معرفی شرکت ها
TUPA-1.4.2
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Transition-based UCCA Parser
| ویژگی | مقدار |
|---|---|
| سیستم عامل | POSIX :: Linux |
| نام فایل | TUPA-1.4.2 |
| نام | TUPA |
| نسخه کتابخانه | 1.4.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Daniel Hershcovich |
| ایمیل نویسنده | danielh@cs.huji.ac.il |
| آدرس صفحه اصلی | https://github.com/huji-nlp/tupa |
| آدرس اینترنتی | https://pypi.org/project/TUPA/ |
| مجوز | - |
Transition-based UCCA Parser
============================
TUPA is a transition-based parser for `Universal Conceptual Cognitive
Annotation (UCCA) <http://github.com/huji-nlp/ucca>`__.
Requirements
~~~~~~~~~~~~
- Python 3.6
Install
~~~~~~~
Create a Python virtual environment. For example, on Linux:
::
virtualenv --python=/usr/bin/python3 venv
. venv/bin/activate # on bash
source venv/bin/activate.csh # on csh
Install the latest release:
::
pip install tupa
Alternatively, install the latest code from GitHub (may be unstable):
::
git clone https://github.com/danielhers/tupa
cd tupa
pip install .
Train the parser
----------------
Having a directory with UCCA passage files (for example, `the English
Wiki
corpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_English-Wiki>`__),
run:
::
python -m tupa -t <train_dir> -d <dev_dir> -c <model_type> -m <model_filename>
The possible model types are ``sparse``, ``mlp``, and ``bilstm``.
Parse a text file
~~~~~~~~~~~~~~~~~
Run the parser on a text file (here named ``example.txt``) using a
trained model:
::
python -m tupa example.txt -m <model_filename>
An ``xml`` file will be created per passage (separate by blank lines in
the text file).
Pre-trained models
~~~~~~~~~~~~~~~~~~
To download and extract `a model pre-trained on the Wiki
corpus <https://github.com/huji-nlp/tupa/releases/download/v1.3.10/ucca-bilstm-1.3.10.tar.gz>`__,
run:
::
curl -LO https://github.com/huji-nlp/tupa/releases/download/v1.3.10/ucca-bilstm-1.3.10.tar.gz
tar xvzf ucca-bilstm-1.3.10.tar.gz
Run the parser using the model:
::
python -m tupa example.txt -m models/ucca-bilstm
Other languages
~~~~~~~~~~~~~~~
To get `a
model <https://github.com/huji-nlp/tupa/releases/download/v1.3.10/ucca-bilstm-1.3.10-fr.tar.gz>`__
pre-trained on the `French *20K Leagues*
corpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_French-20K>`__
or `a
model <https://github.com/huji-nlp/tupa/releases/download/v1.3.10/ucca-bilstm-1.3.10-de.tar.gz>`__
pre-trained on the `German *20K Leagues*
corpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_German-20K>`__,
run:
::
curl -LO https://github.com/huji-nlp/tupa/releases/download/v1.3.10/ucca-bilstm-1.3.10-fr.tar.gz
tar xvzf ucca-bilstm-1.3.10-fr.tar.gz
curl -LO https://github.com/huji-nlp/tupa/releases/download/v1.3.10/ucca-bilstm-1.3.10-de.tar.gz
tar xvzf ucca-bilstm-1.3.10-de.tar.gz
Run the parser on a French/German text file (separate passages by blank
lines):
::
python -m tupa exemple.txt -m models/ucca-bilstm-fr --lang fr
python -m tupa beispiel.txt -m models/ucca-bilstm-de --lang de
Using BERT
----------
BERT can be used instead of standard word embeddings. First, install the
required dependencies:
::
pip install -r requirements.bert.txt
Then pass the ``--use-bert`` argument to the training command.
See the possible configuration options in ``config.py`` (relevant
options have the prefix ``bert``).
BERT Multilingual Training
~~~~~~~~~~~~~~~~~~~~~~~~~~
A multilingual model can be trained, to leverage cross-lingual transfer
and improve results on low-resource languages:
1. Make sure the input passage files have the ``lang`` attribute. See
the script
```set_lang`` <https://github.com/huji-nlp/semstr/blob/master/semstr/scripts/set_lang.py>`__
in the package ``semstr``.
2. Enable BERT by passing the ``--use-bert`` argument.
3. Use the multilingual model by passing
``--bert-model=bert-base-multilingual-cased``.
4. Pass the ``--bert-multilingual=0`` argument to enable multilingual
training.
