padas-lab-de/learn-from-one-specialized-sub-teacher

Official implementation of "Learn From One Specialized Sub-Teacher: One-to-One Mapping for Feature-Based Knowledge Distillation" accepted in EMNLP-Findings 2023

Learn From One Specialized Sub-Teacher: One-to-One Mapping for Feature-Based Knowledge Distillation

This repository contains the official implementation of the paper Learn From One Specialized Sub-Teacher: One-to-One Mapping for Feature-Based Knowledge Distillation (EMNLP-Findings 2023). A short version of this paper was at first accepted at ICML-Neural-Compression workshop 2023.

For more details about the technical details, please refer to our paper.

Installation

Run command below to install the environment (using python3.9):

pip install -r requirements.txt

Data Preparation

Run command below to get SQUAD-V1 data:

download the data from: https://drive.google.com/drive/folders/1IHRfVG0VBE22-0AAiWODNy80T8ckA-yK?usp=sharing

Run command below to get GLUE data:

python download_glue_data.py --data_dir glue_data --task all

Student Initialization

The 6-layer DistilBERT is the initial student model.

Fine-tuning

Run command below to get fine-tuned teacher model for every task of GLUE, SQUAD-V1, and IMDB:

# GLUE (e.g., RTE)
  python run_glue.py   --model_type bert   --model_name_or_path bert-base-uncased   --task_name rte   --data_dir ./glue_data/RTE/   --do_lower_case   --max_seq_length 128   --do_train   --per_gpu_train_batch_size 32   --per_gpu_eval_batch_size 32   --learning_rate 5e-5   --num_train_epochs 3.0   --output_dir ./model/RTE/teacher/
# SQUAD
 python run_squad.py --model bert-base-uncased  --output_dir  ./output   --output_dir_intermed ./output_intermed

# IMDB
 python run_imdb.py --model bert-base-uncased  --output_dir  ./output   --output_dir_intermed ./output_intermed

Distillation

Run command below to get distilled student model for every task of GLUE, SQUAD-V1, and IMDB:

# GLUE (e.g., RTE)
python run_glue_w_distillation.py  --teacher_model_name_or_path ./model/RTE/teacher     --student_model_name_or_path distilbert-base-cased   --task_name rte  --max_seq_length 128 --output_dir ./output  --alpha_ce 0 --alpha_glue 0.5 --alpha_corr 0.005 --alpha_mse 0 --alpha_cos 0 --do_mask True --num_train_epochs 3.0

# SQUAD
python run_squad_w_distillation.py --model_type distilbert --model_name_or_path distilbert-base-uncased --output_dir ./run_squad_w_distillation_CrossCorrLoss --teacher_type bert --teacher_name_or_path ./bert-base-uncased-finetuned-squad-v1 --data_dir data  --train_file train.json --predict_file dev.json  --do_train --do_eval  --do_lower_case --save_steps 5541 --logging_steps 5541 --alpha_cos 0.0 --alpha_corr 0.5 --alpha_ce 0.0 --alpha_squad 0.5 --num_train_epochs 3

# IMDB
 python run_imdb_w_distillation.py  --student_model_name_or_path  distilbert-base-uncased  --teacher_model_name_or_path ./bert_model_fine_tuned_on_imdb --output_dir ./output --output_dir_intermed ./output_intermed

Results

On the GLUE development set:

Cite

@inproceedings{saadi-etal-2023-learn,
    title = "Learn From One Specialized Sub-Teacher: One-to-One Mapping for Feature-Based Knowledge Distillation",
    author = "Saadi, Khouloud  and Mitrovi{\'c}, Jelena  and Granitzer, Michael",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    year = "2023",
    doi = "10.18653/v1/2023.findings-emnlp.882",
    pages = "13235--13245"
 }