diff --git a/.pylintrc b/.pylintrc
index bd90039..4b14e41 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -1,7 +1,10 @@
[MASTER]
disable =
+ duplicate-code,
logging-fstring-interpolation,
+ too-few-public-methods,
+ too-many-locals,
[FORMAT]
diff --git a/COPYING b/COPYING
new file mode 100644
index 0000000..f288702
--- /dev/null
+++ b/COPYING
@@ -0,0 +1,674 @@
+ GNU GENERAL PUBLIC LICENSE
+ Version 3, 29 June 2007
+
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+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+ 17. Interpretation of Sections 15 and 16.
+
+ If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+ END OF TERMS AND CONDITIONS
+
+ How to Apply These Terms to Your New Programs
+
+ If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+ To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+
+ Copyright (C)
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+
+Also add information on how to contact you by electronic and paper mail.
+
+ If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+ Copyright (C)
+ This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+ This is free software, and you are welcome to redistribute it
+ under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License. Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+ You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+.
+
+ The GNU General Public License does not permit incorporating your program
+into proprietary programs. If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library. If this is what you want to do, use the GNU Lesser General
+Public License instead of this License. But first, please read
+.
diff --git a/LICENSE b/LICENSE
index 7bf4e37..d231b2e 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,14 @@
Copyright (c) 2020 CKIP Lab.
-This work is licensed under the Creative Commons
-Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy
-of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or send
-a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program. If not, see .
diff --git a/README.rst b/README.rst
index 80f9c68..03fb548 100644
--- a/README.rst
+++ b/README.rst
@@ -1,4 +1,212 @@
CKIP Transformers
-----------------
-UNDER CONSTRUCTION...
+This open-source library implements CKIP Chinese NLP tools using transformers models.
+
+* (WS) Word Segmentation
+* (POS) Part-of-Speech Tagging
+* (NER) Named Entity Recognition
+
+Git
+^^^
+
+https://github.com/emfomy/ckip-transformers
+
+|GitHub Version| |GitHub Release| |GitHub Issues|
+
+.. |GitHub Version| image:: https://img.shields.io/github/v/release/emfomy/ckip-transformers.svg?maxAge=3600
+ :target: https://github.com/emfomy/ckip-transformers/releases
+
+.. |GitHub License| image:: https://img.shields.io/github/license/emfomy/ckip-transformers.svg?maxAge=3600
+ :target: https://github.com/emfomy/ckip-transformers/blob/master/LICENSE
+
+.. |GitHub Release| image:: https://img.shields.io/github/release-date/emfomy/ckip-transformers.svg?maxAge=3600
+
+.. |GitHub Downloads| image:: https://img.shields.io/github/downloads/emfomy/ckip-transformers/total.svg?maxAge=3600
+ :target: https://github.com/emfomy/ckip-transformers/releases/latest
+
+.. |GitHub Issues| image:: https://img.shields.io/github/issues/emfomy/ckip-transformers.svg?maxAge=3600
+ :target: https://github.com/emfomy/ckip-transformers/issues
+
+.. |GitHub Forks| image:: https://img.shields.io/github/forks/emfomy/ckip-transformers.svg?style=social&label=Fork&maxAge=3600
+
+.. |GitHub Stars| image:: https://img.shields.io/github/stars/emfomy/ckip-transformers.svg?style=social&label=Star&maxAge=3600
+
+.. |GitHub Watchers| image:: https://img.shields.io/github/watchers/emfomy/ckip-transformers.svg?style=social&label=Watch&maxAge=3600
+
+PyPI
+^^^^
+
+https://pypi.org/project/ckip-transformers
+
+|PyPI Version| |PyPI License| |PyPI Downloads| |PyPI Python| |PyPI Implementation| |PyPI Status|
+
+.. |PyPI Version| image:: https://img.shields.io/pypi/v/ckip-transformers.svg?maxAge=3600
+ :target: https://pypi.org/project/ckip-transformers
+
+.. |PyPI License| image:: https://img.shields.io/pypi/l/ckip-transformers.svg?maxAge=3600
+ :target: https://github.com/emfomy/ckip-transformers/blob/master/LICENSE
+
+.. |PyPI Downloads| image:: https://img.shields.io/pypi/dm/ckip-transformers.svg?maxAge=3600
+ :target: https://pypi.org/project/ckip-transformers#files
+
+.. |PyPI Python| image:: https://img.shields.io/pypi/pyversions/ckip-transformers.svg?maxAge=3600
+
+.. |PyPI Implementation| image:: https://img.shields.io/pypi/implementation/ckip-transformers.svg?maxAge=3600
+
+.. |PyPI Format| image:: https://img.shields.io/pypi/format/ckip-transformers.svg?maxAge=3600
+
+.. |PyPI Status| image:: https://img.shields.io/pypi/status/ckip-transformers.svg?maxAge=3600
+
+Documentation
+^^^^^^^^^^^^^
+
+https://ckip-transformers.readthedocs.io/
+
+|ReadTheDocs Home|
+
+.. |ReadTheDocs Home| image:: https://img.shields.io/website/https/ckip-transformers.readthedocs.io.svg?maxAge=3600&up_message=online&down_message=offline
+ :target: https://ckip-transformers.readthedocs.io
+
+Relative Demos / Packages
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+- `CkipTagger `_: An alternative Chinese NLP library with using BiLSTM.
