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https://github.com/huggingface/transfer-learning-conv-ai

🦄 State-of-the-Art Conversational AI with Transfer Learning
https://github.com/huggingface/transfer-learning-conv-ai

chatbots deep-learning dialog gpt gpt-2 neural-networks nlp pytorch transfer-learning

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🦄 State-of-the-Art Conversational AI with Transfer Learning

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# 🦄 Building a State-of-the-Art Conversational AI with Transfer Learning



The present repo contains the code accompanying the blog post [🦄 How to build a State-of-the-Art Conversational AI with Transfer Learning](https://medium.com/@Thomwolf/how-to-build-a-state-of-the-art-conversational-ai-with-transfer-learning-2d818ac26313).



This code is a clean and commented code base with training and testing scripts that can be used to train a dialog agent leveraging transfer Learning from an OpenAI GPT and GPT-2 Transformer language model.



This codebase can be used to reproduce the results of HuggingFace's participation to NeurIPS 2018 dialog competition [ConvAI2](http://convai.io/) which was state-of-the-art on the automatic metrics. The 3k+ lines of competition code was distilled in about 250 lines of training code with distributed & FP16 options to form the present repository.



This model can be trained in about one hour on a 8 V100 cloud instance (currently costs about $25) and a pre-trained model is also made available.



## Installation



To install and use the training and inference scripts please clone the repo and install the requirements:



```bash

git clone https://github.com/huggingface/transfer-learning-conv-ai

cd transfer-learning-conv-ai

pip install -r requirements.txt

python -m spacy download en

```



## Installation with Docker



To install using docker please build the self-contained image:



```bash

docker build -t convai .

```



_Note: Make sure your Docker setup allocates enough memory to building the container. Building with the default of 1.75GB will fail due to large Pytorch wheel._



You can then enter the image  



```bash

ip-192-168-22-157:transfer-learning-conv-ai loretoparisi$ docker run --rm -it convai bash

root@91e241bb823e:/# ls

Dockerfile  README.md  boot                  dev  home         lib    media  models  proc              root  sbin  sys  train.py  utils.py

LICENCE     bin        convai_evaluation.py  etc  interact.py  lib64  mnt    opt     requirements.txt  run   srv   tmp  usr       var

```



You can then run the `interact.py` script on the pretrained model:



```bash

python3 interact.py --model models/

```



## Pretrained model



We make a pretrained and fine-tuned model available on our S3 [here](https://s3.amazonaws.com/models.huggingface.co/transfer-learning-chatbot/finetuned_chatbot_gpt.tar.gz). The easiest way to download and use this model is just to run the `interact.py` script to talk with the model. Without any argument, this script will automatically download and cache our model.



## Using the training script



The training script can be used in single GPU or multi GPU settings:



```bash

python ./train.py  # Single GPU training

python -m torch.distributed.launch --nproc_per_node=8 ./train.py  # Training on 8 GPUs

```



The training script accept several arguments to tweak the training:



Argument | Type | Default value | Description

---------|------|---------------|------------

dataset_path | `str` | `""` | Path or url of the dataset. If empty download from S3.

dataset_cache | `str` | `'./dataset_cache.bin'` | Path or url of the dataset cache

model | `str` | `"openai-gpt"` | Path, url or short name of the model

num_candidates | `int` | `2` | Number of candidates for training

max_history | `int` | `2` | Number of previous exchanges to keep in history

train_batch_size | `int` | `4` | Batch size for training

valid_batch_size | `int` | `4` | Batch size for validation

gradient_accumulation_steps | `int` | `8` | Accumulate gradients on several steps

lr | `float` | `6.25e-5` | Learning rate

lm_coef | `float` | `1.0` | LM loss coefficient

mc_coef | `float` | `1.0` | Multiple-choice loss coefficient

max_norm | `float` | `1.0` | Clipping gradient norm

n_epochs | `int` | `3` | 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)



Here is how to reproduce our results on a server with 8 V100 GPUs (adapt number of nodes and batch sizes to your configuration):



```bash

python -m torch.distributed.launch --nproc_per_node=8 ./train.py --gradient_accumulation_steps=4 --lm_coef=2.0 --max_history=2 --n_epochs=1 --num_candidates=4 --personality_permutations=2 --train_batch_size=2 --valid_batch_size=2

```



This model should give a Hits@1 over 79, perplexity of 20.5 and F1 of 16.5 using the convai2 evaluation script (see below).



