jjdblast / quick-nlp

Pytorch NLP library based on FastAI

Quick NLP

Quick NLP is a deep learning nlp library inspired by the fast.ai library

It follows the same api as fastai and extends it allowing for quick and easy running of nlp models

Features

• Python 3.6 code

• Tight-knit integration with Fast.ai library:

  • Fast.ai style DataLoader objects for sentence to sentence algorithms
  • Fast.ai style DataLoader objects for dialogue algorithms
  • Fast.ai style DataModel objects for training nlp models

• Can run a seq2seq model with a few lines of code similar to existing fast.ai examples
• Easy to expand/train and try different models or use different data

• Ready made algorithms to try out

  • Seq2Seq https://arxiv.org/abs/1506.05869
  • Seq2Seq with Attention https://arxiv.org/abs/1703.03906
  • HRED http://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14567/14219
  • Attention is all you need (In Progress) http://papers.nips.cc/paper/7181-attention-is-all-you-need
  • Depthwise Separable Convolutions for Neural Machine Translation (TODO) https://arxiv.org/abs/1706.03059

Installation

Installation of fast.ai library is required. Please install using the instructions here . It is important that the latest version of fast.ai is used and not the pip version which is not up to date.

After setting up an environment using the fasta.ai instructions please clone the quick-nlp repo and use pip install to install the package as follows:

git clone https://github.com/outcastofmusic/quick-nlp
cd quick-nlp
pip install .

Docker Image

A docker image with the latest master is available to run it please run:

docker run --runtime nvidia -it -p 8888:8888 --mount type=bind,source="$(pwd)",target=/workspace agispof/quicknlp:latest

this will mount your current directory to /workspace and start a jupyter lab session in that directory

Usage Example

The main goal of quick-nlp is to provided the easy interface of the fast.ai library for seq2seq models.

For example Lets assume that we have a dataset_path with folders for training, validation files. Each file is a tsv file where each row is two sentences separated by a tab. For example a file inside the train folder can be a eng_to_fr.tsv file with the following first few lines:

Go. Va !
Run!    Cours !
Run!    Courez !
Wow!    Ça alors !
Fire!   Au feu !
Help!   À l'aide !
Jump.   Saute.
Stop!   Ça suffit !
Stop!   Stop !
Stop!   Arrête-toi !
Wait!   Attends !
Wait!   Attendez !
I see.  Je comprends.

loading the data from the directory is as simple as:

from fastai.plots import *
from torchtext.data import Field
from fastai.core import SGD_Momentum
from fastai.lm_rnn import seq2seq_reg
from quicknlp import SpacyTokenizer, print_batch, S2SModelData
INIT_TOKEN = "<sos>"
EOS_TOKEN = "<eos>"
DATAPATH = "dataset_path"
fields = [
    ("english", Field(init_token=INIT_TOKEN, eos_token=EOS_TOKEN, tokenize=SpacyTokenizer('en'), lower=True)),
    ("french", Field(init_token=INIT_TOKEN, eos_token=EOS_TOKEN, tokenize=SpacyTokenizer('fr'), lower=True))

]
batch_size = 64
data = S2SModelData.from_text_files(path=DATAPATH, fields=fields,
                                    train="train",
                                    validation="validation",
                                    source_names=["english", "french"],
                                    target_names=["french"],
                                    bs= batch_size
                                   )

Finally, to train a seq2seq model with the data we only need to do:

emb_size = 300
nh = 1024
nl = 3
learner = data.get_model(opt_fn=SGD_Momentum(0.7), emb_sz=emb_size,
                         nhid=nh,
                         nlayers=nl,
                         bidir=True,
                        )
clip = 0.3
learner.reg_fn = reg_fn
learner.clip = clip
learner.fit(2.0, wds=1e-6)