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KEMI

README.md

Running Scripts for Muffin (KEMI)

Training Base Model

Run:

python train_KEMI.py --config_name strat --inputter_name strat --data_name esconv --knowledge_name sbert --eval_input_file ./DATA --seed 13 --max_input_length 256 --max_decoder_input_length 40 --train_batch_size 16 --gradient_accumulation_steps 1 --eval_batch_size 16 --learning_rate 3e-5 --num_epochs 5 --warmup_steps 100 --fp16 false --loss_scale 0.0 --pbar true

For more details, refers to the original codes from here.

Generating Samples

Run:

python sample_ESConv.py --config_name vanilla --inputter_name vanilla --seed 0 --load_checkpoint ./DATA/vanilla.vanilla/best_model.bin --fp16 false --max_input_length 160 --max_decoder_input_length 40 --max_length 40 --min_length 10 --infer_batch_size 8 --infer_input_file ./DATA/train.txt --temperature 0.7 --top_k 0 --top_p 0.9 --num_beams 10 --num_beam_groups 10 --repetition_penalty 1 --no_repeat_ngram_size 3 --num_return_sequences 10

./DATA/vanilla.vanilla/best_model.bin is the path with the checkpoint of the base model. Change all * vanilla * as * strat * to generate samples for the Joint model.

Getting AI Feedback

First, process the raw by enter reward_model/Llama and run:

python process_raw.py --input_file_dir /home/jiashuo/codes/Muffin/KEMI/DATA/strat.strat.esconv.sbert/candidates_10_best_model.bin_train

Training Muffin Model

Run bash RUN/train_vanilla.sh to train your model.