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توضیحات

FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction

ویژگی	مقدار
سیستم عامل	-
نام فایل	famie-0.3.0
نام	famie
نسخه کتابخانه	0.3.0
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	NLP Group at the University of Oregon
ایمیل نویسنده	thien@cs.uoregon.edu
آدرس صفحه اصلی	https://github.com/nlp-uoregon/famie
آدرس اینترنتی	https://pypi.org/project/famie/
مجوز	GPL-3.0 License

<h2 align="center">FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction</h2> <div align="center"> <a href="https://github.com/nlp-uoregon/famie/blob/master/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/nlp-uoregon/famie.svg?color=blue"> </a> <a href='https://famie.readthedocs.io/en/latest/?badge=latest'> <img src='https://readthedocs.org/projects/famie/badge/?version=latest' alt='Documentation Status' /> </a> <a href="http://nlp.uoregon.edu:9000/"> <img alt="Demo Website" src="https://img.shields.io/website/http/famie.readthedocs.io/en/latest/index.html.svg?down_color=red&down_message=offline&up_message=online"> </a> <a href="https://pypi.org/project/famie/"> <img alt="PyPI Version" src="https://img.shields.io/pypi/v/famie?color=blue"> </a> <a href="https://pypi.org/project/famie/"> <img alt="Python Versions" src="https://img.shields.io/pypi/pyversions/famie?colorB=blue"> </a> </div> ### Introduction FAMIE is a comprehensive and efficient **active learning** (AL) toolkit for **multilingual information extraction** (IE). FAMIE is designed to address a fundamental problem in existing AL frameworks where annotators need to wait for a long time between annotation batches due to the time-consuming nature of model training and data selection at each AL iteration. With a novel [proxy AL mechanism](https://famie.readthedocs.io/en/latest/howitworks.html) and the integration of our SOTA multilingual toolkit [Trankit](https://github.com/nlp-uoregon/trankit), **it takes FAMIE only a few hours to provide users with a labeled dataset and a ready-to-use model for different IE tasks over [100 languages](https://trankit.readthedocs.io/en/latest/pkgnames.html#trainable-languages)**. If you use FAMIE in your research or products, please cite our following paper: ```bibtex @inproceedings{vannguyen2022famie, title={FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction}, author={Nguyen, Minh Van and Ngo, Nghia Trung and Min, Bonan and Nguyen, Thien Huu}, booktitle={Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations}, year={2022} } ``` FAMIE's technical paper: https://arxiv.org/pdf/2202.08316.pdf FAMIE's documentation page: https://famie.readthedocs.io FAMIE's demo website: http://nlp.uoregon.edu:9000/ ### Installation FAMIE can be easily installed via one of the following methods: #### Using pip ``` pip install famie ``` The command would install FAMIE and all dependent packages automatically. #### From source ``` git clone https://github.com/nlp-uoregon/famie.git cd famie pip install -e . ``` This would first clone our github repo and install FAMIE. ### Usage FAMIE currently supports Named Entity Recognition, Event Detection, and Event Argument Extraction for over [100 languages](https://trankit.readthedocs.io/en/latest/pkgnames.html#trainable-languages). Using FAMIE includes three following steps: - Start an annotation session. - Annotate data for a target task. - Access the labeled data and a ready-to-use model returned by FAMIE. #### Starting an annotation session <ins>Running on local machines</ins> To start an annotation session, please use the following command: ```python famie start ``` This will run a server on users' local machines (no data or models will leave users' local machines), users can access FAMIE's web interface via the URL: http://127.0.0.1:9000/ <ins>Running on remote servers</ins> To use FAMIE on a remote server for a local machine, users can run famie on the corresponding server and ssh-forward famie's port (9000) from their local machine: ```bash # On remote famie start # On local ssh -NL <local-port>:localhost:<famie-port> <remote-username>@<remote-address> # Open localhost:9000 on local to access FAMIE's web interface. ``` <ins>Running on Google Colab</ins> To use FAMIE on google colab, use [colab-ssh](https://stackoverflow.com/a/61682149) to create an ssh connect to the Colab VM. Then follow the same remote-local process above to run FAMIE on the colab notebook through your local machine. ```bash # On Colab Notebook ## Install colab_ssh and run ngrok to get ssh address and port !pip install colab_ssh --upgrade from colab_ssh import launch_ssh launch_ssh('YOUR_NGROK_AUTH_TOKEN', 'SOME_PASSWORD') # return a ssh-address and ssh-port ## Run FAMIE famie start --port <famie-port> # On local ssh -NL <local-port>:localhost:<famie-port> root@<ssh-address> -p <ssh-port> # Open localhost:<local-port> on local to access FAMIE's web interface. ``` As FAMIE is an AL framework, it provides different data selection algorithms that recommend users the most beneficial examples to label at each annotation iteration. This is done via passing an optional argument `--selection [mnlp|badge|bertkm|random]`. #### Annotating data After initiating a new project and uploading an unlabeled dataset file with an entity types file (in text format), annotators can start the annotation process. Given one annotation batch in an iteration, annotators label one sentence at a time, annotating the word spans for each label by first choosing the label and then highlighting the appropriate spans. <p align="middle">   <img src="pics/3_save_next.png" width="400" />  </p> After finishing each iteration, FAMIE then allows users to download the trained models and annotated data by clicking on the buttons **DOWNLOAD LABELED DATA** and **DOWNLOAD TRAINED MODEL**. <p align="middle"> <img src="pics/download.png" width="400" /> </p> #### Accessing labeled data and trained models FAMIE also provides a simple and intuitive code interface for interacting with the resulting labeled dataset and trained main models after the AL processes. ```python import famie # access a project via its name p = famie.get_project('named-entity-recognition') # access the project's labeled data data = p.get_labeled_data() # a Python dictionary # export the project's labeled data to a file p.export_labeled_data('data.json') # export the project's trained model to a file p.export_trained_model('model.ckpt') # access the project's trained model model = p.get_trained_model() # access a trained model from file model = famie.load_model_from_file('model.ckpt') # use the trained model to make predictions model.predict('Oregon is a beautiful state!') # ['B-Location', 'O', 'O', 'O', 'O'] ```

نیازمندی

مقدار	نام
>=1.1.1	Flask
==3.0.10	flask-cors
==1.1	html5lib
>=1.19.2	numpy
>=2.23	requests
==2.1.0	adapter-transformers
==1.1.6	langid
==1.1.0	trankit
<0.22	scikit-learn
>=0.22	scikit-learn

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl famie-0.3.0:

pip install famie-0.3.0.whl

نصب پکیج tar.gz famie-0.3.0:

pip install famie-0.3.0.tar.gz