Sequence-to-Sequence learning using PyTorch

Last update: Nov 17, 2022

Overview

Seq2Seq in PyTorch

This is a complete suite for training sequence-to-sequence models in PyTorch. It consists of several models and code to both train and infer using them.

Using this code you can train:

Neural-machine-translation (NMT) models
Language models
Image to caption generation
Skip-thought sentence representations
And more...

Installation

git clone --recursive https://github.com/eladhoffer/seq2seq.pytorch
cd seq2seq.pytorch; python setup.py develop

Models

Models currently available:

Simple Seq2Seq recurrent model
Recurrent Seq2Seq with attentional decoder
Google neural machine translation (GNMT) recurrent model
Transformer - attention-only model from "Attention Is All You Need"

Datasets

Datasets currently available:

WMT16
WMT17
OpenSubtitles 2016
COCO image captions
Conceptual captions

All datasets can be tokenized using 3 available segmentation methods:

Character based segmentation
Word based segmentation
Byte-pair-encoding (BPE) as suggested by bpe with selectable number of tokens.

After choosing a tokenization method, a vocabulary will be generated and saved for future inference.

Training methods

The models can be trained using several methods:

Basic Seq2Seq - given encoded sequence, generate (decode) output sequence. Training is done with teacher-forcing.
Multi Seq2Seq - where several tasks (such as multiple languages) are trained simultaneously by using the data sequences as both input to the encoder and output for decoder.
Image2Seq - used to train image to caption generators.

Usage

Example training scripts are available in scripts folder. Inference examples are available in examples folder.

example for training a transformer on WMT16 according to original paper regime:

DATASET=${1:-"WMT16_de_en"}
DATASET_DIR=${2:-"./data/wmt16_de_en"}
OUTPUT_DIR=${3:-"./results"}

WARMUP="4000"
LR0="512**(-0.5)"

python main.py \
  --save transformer \
  --dataset ${DATASET} \
  --dataset-dir ${DATASET_DIR} \
  --results-dir ${OUTPUT_DIR} \
  --model Transformer \
  --model-config "{'num_layers': 6, 'hidden_size': 512, 'num_heads': 8, 'inner_linear': 2048}" \
  --data-config "{'moses_pretok': True, 'tokenization':'bpe', 'num_symbols':32000, 'shared_vocab':True}" \
  --b 128 \
  --max-length 100 \
  --device-ids 0 \
  --label-smoothing 0.1 \
  --trainer Seq2SeqTrainer \
  --optimization-config "[{'step_lambda':
                          \"lambda t: { \
                              'optimizer': 'Adam', \
                              'lr': ${LR0} * min(t ** -0.5, t * ${WARMUP} ** -1.5), \
                              'betas': (0.9, 0.98), 'eps':1e-9}\"
                          }]"

example for training attentional LSTM based model with 3 layers in both encoder and decoder:

python main.py \
  --save de_en_wmt17 \
  --dataset ${DATASET} \
  --dataset-dir ${DATASET_DIR} \
  --results-dir ${OUTPUT_DIR} \
  --model RecurrentAttentionSeq2Seq \
  --model-config "{'hidden_size': 512, 'dropout': 0.2, \
                   'tie_embedding': True, 'transfer_hidden': False, \
                   'encoder': {'num_layers': 3, 'bidirectional': True, 'num_bidirectional': 1, 'context_transform': 512}, \
                   'decoder': {'num_layers': 3, 'concat_attention': True,\
                               'attention': {'mode': 'dot_prod', 'dropout': 0, 'output_transform': True, 'output_nonlinearity': 'relu'}}}" \
  --data-config "{'moses_pretok': True, 'tokenization':'bpe', 'num_symbols':32000, 'shared_vocab':True}" \
  --b 128 \
  --max-length 80 \
  --device-ids 0 \
  --trainer Seq2SeqTrainer \
  --optimization-config "[{'epoch': 0, 'optimizer': 'Adam', 'lr': 1e-3},
                          {'epoch': 6, 'lr': 5e-4},
                          {'epoch': 8, 'lr':1e-4},
                          {'epoch': 10, 'lr': 5e-5},
                          {'epoch': 12, 'lr': 1e-5}]" \

Sequence-to-Sequence learning using PyTorch

Related tags

Overview

Seq2Seq in PyTorch

Installation

Models

Datasets

Training methods

Usage

Owner

Elad Hoffer

Training code for Korean multi-class sentiment analysis

(ACL-IJCNLP 2021) Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models.

Samantha, A covid-19 information bot which will provide basic information about this pandemic in form of conversation.

多语言降噪预训练模型MBart的中文生成任务

Journey is a NLP-Powered Developer assistant

A Python 3.6+ package to run .many files, where many programs written in many languages may exist in one file.

Trained T5 and T5-large model for creating keywords from text

InferSent sentence embeddings

TLA - Twitter Linguistic Analysis

Non-Autoregressive Predictive Coding

NLP Overview

A Python/Pytorch app for easily synthesising human voices

Random-Word-Generator - Generates meaningful words from dictionary with given no. of letters and words.

Code for the Python code smells video on the ArjanCodes channel.

Code for paper "Which Training Methods for GANs do actually Converge? (ICML 2018)"

A pytorch implementation of the ACL2019 paper "Simple and Effective Text Matching with Richer Alignment Features".

Learn meanings behind words is a key element in NLP. This project concentrates on the disambiguation of preposition senses. Therefore, we train a bert-transformer model and surpass the state-of-the-art.

Transformer training code for sequential tasks

Multi Task Vision and Language

The ability of computer software to identify words and phrases in spoken language and convert them to human-readable text