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https://github.com/kklemon/keras-loves-torchtext
Make Torchtext work with Keras.
https://github.com/kklemon/keras-loves-torchtext
data-science deep-learning keras natural-language-processing pytorch tensorflow torchtext
Last synced: 4 days ago
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Make Torchtext work with Keras.
- Host: GitHub
- URL: https://github.com/kklemon/keras-loves-torchtext
- Owner: kklemon
- License: bsd-3-clause
- Created: 2018-09-28T21:48:27.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-04-03T05:25:45.000Z (over 5 years ago)
- Last Synced: 2024-09-29T13:06:57.162Z (about 2 months ago)
- Topics: data-science, deep-learning, keras, natural-language-processing, pytorch, tensorflow, torchtext
- Language: Python
- Homepage:
- Size: 7.81 KB
- Stars: 18
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Keras ❤️ torchtext
=================
> Keras is love
Keras is life
Keras loves torchtext
[torchtext](https://github.com/pytorch/text) is a great library, putting a layer of abstraction over the usually very heavy data component in NLP projects, making the work with complex datasets a pace.
Sadly, as torchtext is based and built on PyTorch, using it with Keras is not directly possible.
_Keras ❤️ torchtext_ is to the rescue by providing lightweight wrappers for some Torchtext classes, making them easily work with Keras.
Installation
------------
```bash
pip install keras-loves-torchtext
```
Examples
--------
Wrap a `torchtext.data.Iterator` with `WrapIterator` and use it to train a Keras model:
```python
from torchtext.data import Dataset, Field, Iterator
from kltt import WrapIterator
...
fields = [('text', Field()),
('label', Field(sequential=False))]
dataset = Dataset(examples, fields)
iterator = Iterator(dataset, batch_size=32)
# Keras ❤️ torchtext comes to play
data_gen = WrapIterator(iterator, x_fields=['text'], y_fields=['label'])
model.fit_generator(iter(data_gen), steps_per_epoch=len(data_gen))
```
Easily wrap multiple iterators at once:
```python
from torchtext.data import Dataset, Field, Iterator
from kltt import WrapIterator
...
fields = [('text', Field()),
('label', Field(sequential=False))]
dataset = Dataset(examples, fields)
splits = dataset.split()
iterators = Iterator.splits(splits, batch_size=32)
train, valid, test = WrapIterator.wraps(iterators, x_fields=['text'], y_fields=['label'])
model.fit_generator(iter(train), steps_per_epoch=len(train),
validation_data=iter(valid), validation_steps=len(valid))
loss, acc = model.evaluate_generator(iter(test), steps=len(test))
```
Further and full working examples can be found in the `examples` folder.
Documentation
-------------
Todo
See `examples` and inline documentation for now.