Codes for NAACL 2021 paper 'Noisy Self-Knowledge Distillation for Text Summarization'
The code is based on UNILM, and summarization data can be download at (https://github.com/microsoft/unilm/tree/master/s2s-ft)
MODEL_PATH=../models/xsum.unilm/ckpt-40000 SPLIT=test INPUT_JSON=../data/xsum.test.uncased_tokenized.json
export CUDA_VISIBLE_DEVICES=5 export OMP_NUM_THREADS=4 export MKL_NUM_THREADS=4
python decode_seq2seq.py
--fp16 --model_type unilm --tokenizer_name unilm1.2-base-uncased --input_file ${INPUT_JSON} --split $SPLIT --do_lower_case
--model_path ${MODEL_PATH} --max_seq_length 512 --max_tgt_length 48 --batch_size 32 --beam_size 5
--length_penalty 0 --forbid_duplicate_ngrams --mode s2s --forbid_ignore_word "."
TRAIN_FILE=../data/xsum.train.uncased_tokenized.json CACHE_DIR=../../cache OUTPUT_DIR=../models/xsum.unilm.distill TEACHER=../models/xsum.unilm/ckpt-40000/pytorch_model.bin
BATCH_SIZE=8
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python -m torch.distributed.launch --nproc_per_node=8 --master_port 29886 run_seq2seq.py
--train_file $TRAIN_FILE --output_dir $OUTPUT_DIR
--model_type unilm --model_name_or_path unilm1.2-base-uncased --do_lower_case --fp16 --fp16_opt_level O2
--max_source_seq_length 464 --max_target_seq_length 48 --per_gpu_train_batch_size $BATCH_SIZE --gradient_accumulation_steps 1
--learning_rate 7e-5 --num_warmup_steps 500 --num_training_steps 40000 --cache_dir $CACHE_DIR --save_steps 2000
--use_distill 1 --kd_weight 0.6 --teacher_dropout_prob 0.15 --use_teacher_dropout 1 --teacher_model $TEACHER --word_drop_prob 0.1 --use_noisy_student 1 --sent_shuffle_k 2
MODEL_PATH=../models/xsum.unilm.distill/ckpt-40000 SPLIT=test INPUT_JSON=../data/xsum.test.uncased_tokenized.json
export CUDA_VISIBLE_DEVICES=1 export OMP_NUM_THREADS=4 export MKL_NUM_THREADS=4
python decode_seq2seq.py --model_type unilm --tokenizer_name unilm1.2-base-uncased --input_file ${INPUT_JSON} --split $SPLIT --do_lower_case
--model_path ${MODEL_PATH} --max_seq_length 512 --max_tgt_length 48 --batch_size 32 --beam_size 8
--length_penalty 0.9 --forbid_duplicate_ngrams --mode s2s --forbid_ignore_word "." --min_len 5