BERT Performance
~~~~~~~~~~~~~~~~
Here are the average results over 3 BERT multilingual models trained on
the `German *20K Leagues*
corpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_German-20K>`__,
`English Wiki
corpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_English-Wiki>`__
and only on 15 sentences from the `French *20K Leagues*
corpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_French-20K>`__,
with the following settings:
::
bert-model=bert-base-multilingual-cased
bert-layers=-1 -2 -3 -4
bert-layers-pooling=weighted
bert-token-align-by=sum
The results:
+-----------------------+-------------------+------------------+----------------+
| description | test primary F1 | test remote F1 | test average |
+=======================+===================+==================+================+
| German 20K Leagues | 0.828 | 0.6723 | 0.824 |
+-----------------------+-------------------+------------------+----------------+
| English 20K Leagues | 0.763 | 0.359 | 0.755 |
+-----------------------+-------------------+------------------+----------------+
| French 20K Leagues | 0.739 | 0.46 | 0.732 |
+-----------------------+-------------------+------------------+----------------+
| English Wiki | 0.789 | 0.581 | 0.784 |
+-----------------------+-------------------+------------------+----------------+
\*\ `English *20K Leagues*
corpus <https://github.com/UniversalConceptualCognitiveAnnotation/UCCA_English-20K>`__
is used as out of domain test.
Pre-trained Models with BERT
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
To download and extract `a multilingual
model <https://github.com/huji-nlp/tupa/releases/download/v1.4.0/bert_multilingual_layers_4_layers_pooling_weighted_align_sum.tar.gz>`__
trained with the settings above, run:
::
curl -LO https://github.com/huji-nlp/tupa/releases/download/v1.4.0/bert_multilingual_layers_4_layers_pooling_weighted_align_sum.tar.gz
tar xvzf bert_multilingual_layers_4_layers_pooling_weighted_align_sum.tar.gz
To run the parser using the model, use the following command. Pay
attention that you need to replace ``[lang]`` with the right language
symbol (``fr``, ``en``, or ``de``):
::
python -m tupa example.txt --lang [lang] -m bert_multilingual_layers_4_layers_pooling_weighted_align_sum
Author
------
- Daniel Hershcovich: daniel.hershcovich@gmail.com
Contributors
------------
- Ofir Arviv: ofir.arviv@mail.huji.ac.il
Citation
--------
If you make use of this software, please cite `the following
paper <http://aclweb.org/anthology/P17-1104>`__:
::
@InProceedings{hershcovich2017a,
author = {Hershcovich, Daniel and Abend, Omri and Rappoport, Ari},
title = {A Transition-Based Directed Acyclic Graph Parser for {UCCA}},
booktitle = {Proc. of ACL},
year = {2017},
pages = {1127--1138},
url = {http://aclweb.org/anthology/P17-1104}
}
The version of the parser used in the paper is
`v1.0 <https://github.com/huji-nlp/tupa/releases/tag/v1.0>`__. To
reproduce the experiments, run:
::
curl -L https://raw.githubusercontent.com/huji-nlp/tupa/master/experiments/acl2017.sh | bash
If you use the French, German or multitask models, please cite `the
following paper <http://aclweb.org/anthology/P18-1035>`__:
::
@InProceedings{hershcovich2018multitask,
author = {Hershcovich, Daniel and Abend, Omri and Rappoport, Ari},
title = {Multitask Parsing Across Semantic Representations},
booktitle = {Proc. of ACL},
year = {2018},
pages = {373--385},
url = {http://aclweb.org/anthology/P18-1035}
}
The version of the parser used in the paper is
`v1.3.3 <https://github.com/huji-nlp/tupa/releases/tag/v1.3.3>`__. To
reproduce the experiments, run:
::
curl -L https://raw.githubusercontent.com/huji-nlp/tupa/master/experiments/acl2018.sh | bash
License
-------
This package is licensed under the GPLv3 or later license (see
```LICENSE.txt`` <LICENSE.txt>`__).
|Build Status (Travis CI)| |Build Status (AppVeyor)| |Build Status
(Docs)| |PyPI version|
.. |Build Status (Travis CI)| image:: https://travis-ci.org/danielhers/tupa.svg?branch=master
:target: https://travis-ci.org/danielhers/tupa
.. |Build Status (AppVeyor)| image:: https://ci.appveyor.com/api/projects/status/github/danielhers/tupa?svg=true
:target: https://ci.appveyor.com/project/danielh/tupa
.. |Build Status (Docs)| image:: https://readthedocs.org/projects/tupa/badge/?version=latest
:target: http://tupa.readthedocs.io/en/latest/
.. |PyPI version| image:: https://badge.fury.io/py/TUPA.svg
:target: https://badge.fury.io/py/TUPA
نیازمندی
| مقدار | نام |
|---|---|
| >=1.15.0 | numpy |
| >=0.29 | cython |
| >=4.32.2 | tqdm |
| >=0.14.0 | configargparse |
| <1.3,>=1.2.3 | ucca |
| <1.3,>=1.2.2 | semstr[amr] |
| ==2.1 | dynet |
| >=1.5.2 | logbook |
| ==1.3.1 | torch |
| ==0.6.2 | pytorch-pretrained-bert |
| >=0.12.2 | Flask |
| >=0.12 | Flask-Assets |
| >=1.4.0 | Flask-Compress |
| >=2.9.6 | Jinja2 |
| >=2.0.2 | matplotlib |
| >=1.11 | networkx |
| >=0.12.1 | webassets |
| - | scipy |
| - | pillow |
| - | matplotlib |
نحوه نصب
نصب پکیج whl TUPA-1.4.2:
pip install TUPA-1.4.2.whl
نصب پکیج tar.gz TUPA-1.4.2:
pip install TUPA-1.4.2.tar.gz