+- `CKIP CoreNLP Toolkit `_: A Chinese NLP library with more NLP tasks and utilities.
+
+Contributers
+^^^^^^^^^^^^
+
+* `Mu Yang `__ at `CKIP `__ (Author & Maintainer)
+* `Wei-Yun Ma `__ at `CKIP `__ (Maintainer)
+
+Installation
+------------
+
+``pip install -U ckip-transformers``
+
+Requirements:
+
+* `Python `__ 3.6+
+* `PyTorch `__ 1.1+
+* `HuggingFace Transformers `__ 3.5+
+
+Installation via Pip
+^^^^^^^^^^^^^^^^^^^^
+
+``pip install -U ckip-transformers``
+
+Usage
+-----
+
+See https://ckip-transformers.readthedocs.io/en/latest/_api/ckip_transformers.html for API details.
+
+The complete script of this example is https://github.com/ckiplab/ckip-transformers/blob/master/example/example.py.
+
+1. Import module
+^^^^^^^^^^^^^^^^
+
+.. code-block:: python
+
+ from ckip_transformers.nlp import CkipWordSegmenter, CkipPosTagger, CkipNerChunker
+
+2. Load models
+^^^^^^^^^^^^^^
+
+.. code-block:: python
+
+ # Initialize drivers
+ ws_driver = CkipWordSegmenter()
+ pos_driver = CkipPosTagger()
+ ner_driver = CkipNerChunker()
+
+3. Run pipeline
+^^^^^^^^^^^^^^^
+
+- The input for word segmentation and named-entity recognition must be a list of sentences.
+- The input for part-of-speech tagging must be a list of list of words (the output of word segmentation).
+
+.. code-block:: python
+
+ # Input text
+ text = [
+ '傅達仁今將執行安樂死,卻突然爆出自己20年前遭緯來體育台封殺,他不懂自己哪裡得罪到電視台。',
+ '美國參議院針對今天總統布什所提名的勞工部長趙小蘭展開認可聽證會,預料她將會很順利通過參議院支持,成為該國有史以來第一位的華裔女性內閣成員。',
+ ]
+
+ # Run pipeline
+ ws = ws_driver(text)
+ pos = pos_driver(ws)
+ ner = ner_driver(text)
+
+4. Show results
+^^^^^^^^^^^^^^^
+
+.. code-block:: python
+
+ # Pack word segmentation and part-of-speech results
+ def pack_ws_pos_sentece(sentence_ws, sentence_pos):
+ assert len(sentence_ws) == len(sentence_pos)
+ res = []
+ for word_ws, word_pos in zip(sentence_ws, sentence_pos):
+ res.append(f'{word_ws}({word_pos})')
+ return '\u3000'.join(res)
+
+ # Show results
+ for sentence, sentence_ws, sentence_pos, sentence_ner in zip(text, ws, pos, ner):
+ print(sentence)
+ print(pack_ws_pos_sentece(sentence_ws, sentence_pos))
+ for entity in sentence_ner:
+ print(entity)
+ print()
+
+.. code-block:: text
+
+ 傅達仁今將執行安樂死,卻突然爆出自己20年前遭緯來體育台封殺,他不懂自己哪裡得罪到電視台。
+ 傅達仁(Nb) 今(Nd) 將(D) 執行(VC) 安樂死(Na) ,(COMMACATEGORY) 卻(D) 突然(D) 爆出(VJ) 自己(Nh) 20(Neu) 年(Nf) 前(Ng) 遭(P) 緯來(Nb) 體育台(Na) 封殺(VC) ,(COMMACATEGORY) 他(Nh) 不(D) 懂(VK) 自己(Nh) 哪裡(Ncd) 得罪到(VC) 電視台(Nc) 。(PERIODCATEGORY)
+ NerToken(word='傅達仁', ner='PERSON', idx=(0, 3))
