├── summaqa ├── __init__.py ├── f1_squad.py ├── qa_models.py └── summaqa.py ├── fig_emnlp.png ├── .gitignore ├── setup.py ├── README.md └── LICENSE /summaqa/__init__.py: -------------------------------------------------------------------------------- 1 | from .summaqa import QG_masked, QA_Metric, evaluate_corpus 2 | -------------------------------------------------------------------------------- /fig_emnlp.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ThomasScialom/summa-qa/HEAD/fig_emnlp.png -------------------------------------------------------------------------------- /summaqa/f1_squad.py: -------------------------------------------------------------------------------- 1 | from collections import Counter 2 | import string 3 | import re 4 | 5 | def normalize_answer(s): 6 | """Lower text and remove punctuation, articles and extra whitespace.""" 7 | def remove_articles(text): 8 | return re.sub(r'\b(a|an|the)\b', ' ', text) 9 | 10 | def white_space_fix(text): 11 | return ' '.join(text.split()) 12 | 13 | def remove_punc(text): 14 | exclude = set(string.punctuation) 15 | return ''.join(ch for ch in text if ch not in exclude) 16 | 17 | def lower(text): 18 | return text.lower() 19 | 20 | return white_space_fix(remove_articles(remove_punc(lower(s)))) 21 | 22 | 23 | 24 | def f1_score(prediction, ground_truth): 25 | prediction_tokens = normalize_answer(prediction).split() 26 | ground_truth_tokens = normalize_answer(ground_truth).split() 27 | 28 | common = Counter(prediction_tokens) & Counter(ground_truth_tokens) 29 | num_same = sum(common.values()) 30 | if num_same == 0: 31 | return 0 32 | precision = 1.0 * num_same / len(prediction_tokens) 33 | recall = 1.0 * num_same / len(ground_truth_tokens) 34 | f1 = (2 * precision * recall) / (precision + recall) 35 | return f1 36 | 37 | -------------------------------------------------------------------------------- /summaqa/qa_models.py: -------------------------------------------------------------------------------- 1 | # code from huggingface see https://huggingface.co/transformers/model_doc/bert.html#bertforquestionanswering 2 | 3 | import torch 4 | from transformers import BertTokenizer, BertForQuestionAnswering 5 | 6 | class QA_Bert(): 7 | def __init__(self): 8 | 9 | self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') 10 | self.model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad') 11 | self.SEP_id = self.tokenizer.encode('[SEP]')[0] 12 | 13 | def predict(self, question, text): 14 | 15 | input_text = "[CLS] " + question + " [SEP] " + text + " [SEP]" 16 | input_ids = self.tokenizer.encode(input_text) 17 | token_type_ids = [0 if i <= input_ids.index(self.SEP_id) else 1 for i in range(len(input_ids))] 18 | start_scores, end_scores = self.model(torch.tensor([input_ids])) 19 | 20 | start_scores = torch.functional.F.softmax(start_scores, -1) * torch.Tensor(token_type_ids) 21 | end_scores = torch.functional.F.softmax(end_scores, -1) * torch.Tensor(token_type_ids) 22 | 23 | start_values, start_indices = start_scores.topk(1) 24 | end_values, end_indices = end_scores.topk(1) 25 | 26 | all_tokens = self.tokenizer.convert_ids_to_tokens(input_ids) 27 | 28 | asw = ' '.join(all_tokens[start_indices[0][0] : end_indices[0][0]+1]) 29 | prob = start_values[0][0] * end_values[0][0] 30 | 31 | return asw, prob.item() -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | 106 | # vscode 107 | .vscode/ -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | import io 2 | import os 3 | import sys 4 | from shutil import rmtree 5 | 6 | from setuptools import find_packages, setup, Command 