Metadata-Version: 2.1
Name: vizseq
Version: 0.1.12
Summary: Visual Analysis Toolkit for Text Generation Tasks
Home-page: https://github.com/facebookresearch/vizseq
License: MIT
Description: [![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)
        
        # <img src="logo.png" alt="VizSeq" width="160">
        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)
        
        <p align="center">
        <img src="overview.png" alt="VizSeq Overview" width="480">
        <img src="teaser.gif" alt="VizSeq Teaser" width="480">
        </p>
        
        ### 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 ([changhan@fb.com](mailto:changhan@fb.com)), Jiatao Gu ([jgu@fb.com](mailto:jgu@fb.com))
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