+ NerToken(word='今', ner='DATE', idx=(3, 4))
+ NerToken(word='20年', ner='DATE', idx=(18, 21))
+ NerToken(word='緯來體育台', ner='ORG', idx=(23, 28))
+
+ 美國參議院針對今天總統布什所提名的勞工部長趙小蘭展開認可聽證會,預料她將會很順利通過參議院支持,成為該國有史以來第一位的華裔女性內閣成員。
+ 美國(Nc) 參議院(Nc) 針對(P) 今天(Nd) 總統(Na) 布什(Nb) 所(D) 提名(VC) 的(DE) 勞工部長(Na) 趙小蘭(Nb) 展開(VC) 認可(VC) 聽證會(Na) ,(COMMACATEGORY) 預料(VE) 她(Nh) 將(D) 會(D) 很(Dfa) 順利(VH) 通過(VC) 參議院(Nc) 支持(VC) ,(COMMACATEGORY) 成為(VG) 該(Nes) 國(Nc) 有史以來(D) 第一(Neu) 位(Nf) 的(DE) 華裔(Na) 女性(Na) 內閣(Na) 成員(Na) 。(PERIODCATEGORY)
+ NerToken(word='美國參議院', ner='ORG', idx=(0, 5))
+ NerToken(word='今天', ner='LOC', idx=(7, 9))
+ NerToken(word='布什', ner='PERSON', idx=(11, 13))
+ NerToken(word='勞工部長', ner='ORG', idx=(17, 21))
+ NerToken(word='趙小蘭', ner='PERSON', idx=(21, 24))
+ NerToken(word='認可聽證會', ner='EVENT', idx=(26, 31))
+ NerToken(word='參議院', ner='ORG', idx=(42, 45))
+ NerToken(word='第一', ner='ORDINAL', idx=(56, 58))
+ NerToken(word='華裔', ner='NORP', idx=(60, 62))
+
+Pretrained Models
+-----------------
+
+One may also use our pretrained models with HuggingFace transformers library directly: https://huggingface.co/ckiplab/.
+
+Pretrained Language Models
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* `ALBERT Tiny `_
+* `ALBERT Base `_
+* `BERT Base `_
+* `GPT2 Base `_
+
+NLP Task Models
+^^^^^^^^^^^^^^^
+
+* `BERT Base — Word Segmentation `_
+* `BERT Base — Part-of-Speech Tagging `_
+* `BERT Base — Named-Entity Recognition `_
+
+License
+-------
+
+|GPL-3.0|
+
+Copyright (c) 2020 `CKIP Lab `__ under the `GPL-3.0 License `__.
+
+.. |GPL-3.0| image:: https://www.gnu.org/graphics/gplv3-with-text-136x68.png
+ :target: https://www.gnu.org/licenses/gpl-3.0.html
diff --git a/ckip_transformers/__init__.py b/ckip_transformers/__init__.py
index 2dd83a3..571faba 100644
--- a/ckip_transformers/__init__.py
+++ b/ckip_transformers/__init__.py
@@ -10,9 +10,9 @@
__copyright__ = '2020 CKIP Lab'
__title__ = 'CKIP Transformers'
-__version__ = '0.0.0'
+__version__ = '0.1.0'
__description__ = 'CKIP Transformers'
-__license__ = 'CC BY-NC-SA 4.0'
+__license__ = 'GPL-3.0'
__url__ = 'https://ckip-transformers.readthedocs.io'
__download_url__ = __url__+'/tarball/'+__version__
diff --git a/ckip_transformers/nlp/__init__.py b/ckip_transformers/nlp/__init__.py
new file mode 100644
index 0000000..cdaffe2
--- /dev/null
+++ b/ckip_transformers/nlp/__init__.py
@@ -0,0 +1,16 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+"""
+This module provides the CKIP Transformers NLP drivers.
