Metadata-Version: 1.1
Name: LightNER
Version: 0.2.2
Summary: A Toolkit for Pre-trained Sequence Labeling Models Inference
Home-page: https://github.com/LiyuanLucasLiu/LightNER
Author: Lucas Liu
Author-email: llychinalz@gmail.com
License: Apache License 2.0
Description-Content-Type: UNKNOWN
Description: # LightNER
        
        [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
        [![PyPI version](https://badge.fury.io/py/LightNER.svg)](https://badge.fury.io/py/LightNER)
        [![Downloads](https://pepy.tech/badge/lightner)](https://pepy.tech/project/lightner)
        <!-- [![Documentation Status](https://readthedocs.org/projects/tensorboard-wrapper/badge/?version=latest)](http://tensorboard-wrapper.readthedocs.io/en/latest/?badge=latest) -->
        
        **Check Our New NER Toolkit🚀🚀🚀**
        - **Inference**:
          - **[LightNER](https://github.com/LiyuanLucasLiu/LightNER)**: inference w. models pre-trained / trained w. *any* following tools, *efficiently*. 
        - **Training**:
          - **[LD-Net](https://github.com/LiyuanLucasLiu/LD-Net)**: train NER models w. efficient contextualized representations.
          - **[VanillaNER](https://github.com/LiyuanLucasLiu/Vanilla_NER)**: train vanilla NER models w. pre-trained embedding.
        - **Distant Training**:
          - **[AutoNER](https://shangjingbo1226.github.io/AutoNER/)**: train NER models w.o. line-by-line annotations and get competitive performance.
        
        --------------------------------
        
        This package supports to conduct inference with models pre-trained by:
        - [Vanilla_NER](https://github.com/LiyuanLucasLiu/Vanilla_NER): vanilla sequence labeling models.
        - [LD-Net](https://github.com/LiyuanLucasLiu/LD-Net): sequence labeling models w. efficient contextualized representation.
        - [AutoNER](https://github.com/shangjingbo1226/AutoNER): distant supervised named entity recognition models (*no line-by-line annotations for training*).
        
        We are in an early-release beta. Expect some adventures and rough edges.
        
        ## Quick Links
        
        - [Installation](#installation)
        - [Usage](#usage)
        
        ## Installation
        
        To install via pypi:
        ```
        pip install lightner
        ```
        
        To build from source:
        ```
        pip install git+https://github.com/LiyuanLucasLiu/LightNER
        ```
        or
        ```
        git clone https://github.com/LiyuanLucasLiu/LightNER.git
        cd LightNER
        python setup.py install
        ```
        
        ## Usage
        
        ### Pre-trained Models
        
        |               | Model             | Task            | Performance            |
        | ------------- |-------------      | -------------   | -------------          |
        | LD-Net        | [pner1.th](http://dmserv4.cs.illinois.edu/pner1.th) | NER for (PER, LOC, ORG & MISC) | F1 92.21 |
        | LD-Net        | [pnp0.th](http://dmserv4.cs.illinois.edu/pnp0.th)   | Chunking                       | F1 95.79 |  
        | Vanilla_NER   |                                                               | NER for (PER, LOC, ORG & MISC) | |
        | Vanilla_NER   |                                                               | Chunking                       | |
        | AutoNER       |                                                               | Distant NER trained w.o. line-by-line annotations | |
        
        
        ### Decode API
        
        The decode api can be called in the following way:
        ```
        from lightner import decoder_wrapper
        model = decoder_wrapper()
        model.decode(["Ronaldo", "won", "'t", "score", "more", "than", "30", "goals", "for", "Juve", "."])
        ```
        
        The ```decode()``` method also can conduct decoding at document level (takes list of list of ```str``` as input) or corpus level (takes list of list of list of ```str``` as input).
        
        The ```decoder_wrapper``` method can be customized by choosing a different pre-trained model or passing an additional ```configs``` file as:
        ```
        model = decoder_wrapper(URL_OR_PATH_TO_CHECKPOINT, configs)
        ```
        And you can access the config options by:
        ```
        lightner decode -h
        ```
        
        ### Console
        
        After installing and downloading the pre-trained mdoels, conduct the inference by 
        ```
        lightner decode -m MODEL_FILE -i INPUT_FILE -o OUTPUT_FILE
        ```
        
        You can find more options by:
        ```
        lightner decode -h
        ```
        
        The current accepted paper format is as below (tokenized by line break and ```-DOCSTART-``` is optional):
        ```
        -DOCSTART-
        
        Ronaldo
        won
        't
        score
        more
        30
        goals
        for
        Juve
        .
        ```
        
        The output would be:
        ```
        <PER> Ronaldo </PER> won 't score more than 30 goals for <ORG> Juve </ORG> . 
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