7 | 8 | # Package meta-data. 9 | NAME = 'summaqa' 10 | DESCRIPTION = "Supporting code for the EMNLP 2019 paper 'Answers Unite! Unsupervised Metrics for Reinforced Summarization Models'" 11 | URL = 'https://github.com/recitalAI/summa-qa' 12 | EMAIL = 'contact@recital.ai' 13 | AUTHOR = 'Thomas Scialom' 14 | REQUIRES_PYTHON = '>=3.6.0' 15 | VERSION = '0.1.0' 16 | 17 | REQUIRED = [ 18 | 'spacy>=2.2.0', 19 | 'en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz', 20 | 'transformers==2.1.1', 21 | ] 22 | 23 | EXTRAS = { 24 | } 25 | 26 | here = os.path.abspath(os.path.dirname(__file__)) 27 | 28 | try: 29 | with io.open(os.path.join(here, 'README.md'), encoding='utf-8') as f: 30 | long_description = '\n' + f.read() 31 | except FileNotFoundError: 32 | long_description = DESCRIPTION 33 | 34 | about = {} 35 | if not VERSION: 36 | project_slug = NAME.lower().replace("-", "_").replace(" ", "_") 37 | with open(os.path.join(here, project_slug, '__version__.py')) as f: 38 | exec(f.read(), about) 39 | else: 40 | about['__version__'] = VERSION 41 | 42 | 43 | # Where the magic happens: 44 | setup( 45 | name=NAME, 46 | version=about['__version__'], 47 | description=DESCRIPTION, 48 | long_description=long_description, 49 | long_description_content_type='text/markdown', 50 | author=AUTHOR, 51 | author_email=EMAIL, 52 | python_requires=REQUIRES_PYTHON, 53 | url=URL, 54 | packages=find_packages(exclude=["tests", "*.tests", "*.tests.*", "tests.*"]), 55 | install_requires=REQUIRED, 56 | extras_require=EXTRAS, 57 | include_package_data=True, 58 | license='Apache', 59 | classifiers=[ 60 | # Trove classifiers 61 | # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers 62 | 'License :: OSI Approved :: Apache Software License', 63 | 'Programming Language :: Python', 64 | 'Programming Language :: Python :: 3', 65 | 'Programming Language :: Python :: 3.6', 66 | 'Programming Language :: Python :: 3.7', 67 | 'Programming Language :: Python :: Implementation :: CPython', 68 | 'Programming Language :: Python :: Implementation :: PyPy' 69 | ], 70 | ) 71 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # SummaQA 2 | *Supporting code for the EMNLP 2019 paper ["Answers Unite! Unsupervised Metrics for Reinforced Summarization Models"](https://arxiv.org/abs/1909.01610)* 3 | 4 | ![](fig_emnlp.png) 5 | 6 | ## Quickstart 7 | #### Clone & Install (*recommended: use a virtual environment*) 8 | ##### The following assumes PyTorch (>=1.1.0) is installed in your environment 9 | ```shell 10 | git clone https://github.com/recitalAI/summa-qa.git 11 | cd summa-qa 12 | pip install -e . 13 | ``` 14 | 15 | #### Generate questions and answers for a text doccument 16 | 17 | ```python 18 | from summaqa import QG_masked 19 | question_generator = QG_masked() 20 | 21 | article = """Super Bowl 50 was an American football game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24–10 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California. As this was the 50th Super Bowl, the league emphasized the "golden anniversary" with various gold-themed initiatives, as well as temporarily suspending the tradition of naming each Super Bowl game with Roman numerals (under which the game would have been known as "Super Bowl L"), so that the logo could prominently feature the Arabic numerals 50.""" 22 | 23 | masked_questions, answer_spans = question_generator.get_questions(article) 24 | ``` 25 | 26 | #### Score a summary 27 | 28 | ```python 29 | from summaqa import QA_Metric 30 | 31 | qa_metric = QA_Metric() 32 | 33 | summary_1 = """Super Bowl 50 determined the champion of the champion of NFL for the 2015 season.""" 