+"""
+
+__author__ = 'Mu Yang '
+__copyright__ = '2020 CKIP Lab'
+__license__ = 'GPL-3.0'
+
+from .driver import (
+ CkipWordSegmenter,
+ CkipPosTagger,
+ CkipNerChunker,
+)
diff --git a/ckip_transformers/nlp/driver.py b/ckip_transformers/nlp/driver.py
new file mode 100644
index 0000000..ae01a6c
--- /dev/null
+++ b/ckip_transformers/nlp/driver.py
@@ -0,0 +1,256 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+"""
+This module implements the CKIP Transformers NLP drivers.
+"""
+
+__author__ = 'Mu Yang '
+__copyright__ = '2020 CKIP Lab'
+__license__ = 'GPL-3.0'
+
+from typing import (
+ List,
+ Optional,
+)
+
+import numpy as np
+
+from .util import (
+ CkipTokenClassification,
+ NerToken,
+)
+
+################################################################################################################################
+
+class CkipWordSegmenter(CkipTokenClassification):
+ """The word segmentation driver.
+
+ Parameters
+ ----------
+ model_name : ``str``, *optional*, defaults to ``'ckiplab/bert-base-chinese-ws'``
+ The pretrained model name.
+ tokenizer_name : ``str``, *optional*, defaults to **model_name**
+ The pretrained tokenizer name.
+ """
+
+ def __init__(self,
+ model_name: Optional[str] = 'ckiplab/bert-base-chinese-ws',
+ tokenizer_name: Optional[str] = None,
+ ):
+ super().__init__(model_name=model_name, tokenizer_name=tokenizer_name)
+
+ def __call__(self,
+ input_text: List[str],
+ *,
+ max_length: Optional[int] = None,
+ ) -> List[List[str]]:
+ """Call the driver.
+
+ Parameters
+ ----------
+ input_text : ``List[str]``
+ The input sentences. Each sentence is a string.
+ max_length : ``int``, *optional*
+ The maximum length of the sentence,
+ must not longer then the maximum sequence length for this model (i.e. ``tokenizer.model_max_length``).
+
+ Returns
+ -------
+ ``List[List[NerToken]]``
+ A list of list of words (``str``).
+ """
+
+ # Call model
+ (
+ loss,
+ index_map,
+ ) = super().__call__(input_text, max_length=max_length)
+
+ # Post-process results
+ output_text = []
+ for sent_data in zip(input_text, index_map):
+ output_sent = []
+ word = ''
+ for input_char, loss_index in zip(*sent_data):
+ if loss_index is None:
+ if word:
+ output_sent.append(word)
+ output_sent.append(input_char)
+ word = ''
+ else:
+ loss_b, loss_i = loss[loss_index]
+
+ if loss_b > loss_i:
+ if word:
+ output_sent.append(word)
+ word = input_char
+ else:
+ word += input_char
+
+ if word:
+ output_sent.append(word)
+ output_text.append(output_sent)
+
+ return output_text
+
+################################################################################################################################
+
+class CkipPosTagger(CkipTokenClassification):
+ """The part-of-speech tagging driver.
+
+ Parameters
+ ----------
+ model_name : ``str``, *optional*, defaults to ``'ckiplab/bert-base-chinese-pos'``
+ The pretrained model name.
+ tokenizer_name : ``str``, *optional*, defaults to **model_name**
+ The pretrained tokenizer name.
+ """
+
+ def __init__(self,
+ model_name: Optional[str] = 'ckiplab/bert-base-chinese-pos',
+ tokenizer_name: Optional[str] = None,
+ ):
+ super().__init__(model_name=model_name, tokenizer_name=tokenizer_name)
+
+ def __call__(self,
+ input_text: List[List[str]],
+ *,
+ max_length: Optional[int] = None,
+ ) -> List[List[str]]:
+ """Call the driver.
+
+ Parameters
+ ----------
+ input_text : ``List[List[str]]``
+ The input sentences. Each sentence is a list of strings (words).
+ max_length : ``int``, *optional*
+ The maximum length of the sentence,
+ must not longer then the maximum sequence length for this model (i.e. ``tokenizer.model_max_length``).
+
+ Returns
+ -------
+ ``List[List[NerToken]]``
+ A list of list of POS tags (``str``).
