dialogGenerator_transformer

Installation

pip install -r requirement.txt

학습 방법

train.py / train_key.py 파일 실행 (키워드의 사용 유무)

train의 argument:

Argument	Type	Default value	Description
dataset_path	str	""	Path or url of the dataset.
keyword_module	str	""	Use keyword module or not
train_batch_size	int	20	Batch size for training
valid_batch_size	int	20	Batch size for validation
gradient_accumulation_steps	int	8	Accumulate gradients on several steps
lr	float	6.25e-5	Learning rate
max_norm	float	1.0	Clipping gradient norm
n_epochs	int	5	Number of training epochs
personality_permutations	int	1	Number of permutations of personality sentences
device	str	"cuda" if torch.cuda.is_available() else "cpu"	Device (cuda or cpu)
fp16	str	""	Set to O0, O1, O2 or O3 for fp16 training (see apex documentation)
local_rank	int	-1	Local rank for distributed training (-1: not distributed)
gpt2_model_name	str	"gpt2"	Path, url or short name of the model

문장 생성 방법

interact.py / interact_key.py 파일 실행 (키워드의 사용 유무)

interact의 argument:

Argument	Type	Default value	Description
dataset_path	str	""	Path or url of the dataset.
model_checkpoint	str	""	Path, url or short name of the model
device	str	cuda if torch.cuda.is_available() else cpu	Device (cuda or cpu)
gpt2_model_name	str	"gpt2"	name of the model ex)openai-gpt
no_sample	action store_true	Set to use greedy decoding instead of sampling
max_length	int	40	Maximum length of the output utterances
min_length	int	1	Minimum length of the output utterances
seed	int	0	Seed
temperature	int	0.7	Sampling softmax temperature
top_k	int	0	Filter top-k tokens before sampling (<=0: no filtering)
top_p	float	0.9	Nucleus filtering (top-p) before sampling (<=0.0: no filtering)

python interact.py --dataset_path DATAPATH/Name --model_checkpoint MODELPATH/
python interact_key.py --dataset_path DATAPATH/Name --model_checkpoint MODELPATH/

데이터 포맷

Source|Target 형태로 txt파일 구성.

아래의 형태로 같은 경로에 데이터가 존재해야 함.
Name_train.txt / Name_train_keyword.txt
Name_valid.txt / Name_valid_keyword.txt
Name_test.txt / Name_test_keyword.txt

Reference

@article{DBLP:journals/corr/abs-1901-08149, author = {Thomas Wolf and Victor Sanh and Julien Chaumond and Clement Delangue}, title = {TransferTransfo: {A} Transfer Learning Approach for Neural Network Based Conversational Agents}, journal = {CoRR}, volume = {abs/1901.08149}, year = {2019}, url = {http://arxiv.org/abs/1901.08149}, archivePrefix = {arXiv}, eprint = {1901.08149}, timestamp = {Sat, 02 Feb 2019 16:56:00 +0100}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-1901-08149}, bibsource = {dblp computer science bibliography, https://dblp.org} }