GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → wszlong → transformer

wszlong / transformer

Licence: other
A simple TensorFlow implementation of the Transformer

Programming Languages

python
139335 projects - #7 most used programming language
shell
77523 projects

Labels

translationtransformerattention-is-all-you-need

Projects that are alternatives of or similar to transformer

Dab
Data Augmentation by Backtranslation (DAB) ヽ( •_-)ᕗ
Stars: ✭ 294 (+1076%)
Mutual labels:  transformer, attention-is-all-you-need
Speech Transformer
A PyTorch implementation of Speech Transformer, an End-to-End ASR with Transformer network on Mandarin Chinese.
Stars: ✭ 565 (+2160%)
Mutual labels:  transformer, attention-is-all-you-need
Transformer
A TensorFlow Implementation of the Transformer: Attention Is All You Need
Stars: ✭ 3,646 (+14484%)
Mutual labels:  transformer, attention-is-all-you-need
speech-transformer
Transformer implementation speciaized in speech recognition tasks using Pytorch.
Stars: ✭ 40 (+60%)
Mutual labels:  transformer, attention-is-all-you-need
Machine Translation
Stars: ✭ 51 (+104%)
Mutual labels:  transformer, attention-is-all-you-need
transformer
Neutron: A pytorch based implementation of Transformer and its variants.
Stars: ✭ 60 (+140%)
Mutual labels:  transformer, attention-is-all-you-need
Nmt Keras
Neural Machine Translation with Keras
Stars: ✭ 501 (+1904%)
Mutual labels:  transformer, attention-is-all-you-need
Pytorch Original Transformer
My implementation of the original transformer model (Vaswani et al.). I've additionally included the playground.py file for visualizing otherwise seemingly hard concepts. Currently included IWSLT pretrained models.
Stars: ✭ 411 (+1544%)
Mutual labels:  transformer, attention-is-all-you-need
Sockeye
Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet
Stars: ✭ 990 (+3860%)
Mutual labels:  transformer, attention-is-all-you-need
Witwicky
Witwicky: An implementation of Transformer in PyTorch.
Stars: ✭ 21 (-16%)
Mutual labels:  transformer, attention-is-all-you-need
transformer
A PyTorch Implementation of "Attention Is All You Need"
Stars: ✭ 28 (+12%)
Mutual labels:  transformer, attention-is-all-you-need
Kospeech
Open-Source Toolkit for End-to-End Korean Automatic Speech Recognition.
Stars: ✭ 190 (+660%)
Mutual labels:  transformer, attention-is-all-you-need
kospeech
Open-Source Toolkit for End-to-End Korean Automatic Speech Recognition leveraging PyTorch and Hydra.
Stars: ✭ 456 (+1724%)
Mutual labels:  transformer, attention-is-all-you-need
attention-is-all-you-need-paper
Implementation of Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems. 2017.
Stars: ✭ 97 (+288%)
Mutual labels:  transformer, attention-is-all-you-need
Awesome Fast Attention
list of efficient attention modules
Stars: ✭ 627 (+2408%)
Mutual labels:  transformer, attention-is-all-you-need
Transformers without tears
Transformers without Tears: Improving the Normalization of Self-Attention
Stars: ✭ 80 (+220%)
Mutual labels:  transformer, attention-is-all-you-need
Pytorch Transformer
pytorch implementation of Attention is all you need
Stars: ✭ 199 (+696%)
Mutual labels:  transformer, attention-is-all-you-need
TokenLabeling
Pytorch implementation of "All Tokens Matter: Token Labeling for Training Better Vision Transformers"
Stars: ✭ 385 (+1440%)
Mutual labels:  transformer
text simplification
Text Simplification Model based on Encoder-Decoder (includes Transformer and Seq2Seq) model.
Stars: ✭ 66 (+164%)
Mutual labels:  transformer
sparql-transformer
A more handy way to use SPARQL data in your web app
Stars: ✭ 38 (+52%)
Mutual labels:  transformer
View All Similar Projects ➔

A simple Tensorflow implementation of the Transformer

This project is a simple implementation of Tensor2tensor (https://github.com/tensorflow/tensor2tensor) for machine translation.

Usage

  • Preprosessing. Prepare the parallel data (token, bpe, vocab, and so on), run ./datagen.sh to generate data.

  • Training. Modify the model params (transformer_params_big or transformer_params_base, basic params are set in models/common_hparms.py), and run ./train.sh.

  • inference. Run the command to translate source sentence: ./test.sh.

C++ Version Translator

Once you have trained the model, you can also use a C++/CUDA Transformer-translator to translate source language.

Contact

If you have questions, suggestions and bug reports, please email [email protected] or [email protected].

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].