+ """
+
+ # Call model
+ (
+ loss,
+ index_map,
+ ) = super().__call__(input_text, max_length=max_length)
+
+ # Get labels
+ id2label = self.model.config.id2label
+
+ # Post-process results
+ output_text = []
+ for sent_data in zip(input_text, index_map):
+ output_sent = []
+ for _, loss_index in zip(*sent_data):
+ if loss_index is None:
+ label = 'WHITESPACE'
+ else:
+ label = id2label[np.argmax(loss[loss_index])]
+ output_sent.append(label)
+ output_text.append(output_sent)
+
+ return output_text
+
+################################################################################################################################
+
+class CkipNerChunker(CkipTokenClassification):
+ """The named-entity recognition driver.
+
+ Parameters
+ ----------
+ model_name : ``str``, *optional*, defaults to ``'ckiplab/bert-base-chinese-ner'``
+ The pretrained model name.
+ tokenizer_name : ``str``, *optional*, defaults to **model_name**
+ The pretrained tokenizer name.
+ """
+
+ def __init__(self,
+ model_name: Optional[str] = 'ckiplab/bert-base-chinese-ner',
+ tokenizer_name: Optional[str] = None,
+ ):
+ super().__init__(model_name=model_name, tokenizer_name=tokenizer_name)
+
+ def __call__(self,
+ input_text: List[str],
+ *,
+ max_length: Optional[int] = None,
+ ) -> List[List[NerToken]]:
+ """Call the driver.
+
+ Parameters
+ ----------
+ input_text : ``List[str]``
+ The input sentences. Each sentence is a string.
+ max_length : ``int``, *optional*
+ The maximum length of the sentence,
+ must not longer then the maximum sequence length for this model (i.e. ``tokenizer.model_max_length``).
+
+ Returns
+ -------
+ ``List[List[NerToken]]``
+ A list of list of entities (:class:`~.util.NerToken`).
+ """
+
+ # Call model
+ (
+ loss,
+ index_map,
+ ) = super().__call__(input_text, max_length=max_length)
+
+ # Get labels
+ id2label = self.model.config.id2label
+
+ # Post-process results
+ output_text = []
+ for sent_data in zip(input_text, index_map):
+ output_sent = []
+ entity_word = None
+ entity_ner = None
+ entity_idx0 = None
+ for index_char, (input_char, loss_index,) in enumerate(zip(*sent_data)):
+ if loss_index is None:
+ label = 'O'
+ else:
+ label = id2label[np.argmax(loss[loss_index])]
+
+ if label == 'O':
+ entity_ner = None
+ continue
+
+ bioes, ner = label.split('-')
+
+ if bioes == 'S':
+ output_sent.append(NerToken(
+ word = input_char,
+ ner = ner,
+ idx = (index_char, index_char+len(input_char),),
+ ))
+ entity_ner = None
+ elif bioes == 'B':
+ entity_word = input_char
+ entity_ner = ner
+ entity_idx0 = index_char
+ elif bioes == 'I':
+ if entity_ner == ner:
+ entity_word += input_char
+ else:
+ entity_ner = None
+ elif bioes == 'E':
+ if entity_ner == ner:
+ entity_word += input_char
+ output_sent.append(NerToken(
+ word = entity_word,
+ ner = entity_ner,
+ idx = (entity_idx0, index_char+len(input_char),),
+ ))
+ entity_ner = None
+
+ output_text.append(output_sent)
+
+ return output_text
diff --git a/ckip_transformers/nlp/util.py b/ckip_transformers/nlp/util.py
new file mode 100644
index 0000000..d2f9afb
--- /dev/null
+++ b/ckip_transformers/nlp/util.py
@@ -0,0 +1,182 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+"""
+This module implements the utilities for CKIP Transformers NLP drivers.
+"""
+
+__author__ = 'Mu Yang '
+__copyright__ = '2020 CKIP Lab'
+__license__ = 'GPL-3.0'
+
+from typing import (
+ List,
+ NamedTuple,
+ Optional,
+ Tuple,
+ Union,
+)
+
+import torch
+
+from transformers import (
+ AutoModelForTokenClassification,
+ BatchEncoding,
+ BertTokenizerFast,
+)
+
+################################################################################################################################
+
+class CkipTokenClassification:
+ """The base class for token classification task.
