Neural machine translation between the writings of Shakespeare and modern English using TensorFlow

Last update: Dec 28, 2022

Overview

Shakespeare translations using TensorFlow

This is an example of using the new Google's TensorFlow library on monolingual translation going from modern English to Shakespeare based on research from Wei Xu.

Prepare

First download the TensorFlow library depending on your platform:

pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl # for mac
pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl # for ubuntu

Grabs parallel data.
Gets train, dev split.
Builds vocabulary
Converts parallel data into ids

From the root directory:

python -m tensorshake.get_data
python -m tensorshake.prepare_corpus

Delete /cache to start anew.

Train

Use the example BASH script to train the model. This saves the check points in the --train_dir directory. If you run it again, the training process continues from the check point. To restart with fresh parameters, simply delete/rename the check points.

./run.sh

Results

Benchmarks from original paper. (Shakespeare -> Modern English)

Input	Output
i will bite thee by the ear for that jest .	i ’ ll bite you by the ear for that joke .
what further woe conspires against mine age ?	what ’ s true despair conspires against my old age ?
how doth my lady ?	how is my lady ?
hast thou slain tybalt ?	have you killed tybalt ?
an i might live to see thee married once , i have my wish .	if i could live to see you married, i ’ ve my wish .
benvolio , who began this bloody fray ?	benvolio , who started this bloody fight itself ?
what is your will ?	what do you want ?
call her forth to me .	bring her out to me .

Cherrypicked examples from this repo (Modern English -> Shakespeare)

Input	Output
but you’re not listening to me.	but you do not hear me .
Gregory, on my word, we will not be humiliated, like carrying coal.	regory , we 'll not carry coals .
but he got the promotion.	he is the friend .
i can hit quickly, if i'm motivated.	i strike , i am moved .
Did you just give us the finger, sir?	have you leave the thumb , sir ?
You don’t know what you’re doing!	you do not what you know you .
have you killed Tybalt?	hast thou slain tybalt ?
Why, Romeo, are you crazy?	why , art thou mad , mad ?

Pre-Trained Models

Here is a link for an example model: https://s3-us-west-2.amazonaws.com/foxtype-nlp/tensorshake/model_cache.zip

Possible improvements

word embeddings
beam search
language model reranking

Neural machine translation between the writings of Shakespeare and modern English using TensorFlow

Related tags

Overview

Shakespeare translations using TensorFlow

Prepare

Train

Results

Pre-Trained Models

Possible improvements

Owner

Motoki Wu

Nvdiffrast - Modular Primitives for High-Performance Differentiable Rendering

ManiSkill-Learn is a framework for training agents on SAPIEN Open-Source Manipulation Skill Challenge (ManiSkill Challenge), a large-scale learning-from-demonstrations benchmark for object manipulation.

Code for our paper "Interactive Analysis of CNN Robustness"

Meta-Learning Sparse Implicit Neural Representations (NeurIPS 2021)

moving object detection for satellite videos.

Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks.

tensorrt int8 量化yolov5 4.0 onnx模型

The code from the paper Character Transformations for Non-Autoregressive GEC Tagging

For AILAB: Cross Lingual Retrieval on Yelp Search Engine

PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML)

Official Code for AdvRush: Searching for Adversarially Robust Neural Architectures (ICCV '21)

PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

The Rich Get Richer: Disparate Impact of Semi-Supervised Learning

A mini library for Policy Gradients with Parameter-based Exploration, with reference implementation of the ClipUp optimizer from NNAISENSE.

Stratified Transformer for 3D Point Cloud Segmentation (CVPR 2022)

Official Pytorch implementation of Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations

Code for the paper "M2m: Imbalanced Classification via Major-to-minor Translation" (CVPR 2020)

Histology images query (unsupervised)

This is code of book "Learn Deep Learning with PyTorch"

PyTorch implementation for STIN