Change log#
v2.3 - Jan 25, 2024#
introduced DistributedDataParallel
implemented language tags, see torchhub.ipynb
released a iwslt14 de-en-fr multilingual model (trained using DDP)
special symbols definition refactoring
configuration refactoring
autocast refactoring
enabled activation function selection
bugfixes
upgrade to python 3.11, torch 2.1.2
documentation refactoring
v2.2 - Jan 15, 2023#
compatibility with torch 2.0 tested
torchhub introduced
bugfixes, minor refactoring
v2.1 - Sep 18, 2022#
upgrade to python 3.10, torch 1.12
replace Automated Mixed Precision from NVIDA’s amp to Pytorch’s amp package
replace discord.py with pycord in the Discord Bot demo
data iterator refactoring
add wmt14 ende / deen benchmark trained on v2 from scratch
add tokenizer tutorial
minor bugfixes
v2.0 - Jun 2, 2022#
Breaking change!
upgrade to python 3.9, torch 1.11
torchtext.legacy
dependencies are completely replaced bytorch.utils.data
tokenizers.py: handles tokenization internally (also supports bpe-dropout!)
datasets.py: loads data from plaintext, tsv, and huggingface’s datasets
build_vocab.py: trains subwords, creates joint vocab
enhancement in decoding - scoring with hypotheses or references - repetition penalty, ngram blocker - attention plots for transformers
yapf, isort, flake8 introduced
bugfixes, minor refactoring
Warning
The models trained with Joey NMT v1.x can be decoded with Joey NMT v2.0. But there is no guarantee that you can reproduce the same score as before.
v1.5 - Jan 18, 2022#
requirements update (Six >= 1.12)
v1.4 - Jan 18, 2022#
upgrade to sacrebleu 2.0, python 3.7, torch 1.8
bugfixes
v1.3 - Apr 14, 2021#
upgrade to torchtext 0.9 (torchtext -> torchtext.legacy)
n-best decoding
demo colab notebook
v1.0 - Oct 31, 2020#
Multi-GPU support
fp16 (half precision) support
v0.9 - Jul 28, 2019#
pre-release