+
+ Parameters
+ ----------
+ model_name : ``str``
+ The pretrained model name (e.g. ``'ckiplab/bert-base-chinese-ws'``).
+ tokenizer_name : ``str``, *optional*, defaults to **model_name**
+ The pretrained tokenizer name (e.g. ``'bert-base-chinese'``).
+ """
+
+ def __init__(self,
+ model_name: str,
+ tokenizer_name: Optional[str] = None,
+ ):
+ self.model = AutoModelForTokenClassification.from_pretrained(model_name)
+ self.tokenizer = BertTokenizerFast.from_pretrained(tokenizer_name or model_name)
+
+ def __call__(self,
+ input_text: Union[List[str], List[List[str]]],
+ *,
+ max_length: Optional[int] = None,
+ ):
+ """Call the driver.
+
+ Parameters
+ ----------
+ input_text : ``List[str]`` or ``List[List[str]]``
+ The input sentences. Each sentence is a string or a list of string.
+ max_length : ``int``
+ The maximum length of the sentence,
+ must not longer then the maximum sequence length for this model (i.e. ``tokenizer.model_max_length``).
+ """
+
+ model_max_length = self.tokenizer.model_max_length - 2 # Add [CLS] and [SEP]
+ if max_length:
+ assert max_length < model_max_length, \
+ 'Sequence length is longer than the maximum sequence length for this model ' \
+ f'({max_length} > {model_max_length}).'
+ else:
+ max_length = model_max_length
+
+ # Get worded input IDs
+ input_ids_worded = [
+ [
+ self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(input_word)) for input_word in input_sent
+ ] for input_sent in input_text
+ ]
+
+ # Flatten input IDs
+ (
+ input_ids,
+ index_map,
+ ) = self._flatten_input_ids(
+ input_ids_worded=input_ids_worded,
+ max_length=max_length,
+ )
+
+ # Pad and segment input IDs
+ (
+ input_ids,
+ attention_mask,
+ ) = self._pad_input_ids(
+ input_ids=input_ids,
+ )
+
+ # Convert to tensors
+ batch = BatchEncoding(
+ data=dict(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ ),
+ tensor_type='pt',
+ )
+
+ # Call Model
+ with torch.no_grad():
+ (
+ loss,
+ ) = self.model(**batch)
+ loss = loss.cpu().numpy()[:, 1:, :] # Remove [CLS]
+
+ return loss, index_map
+
+ @staticmethod
+ def _flatten_input_ids(*,
+ input_ids_worded,
+ max_length,
+ ):
+ input_ids = []
+ index_map = []
+
+ input_ids_sent = []
+ index_map_sent = []
+
+ for input_ids_worded_sent in input_ids_worded:
+ for word_ids in input_ids_worded_sent:
+ word_length = len(word_ids)
+
+ if word_length == 0:
+ index_map_sent.append(None)
+ continue
+
+ # Check if sentence segmentation is needed
+ if len(input_ids_sent) + word_length > max_length:
+ input_ids.append(input_ids_sent)
+ input_ids_sent = []
+
+ # Insert tokens
+ index_map_sent.append((
+ len(input_ids), # line index
+ len(input_ids_sent), # token index
+ ))
+ input_ids_sent += word_ids
+
+ # End of a sentence
+ if input_ids_sent:
+ input_ids.append(input_ids_sent)
+ input_ids_sent = []
+ index_map.append(index_map_sent)
+ index_map_sent = []
+
+ return input_ids, index_map
+
+ def _pad_input_ids(self, *,
+ input_ids,
+ ):
+ max_length = max(map(len, input_ids))
+
+ padded_input_ids = []
+ attention_mask = []
+ for input_ids_sent in input_ids:
+ token_count = len(input_ids_sent)
+ pad_count = max_length - token_count
+ padded_input_ids.append(
+ [self.tokenizer.cls_token_id] +
+ input_ids_sent +
+ [self.tokenizer.sep_token_id] +
+ [self.tokenizer.pad_token_id] * pad_count
+ )
+ attention_mask.append(
+ [1] * (token_count+2) + # [CLS] & input & [SEP]
+ [0] * pad_count # [PAD]s
+ )
+ return padded_input_ids, attention_mask
+
+################################################################################################################################
+
+class NerToken(NamedTuple):
+ """A named-entity recognition token."""
