
-tsim TARGET_SIM, --target-sim TARGET_SIM
Target similarity matrix
-s SPLIT_START, --split-start SPLIT_START
-e SPLIT_END, --split-end SPLIT_END
-i INTERM, --interm INTERM
-t TEMPERATURE, --temperature TEMPERATURE
- Create a conda environment
conda create -n sspclt python=3.10
conda activate sspclt
pip install -r requirements.txt
-
Add keys in .env. You will require an
AZURE_OPENAI_KEYand anAZURE_OPENAI_ENDPOINTfor running GPT models, and aTOGETHER_API_KEYfor running LLaMa. Place the .env file in the parent directory. -
Run iterate.py using the commands shown above. You would need to pass a path to the source dataset, and the source retrievals in tsv/jsonl format. The columns of the TSV should be input, output and language, and JSONL dataset records should have these three keys as well. Refer to the shell script (run_all.sh) for more details.
-
For running LLaMa, use
iterate_llama.py. For running SSP-CLT-ILP, useiterate_ilp.py.