These numbers are slightly lower than the number we obtained in the ConvAI2 competition. Here is what you can tweak to reach the same results:



- in the ConvAI2 competition we also used tweaked position emebddings so that the history of the dialog always start at with the same embeddings. This is easy to add with pytorch-transformers and should improve the hits@1 metric.

- in the ConvAI2 competition we used a beam search decoder. While the results are better in term of f1 metric, our feeling is that the human experience is less compelling with beam search versus the nucleus sampling detector which is provided in the present repository.



## Using the interaction script



The training script saves all the experiments and checkpoints in a sub-folder named with the timestamp of the experiment in the `./runs` folder of the repository base folder.



You can then use the interactive script to interact with the model simply by pointing to this folder.



Here is an example command line to run the interactive script:



```bash

python ./interact.py --model_checkpoint ./data/Apr17_13-31-38_thunder/  # run the interactive script with a training checkpoint

python ./interact.py  # run the interactive script with the finetuned model on our S3

```



The fine-tuned model will gives FINAL Hits@1: 0.715



The interactive script accept a few arguments to tweak the decoding algorithm:



Argument | Type | Default value | Description

---------|------|---------------|------------

dataset_path | `str` | `""` | Path or url of the dataset. If empty download from S3.

dataset_cache | `str` | `'./dataset_cache.bin'` | Path or url of the dataset cache

model | `str` | `"openai-gpt"` | Path, url or short name of the model

max_history | `int` | `2` | Number of previous utterances to keep in history

device | `str` | `cuda` if `torch.cuda.is_available()` else `cpu` | Device (cuda or cpu)

no_sample | action `store_true` | Set to use greedy decoding instead of sampling

max_length | `int` | `20` | Maximum length of the output utterances

min_length | `int` | `1` | Minimum length of the output utterances

seed | `int` | `42` | 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)



## Running ConvAI2 evaluation scripts



To run the evaluation scripts of the ConvAI2 challenge, you first need to install `ParlAI` in the repo base folder like this:



```bash

git clone https://github.com/facebookresearch/ParlAI.git

cd ParlAI

python setup.py develop

```



You can then run the evaluation script from `ParlAI` base folder:



```bash

cd ParlAI

python ../convai_evaluation.py --eval_type hits@1  # to download and evaluate our fine-tuned model on hits@1 metric

python ../convai_evaluation.py --eval_type hits@1  --model_checkpoint ./data/Apr17_13-31-38_thunder/  # to evaluate a training checkpoint on hits@1 metric

```



The evaluation script accept a few arguments to select the evaluation metric and tweak the decoding algorithm:



Argument | Type | Default value | Description

---------|------|---------------|------------

eval_type | `str` | `"hits@1"` | Evaluate the model on `hits@1`, `ppl` or `f1` metric on the ConvAI2 validation dataset

model | `str` | `"openai-gpt"` | Path, url or short name of the model

max_history | `int` | `2` | Number of previous utterances to keep in history

device | `str` | `cuda` if `torch.cuda.is_available()` else `cpu` | Device (cuda or cpu)

no_sample | action `store_true` | Set to use greedy decoding instead of sampling

max_length | `int` | `20` | Maximum length of the output utterances

min_length | `int` | `1` | Minimum length of the output utterances

seed | `int` | `42` | 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)



## Data Format

see `example_entry.py`, and the comment at the top.



## Citation



If you use this code in your research, you can cite our NeurIPS CAI workshop [paper](http://arxiv.org/abs/1901.08149):



```bash

@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}

}

```