+ word: str #: ``str``, the token word.
+ ner: str #: ``str``, the NER-tag.
+ idx: Tuple[int, int] #: ``Tuple[int, int]``, the starting / ending index in the sentence.
diff --git a/docs/_static/custom.css b/docs/_static/custom.css
index f59bf57..4156ddf 100644
--- a/docs/_static/custom.css
+++ b/docs/_static/custom.css
@@ -6,6 +6,10 @@
margin-bottom: 0;
}
+.rst-content tt.literal, .rst-content tt.literal, .rst-content code.literal {
+ color: #0057A9;
+}
+
.wy-nav-content {
max-width: 1280px;
}
diff --git a/docs/main/readme.rst b/docs/main/readme.rst
index c68af5d..a6210d3 100644
--- a/docs/main/readme.rst
+++ b/docs/main/readme.rst
@@ -1,4 +1 @@
-Introduction
-============
-
.. include:: ../../README.rst
diff --git a/example/example.py b/example/example.py
new file mode 100755
index 0000000..3d44872
--- /dev/null
+++ b/example/example.py
@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+from ckip_transformers import __version__
+from ckip_transformers.nlp import CkipWordSegmenter, CkipPosTagger, CkipNerChunker
+
+def main():
+
+ # Show version
+ print(__version__)
+
+ # Initialize drivers
+ print('Initializing drivers ... WS')
+ ws_driver = CkipWordSegmenter()
+ print('Initializing drivers ... POS')
+ pos_driver = CkipPosTagger()
+ print('Initializing drivers ... NER')
+ ner_driver = CkipNerChunker()
+ print('Initializing drivers ... done')
+ print()
+
+ # Input text
+ text = [
+ '傅達仁今將執行安樂死,卻突然爆出自己20年前遭緯來體育台封殺,他不懂自己哪裡得罪到電視台。',
+ '美國參議院針對今天總統布什所提名的勞工部長趙小蘭展開認可聽證會,預料她將會很順利通過參議院支持,成為該國有史以來第一位的華裔女性內閣成員。',
+ ]
+
+ # Run pipeline
+ print('Running pipeline ... WS')
+ ws = ws_driver(text)
+ print('Running pipeline ... POS')
+ pos = pos_driver(ws)
+ print('Running pipeline ... NER')
+ ner = ner_driver(text)
+ print('Running pipeline ... done')
+ print()
+
+ # Show results
+ for sentence, sentence_ws, sentence_pos, sentence_ner in zip(text, ws, pos, ner):
+ print(sentence)
+ print(pack_ws_pos_sentece(sentence_ws, sentence_pos))
+ for entity in sentence_ner:
+ print(entity)
+ print()
+
+# Pack word segmentation and part-of-speech results
+def pack_ws_pos_sentece(sentence_ws, sentence_pos):
+ assert len(sentence_ws) == len(sentence_pos)
+ res = []
+ for word_ws, word_pos in zip(sentence_ws, sentence_pos):
+ res.append(f'{word_ws}({word_pos})')
+ return '\u3000'.join(res)
+
+if __name__ == '__main__':
+ main()
diff --git a/setup.py b/setup.py
index 151ba11..88cd27f 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
__author__ = 'Mu Yang '
__copyright__ = '2020 CKIP Lab'
-__license__ = 'CC BY-NC-SA 4.0'
+__license__ = 'GPL-3.0'
from setuptools import setup, find_namespace_packages
import ckip_transformers as about
@@ -28,7 +28,7 @@ def main():
platforms=['linux_x86_64'],
license=about.__license__,
classifiers=[
- 'Development Status :: 1 - Planning',
+ 'Development Status :: 4 - Beta',
'Environment :: Console',
'Programming Language :: Python',
'Programming Language :: Python :: 3',
@@ -36,15 +36,15 @@ def main():
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3 :: Only',
- 'License :: Free for non-commercial use',
+ 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
'Operating System :: POSIX :: Linux',
'Natural Language :: Chinese (Traditional)',
],
python_requires='>=3.6',
packages=find_namespace_packages(include=['ckip_transformers', 'ckip_transformers.*',]),
install_requires=[
- 'appdirs>=1.4.3',
'transformers>=3.5.0',
+ 'torch>=1.1.0',
],
data_files=[],
)
diff --git a/test/Makefile b/test/Makefile
new file mode 100644
index 0000000..f3630a2
--- /dev/null
+++ b/test/Makefile
@@ -0,0 +1,17 @@
+RM = rm -rf
+TOX = tox
+
+.PHONY: tox tox-v tox-report clean
+
+tox:
+ NO_COV= $(TOX) -p -e py{36,37,38} --
+
+tox-v:
+ NO_COV= $(TOX) -e py{36,37,38} -- -v
+
+tox-report:
+ - $(TOX) -p -e clean,py36,report -- --cov-append
+ python3.7 -m http.server --directory .test/htmlcov/ 3000
+
+clean:
+ - $(RM) .tox .test .lookup
diff --git a/test/requirements.txt b/test/requirements.txt
new file mode 100644
index 0000000..a96aa0e
--- /dev/null
+++ b/test/requirements.txt
@@ -0,0 +1 @@
+..
