/ Natural Language Processing

A Visual Analysis Toolkit for Text Generation Tasks

A Visual Analysis Toolkit for Text Generation Tasks

vizseq

A Visual Analysis Toolkit for Text Generation Tasks.

Task Coverage

Source Example Tasks
Text Machine translation, text summarization, dialog generation, grammatical error correction, open-domain question answering
Image Image captioning, image question answering, optical character recognition
Audio Speech recognition, speech translation
Video Video description
Multimodal Multimodal machine translation

Metric Coverage

Accelerated with multi-processing/multi-threading.

Type Metrics
N-gram-based BLEU (Papineni et al., 2002), NIST (Doddington, 2002), METEOR (Banerjee et al., 2005), TER (Snover et al., 2006), RIBES (Isozaki et al., 2010), chrF (Popović et al., 2015), GLEU (Wu et al., 2016), ROUGE (Lin, 2004), CIDEr (Vedantam et al., 2015), WER
Embedding-based LASER (Artetxe and Schwenk, 2018), BERTScore (Zhang et al., 2019)

Installation

VizSeq requires Python 3.6+ and currently runs on Unix/Linux and macOS/OS X. It will support Windows as well in the future.

You can install VizSeq from PyPI repository:

$ pip install vizseq

Or install it from source:

$ git clone https://github.com/facebookresearch/vizseq
$ cd vizseq
$ pip install -e .

Getting Started

[Full documentation]

Jupyter Notebook Examples

Web App Example

Download example data:

$ git clone https://github.com/facebookresearch/vizseq
$ cd vizseq
$ bash get_example_data.sh

Launch the web server:

$ python -m vizseq.server --port 9001 --data-root ./examples/data

And then, navigate to the following URL in your web browser:

http://localhost:9001

GitHub