Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/tokestermw/tensorflow-shakespeare
Neural machine translation between the writings of Shakespeare and modern English using TensorFlow
https://github.com/tokestermw/tensorflow-shakespeare
neural-machine-translation seq2seq shakespeare tensorflow
Last synced: 1 day ago
JSON representation
Neural machine translation between the writings of Shakespeare and modern English using TensorFlow
- Host: GitHub
- URL: https://github.com/tokestermw/tensorflow-shakespeare
- Owner: tokestermw
- License: apache-2.0
- Created: 2015-11-17T05:58:21.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2022-12-11T00:42:28.000Z (almost 2 years ago)
- Last Synced: 2024-10-27T23:59:29.436Z (12 days ago)
- Topics: neural-machine-translation, seq2seq, shakespeare, tensorflow
- Language: Python
- Size: 5.04 MB
- Stars: 250
- Watchers: 17
- Forks: 60
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-tensorflow - Neural machine translation between the writings of Shakespeare and modern English using TensorFlow - This performs a monolingual translation, going from modern English to Shakespeare and vice-versa. (Models/Projects)
- Awesome-TensorFlow-Chinese - Neural machine translation between the writings of Shakespeare and modern English using TensorFlow - This performs a monolingual translation, going from modern English to Shakespeare and vis-versa. (模型项目 / 微信群)
- awesome-tensorflow - Neural machine translation between the writings of Shakespeare and modern English using TensorFlow - This performs a monolingual translation, going from modern English to Shakespeare and vice-versa. (Models/Projects)
- fucking-awesome-tensorflow - Neural machine translation between the writings of Shakespeare and modern English using TensorFlow - This performs a monolingual translation, going from modern English to Shakespeare and vice-versa. (Models/Projects)
README
# Shakespeare translations using TensorFlow
This is an example of using the new Google's [TensorFlow](https://github.com/tensorflow/tensorflow) library on
monolingual translation going from modern English to Shakespeare based on research from
[Wei Xu](https://github.com/cocoxu/Shakespeare).
## 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
```
1. Grabs parallel data.
2. Gets train, dev split.
3. Builds vocabulary
4. 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.](http://aclweb.org/anthology/C/C12/C12-1177.pdf) (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