\ No newline at end of file
diff --git a/test/script/nlp/_base.py b/test/script/nlp/_base.py
new file mode 100644
index 0000000..6869481
--- /dev/null
+++ b/test/script/nlp/_base.py
@@ -0,0 +1,27 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+__author__ = 'Mu Yang '
+__copyright__ = '2020 CKIP Lab'
+__license__ = 'GPL-3.0'
+
+from ckip_transformers.nlp import *
+
+################################################################################################################################
+
+text = [
+ '中文字耶,啊哈哈哈。',
+ '「完蛋了!」 畢卡索他想',
+]
+ws = [
+ [ '中文字', '耶', ',', '啊', '哈', '哈哈', '。', ],
+ [ '「', '完蛋', '了', '!', '」', ' ', '畢卡索', '他', '想', ],
+]
+pos = [
+ [ 'Na', 'T', 'COMMACATEGORY', 'I', 'D', 'D', 'PERIODCATEGORY', ],
+ [ 'PARENTHESISCATEGORY', 'VH', 'T', 'EXCLAMATIONCATEGORY', 'PARENTHESISCATEGORY', 'WHITESPACE', 'Nb', 'Nh', 'VE', ],
+]
+ner = [
+ [ ( '中文字', 'LANGUAGE', (0, 3), ), ],
+ [ ( '畢卡索', 'PERSON', (7, 10), ), ],
+]
diff --git a/test/script/nlp/run.py b/test/script/nlp/run.py
new file mode 100644
index 0000000..0c8de72
--- /dev/null
+++ b/test/script/nlp/run.py
@@ -0,0 +1,30 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+__author__ = 'Mu Yang '
+__copyright__ = '2020 CKIP Lab'
+__license__ = 'GPL-3.0'
+
+from _base import *
+
+################################################################################################################################
+
+def test_word_segmenter():
+ nlp = CkipWordSegmenter()
+ output_ws = nlp(text)
+ assert output_ws == ws
+
+################################################################################################################################
+
+def test_pos_tagger():
+ nlp = CkipPosTagger()
+ output_pos = nlp(ws)
+ assert output_pos == pos
+
+################################################################################################################################
+
+def test_ner_chunker():
+ nlp = CkipNerChunker()
+ output_ner = nlp(text)
+ output_ner = [ [ tuple(entity) for entity in sent ] for sent in output_ner ]
+ assert output_ner == ner
diff --git a/test/tox.ini b/test/tox.ini
new file mode 100644
index 0000000..8cc3eee
--- /dev/null
+++ b/test/tox.ini
@@ -0,0 +1,35 @@
+[tox]
+envlist = clean,py{36,37,38},report
+skipsdist = true
+
+[testenv]
+changedir = .test
+setenv =
+ PYTHONPATH = {env:PYTHONPATH:}{:}{toxinidir}/..
+
+deps =
+ pytest
+ pytest-cov
+ pytest-xdist
+ -rrequirements.txt
+
+depends =
+ py{36,37,38}: clean
+ report: py{36,37,38}
+
+[testenv:report]
+deps = coverage
+skip_install = true
+commands =
+ coverage report
+ coverage html
+
+[testenv:clean]
+deps = coverage
+skip_install = true
+commands =
+ coverage erase
+
+[testenv:py{36,37,38}]
+commands =
+ pytest {toxinidir}/script/nlp/run.py {env:NO_COV:--cov=ckip_transformers.nlp} {posargs}