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
Jupyter Notebook Examples
- Multimodal Machine Translation
- Multilingual Machine Translation
- Speech Translation
- 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