Skip to content
master
Switch branches/tags
Code

Latest commit

Summary:
1. add support for using crf output layer
2. there are some bugs in tensorizer: no_label is not added to label vocab and labels_pad_idx was using pad from token vocab (should be label vocab)

Reviewed By: debowin

Differential Revision: D28290128

fbshipit-source-id: eeeed561ae2d03194f83a22680d5a1ba3cf43833
1a25a3c

Git stats

Files

Permalink
Failed to load latest commit information.

Overview

CircleCI

PyText is a deep-learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces and abstractions for model components, and by using PyTorch’s capabilities of exporting models for inference via the optimized Caffe2 execution engine. We are using PyText in Facebook to iterate quickly on new modeling ideas and then seamlessly ship them at scale.

Core PyText features:

Installing PyText

PyText requires Python 3.6.1 or above.

To get started on a Cloud VM, check out our guide.

Get the source code:

  $ git clone https://github.com/facebookresearch/pytext
  $ cd pytext

Create a virtualenv and install PyText:

  $ python3 -m venv pytext_venv
  $ source pytext_venv/bin/activate
  (pytext_venv) $ pip install pytext-nlp

Detailed instructions and more installation options can be found in our Documentation. If you encounter issues with missing dependencies during installation, please refer to OS Dependencies.

Train your first text classifier

For this first example, we'll train a CNN-based text-classifier that classifies text utterances, using the examples in tests/data/train_data_tiny.tsv. The data and configs files can be obtained either by cloning the repository or by downloading the files manually from GitHub.

  (pytext_venv) $ pytext train < demo/configs/docnn.json

By default, the model is created in /tmp/model.pt

Now you can export your model as a caffe2 net:

  (pytext_venv) $ pytext export < demo/configs/docnn.json

You can use the exported caffe2 model to predict the class of raw utterances like this:

  (pytext_venv) $ pytext --config-file demo/configs/docnn.json predict <<< '{"text": "create an alarm for 1:30 pm"}'

More examples and tutorials can be found in Full Documentation.

Join the community

License

PyText is BSD-licensed, as found in the LICENSE file.