gpt2-dialogue-generation-pytorch

This is a multi-turn chatbot project using the pre-trained GPT-2[1] introduced in How to build a State-of-the-Art Conversational AI with Transfer Learning[2].

Especially, this repository uses the GPT-2 Language Modeling Head model which has one additional linear layer to conduct Language Modeling task to consider the dialogue contexts and make a proper next response.

I did not include the persona information unlike the original version.

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Arguments

Arguments for data loading

Argument	Type	Description	Default
data_dir	str	The name of the parent directory where data files are stored.	"data"
train_prefix	str	The prefix of the train data files' name.	"train"
valid_prefix	str	The prefix of the validation data files' name.	"valid"
train_frac	float	The ratio of the conversations to be included in the train set.	0.85
model_type	str	The model type of GPT-2. ("gpt2", "gpt2-medium", "gpt2-large", or "gpt2-xl")	"gpt2"

Arguments for training

Argument	Type	Description	Default
seed	int	The random seed.	0
data_dir	str	The name of the parent directory where data files are stored.	"data"
train_prefix	str	The prefix of the train data files' name.	"train"
valid_prefix	str	The prefix of the validation data files' name.	"valid"
model_type	str	The model type of GPT-2. ("gpt2", "gpt2-medium", "gpt2-large", or "gpt2-xl")	"gpt2"
bos_token	str	The BOS token.	"<bos>"
sp1_token	str	The speaker1 token.	"<sp1>"
sp2_token	str	The speaker2 token.	"<sp2>"
gpu	str	The index of GPU to use.	"0"
lr	float	The learning rate.	2e-5
warmup_ratio	float	The ratio of warmup steps to the total training steps.	0.1
batch_size	int	The batch size.	8
num_workers	int	The number of workers for data loading.	0
num_epochs	int	The number of total epochs.	10
max_len	int	The maximum length of input sequence.	1024
max_turns	int	The maximum number of dialogue histories to include.	5
ckpt_dir	str	The path for saved checkpoints.	"saved_models"
ckpt_name	str	The default name for the trained model. (without extension)	YOU MIGHT SPECIFY

Arguments for inference

Argument	Type	Description	Default
seed	int	The random seed.	0
model_path	str	The path to the model in HuggingFace Hub.	YOU SHOULD SPECIFY
gpu	str	The index of GPU to use.	"0"
max_turns	int	The maximum number of dialogue histories to include.	5
top_p	float	The top-p value for nucleus sampling decoding.	0.8
end_command	str	The command to stop the conversation when inferencing.	"Abort!"

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Datasets

By default, I propose the codes for downloading the datasets and preprocessing.

There are 4 types of the default datasets as follows.

• DailyDialog[3]
• EmpatheticDialogues[4]
• Persona-Chat[5]
• BlendedSkillTalk[6]

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How to run

Fine-tuning a new model

1. Install all required packages.

   pip install -r requirements.txt

   
   

2. Download & Preprocess all datasets.

   sh exec_load_data.sh

   After running it, you will have the following data directory structure if you follow the default argument setting.

   data
   └--gpt2
       └--train_utters.pickle
       └--train_ids.pickle
       └--valid_utters.pickle
       └--valid_ids.pickle
   

   
   

3. Run the following command to train the model.

   If you want to train it starting from a specific checkpoint, add the argument ckpt_name and make sure to notify the proper checkpoint name.

   sh exec_train.sh

Loading a fine-tuned model & Chatting with it

1. Install all required packages.

   pip install -r requirements.txt

   
   

2. If you already have a model which has been pushed to HuggingFace's Hub, you can load it and chat with the model. If you don't have a model, you can use the model I fine-tuned: https://huggingface.co/devjwsong/gpt2-open-domain-dialogue. To do that, change the argument --model_path in exec_infer.sh into a corresponding path.

   After that, run below command.

   sh exec_infer.sh

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References

[1] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.(http://www.persagen.com/files/misc/radford2019language.pdf)

[2] How to build a State-of-the-Art Conversational AI with Transfer Learning . (2019, May 9). (https://medium.com/huggingface/how-to-build-a-state-of-the-art-conversational-ai-with-transfer-learning-2d818ac26313)

[3] Li, Y., Su, H., Shen, X., Li, W., Cao, Z., & Niu, S. (2017). Dailydialog: A manually labelled multi-turn dialogue dataset. arXiv preprint arXiv:1710.03957. (https://arxiv.org/abs/1710.03957)

[4] Rashkin, H., Smith, E. M., Li, M., & Boureau, Y. L. (2018). Towards empathetic open-domain conversation models: A new benchmark and dataset. arXiv preprint arXiv:1811.00207. (https://arxiv.org/abs/1811.00207)

[5] Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., & Weston, J. (2018). Personalizing dialogue agents: I have a dog, do you have pets too?. arXiv preprint arXiv:1801.07243. (https://arxiv.org/abs/1801.07243)

[6] Smith, E. M., Williamson, M., Shuster, K., Weston, J., & Boureau, Y. L. (2020). Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills. arXiv preprint arXiv:2004.08449. (https://arxiv.org/abs/2004.08449)