Visual Analysis Toolkit for Text Generation Tasks
Project description
VizSeq is a Python toolkit for visual analysis on text generation tasks like machine translation, summarization, image captioning, speech translation and video description. It takes multi-modal sources, text references as well as text predictions as inputs, and analyzes them visually in Jupyter Notebook or a built-in Web App (the former has Fairseq integration). VizSeq also provides a collection of multi-process scorers as a normal Python package.
[Paper] [Documentation] [Blog]
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) |
Getting Started
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 .
Documentation
Jupyter Notebook Examples
Fairseq integration
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
License
VizSeq is licensed under MIT. See the LICENSE file for details.
Citation
Please cite as
@inproceedings{wang2019vizseq,
title = {VizSeq: A Visual Analysis Toolkit for Text Generation Tasks},
author = {Changhan Wang, Anirudh Jain, Danlu Chen, Jiatao Gu},
booktitle = {In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
year = {2019},
}
Contact
Changhan Wang (changhan@fb.com), Jiatao Gu (jgu@fb.com)
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