README.md

Tmema

The code of paper "Defining and Remembering Objects: A Transformer Model with External Token Memories and Attention for PersonaChat"

How to reproduce the experiment

The code is adapted from LMEDR.

Environment

pip install -r requirements.txt

git clone https://github.com/Chenrj233/ParlAI.git
cd ParlAI
python setup.py install

Please replace eval_f1.py and eval_hits.py in /ParlAI/projects/convai2/ with the corresponding files in /other/. Similarly, replace the generation_utils.py in transformers/ with the corresponding files in /other/, the file is in a path similar to

| -- python3.8
	| -- site-packages
		| -- transformers
			| -- modeling_utils.py
			| -- generation_utils.py
			| -- ...

In addition, please download the datasets ConvAI2 PersonaChat and DNLI by yourself and replace the corresponding data under the /data folder.

Fine-tuning

Use the following command to fine-tune the model:

python train_PersonaChat.py
    --lr 8e-6 \
    --epochs 31 \
    --train_batch_size 2 \
    --valid_batch_size 2 \
    --infer_batch_size 64 \
    --gpu 1 \
    --output_dir 'checkpoint/persona_tmema' \
    --num_latent 40000 \
    --num_latent2 40000

Use --gpu to select which GPU you want to use. (No parallel support)
Use --num_latent to set the memory pool size. (40000 need 47GB GPU memory)
Add --smalldataset to use the smaller dataset to debug.
Add --revised to train and evaluate revised datasets. (original is the default)

Evaluation

F1 & BLEU

python evaluation_PersonaChat.py \
    --model_checkpoint ./checkpoint/persona_tmema_original \
    --eval_type f1 \
    --beam 2 \
    --max_history 7 \
    --gpu 1

Hits@1

python evaluation_PersonaChat.py \
    --model_checkpoint ./checkpoint/persona_tmema_original \
    --eval_type hits@1 \
    --gpu 2

PPL

python train_PersonaChat.py \
    --load_from ./checkpoint/persona_tmema_original \
    --eval \
    --gpu 1 \
    --num_latent 40000 \
    --num_latent2 40000