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https://github.com/facebookresearch/vizseq

An Analysis Toolkit for Natural Language Generation (Translation, Captioning, Summarization, etc.)
https://github.com/facebookresearch/vizseq

Last synced: 2 days ago
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An Analysis Toolkit for Natural Language Generation (Translation, Captioning, Summarization, etc.)

Host: GitHub
URL: https://github.com/facebookresearch/vizseq
Owner: facebookresearch
License: mit
Created: 2019-08-26T13:19:38.000Z (about 5 years ago)
Default Branch: main
Last Pushed: 2024-10-29T16:15:42.000Z (10 days ago)
Last Synced: 2024-11-04T17:47:40.334Z (4 days ago)
Language: Python
Homepage: https://arxiv.org/abs/1909.05424
Size: 19 MB
Stars: 442
Watchers: 16
Forks: 59
Open Issues: 8
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

awesome-python-machine-learning-resources - GitHub - 40% open · ⏱️ 20.07.2022): (文本数据和NLP)
awesome-nlg - VizSeq - A Visual Analysis Toolkit for Text Generation Tasks. (Evaluation)

README

        [![PyPI](https://img.shields.io/pypi/v/vizseq?style=flat-square)](https://pypi.org/project/vizseq/)

[![CircleCI](https://img.shields.io/circleci/build/github/facebookresearch/vizseq?style=flat-square)](https://circleci.com/gh/facebookresearch/vizseq)

![PyPI - License](https://img.shields.io/pypi/l/vizseq?style=flat-square)

![PyPI - Python Version](https://img.shields.io/pypi/pyversions/vizseq?style=flat-square)

# 

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](https://facebookresearch.github.io/vizseq/docs/getting_started/ipynb_example) or a

built-in [Web App](https://facebookresearch.github.io/vizseq/docs/getting_started/web_app_example)

(the former has [Fairseq integration](https://facebookresearch.github.io/vizseq/docs/getting_started/fairseq_example)).

VizSeq also provides a collection of [multi-process scorers](https://facebookresearch.github.io/vizseq/docs/features/metrics) as

a normal Python package.

[[Paper]](https://arxiv.org/pdf/1909.05424.pdf)

[[Documentation]](https://facebookresearch.github.io/vizseq)

[[Blog]](https://ai.facebook.com/blog/vizseq-a-visual-analysis-toolkit-for-accelerating-text-generation-research)









### 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](https://www.aclweb.org/anthology/P02-1040)), NIST ([Doddington, 2002](http://www.mt-archive.info/HLT-2002-Doddington.pdf)), METEOR ([Banerjee et al., 2005](https://www.aclweb.org/anthology/W05-0909)), TER ([Snover et al., 2006](http://mt-archive.info/AMTA-2006-Snover.pdf)), RIBES ([Isozaki et al., 2010](https://www.aclweb.org/anthology/D10-1092)), chrF ([Popović et al., 2015](https://www.aclweb.org/anthology/W15-3049)), GLEU ([Wu et al., 2016](https://arxiv.org/pdf/1609.08144.pdf)), ROUGE ([Lin, 2004](https://www.aclweb.org/anthology/W04-1013)), CIDEr ([Vedantam et al., 2015](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Vedantam_CIDEr_Consensus-Based_Image_2015_CVPR_paper.pdf)), WER |

| Embedding-based | LASER ([Artetxe and Schwenk, 2018](https://arxiv.org/pdf/1812.10464.pdf)), BERTScore ([Zhang et al., 2019](https://arxiv.org/pdf/1904.09675.pdf)) |

## 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:

```bash

$ pip install vizseq

```

Or install it from source:

```bash

$ git clone https://github.com/facebookresearch/vizseq

$ cd vizseq

$ pip install -e .

```

### [Documentation](https://facebookresearch.github.io/vizseq)

### Jupyter Notebook Examples

- [Basic example](https://facebookresearch.github.io/vizseq/docs/getting_started/ipynb_example)

- [Multimodal Machine Translation](examples/multimodal_machine_translation.ipynb)

- [Multilingual Machine Translation](examples/multilingual_machine_translation.ipynb)

- [Speech Translation](examples/speech_translation.ipynb)

### [Fairseq integration](https://facebookresearch.github.io/vizseq/docs/getting_started/fairseq_example)

### [Web App Example](https://facebookresearch.github.io/vizseq/docs/getting_started/web_app_example)

Download example data:

```bash

$ git clone https://github.com/facebookresearch/vizseq

$ cd vizseq

$ bash get_example_data.sh

```

Launch the web server:

```bash

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

```

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

```text

http://localhost:9001

```

## License

VizSeq is licensed under [MIT](https://github.com/facebookresearch/vizseq/blob/master/LICENSE). See the [LICENSE](https://github.com/facebookresearch/vizseq/blob/master/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 ([[email protected]](mailto:[email protected])), Jiatao Gu ([[email protected]](mailto:[email protected]))