34 | score_1 = qa_metric.compute(masked_questions, answer_spans, summary_1) 35 | print("summary 1:", score_1) 36 | 37 | summary_2 = "what what hello hi" 38 | score_2 = qa_metric.compute(masked_questions, answer_spans, summary_2) 39 | print("summary 2:", score_2) 40 | ``` 41 | 42 | *Output:* 43 | 44 | ``` 45 | summary 1: {'avg_prob': 0.10436534642455324, 'avg_fscore': 0.19754273504273503} 46 | summary 2: {'avg_prob': 0.006622146878119868, 'avg_fscore': 0.0} 47 | 48 | ``` 49 | 50 | 51 | 52 | #### Score multiple summaries 53 | ```python 54 | from summaqa import evaluate_corpus 55 | 56 | srcs = [article, article] 57 | gens = [summary_1, summary_2] 58 | 59 | evaluate_corpus(srcs, gens) 60 | 61 | ``` 62 | 63 | *Output:* 64 | 65 | ``` 66 | {'avg_prob': 0.05549374665133655, 'avg_fscore': 0.09877136752136752}``` 67 | -------------------------------------------------------------------------------- /summaqa/summaqa.py: -------------------------------------------------------------------------------- 1 | import spacy 2 | from .f1_squad import f1_score 3 | from .qa_models import QA_Bert 4 | 5 | 6 | class QG_masked: 7 | """ 8 | Cloze style Question Generator based on spacy named entity recognition 9 | """ 10 | 11 | def __init__(self, 12 | spacy_model="en_core_web_sm"): 13 | self.nlp = spacy.load(spacy_model) 14 | 15 | def get_questions(self, text_input): 16 | """ 17 | Generate a list of questions on a text 18 | Args: 19 | text_input: a string 20 | Returns: 21 | a list of question 22 | """ 23 | masked_questions = [] 24 | asws = [] 25 | 26 | for sent in self.nlp(text_input).sents: 27 | for ent in sent.ents: 28 | id_start = ent.start_char - sent.start_char 29 | id_end = ent.start_char - sent.start_char + len(ent.text) 30 | masked_question = sent.text[:id_start] + \ 31 | "MASKED" + sent.text[id_end:] 32 | masked_questions.append(masked_question) 33 | asws.append(ent.text) 34 | 35 | return masked_questions, asws 36 | 37 | 38 | class QA_Metric: 39 | """ 40 | Question Answering based metric 41 | """ 42 | 43 | def __init__(self, model=None): 44 | 45 | if model is None: 46 | model = QA_Bert() 47 | self.model = model 48 | 49 | def compute(self, questions, true_asws, evaluated_text): 50 | """ 51 | Calculate the QA scores for a given text we want to evaluate and a list of questions and their answers. 52 | Args: 53 | questions: a list of string 54 | true_asws: a list of string 55 | evaluated_text: a string 56 | Returns: 57 | a dict containing the probability score and the f-score 58 | """ 59 | if not questions: 60 | return {"avg_prob": 0, "avg_fscore": 0} 61 | 62 | score_prob, score_f = 0, 0 63 | for question, true_asw in zip(questions, true_asws): 64 | 65 | asw_pred, prob = self.model.predict(question, evaluated_text) 66 | 67 | score_prob += prob 68 | score_f += f1_score(asw_pred, true_asw) 69 | 70 | return {"avg_prob": score_prob/len(questions), "avg_fscore": score_f/len(questions)} 71 | 72 | 73 | def evaluate_corpus(srcs, gens, model=None, questionss=None, aswss=None): 74 | """ 75 | Calculate the QA scores for an entire corpus. 76 | Args: 77 | srcs: a list of string (one string per document) 78 | gens: a list of string (one string per summary) 79 | model: [optional]: any model that fits the function predict in qa_models; by default BERT_QA 80 | questionss: [optional]: a list of list with the questions already generated for each src. If None, it will generate it. 81 | aswss: [optional]: a list of list with the ground truth asws for the questions (questionss). If None, it will generate it as well. 82 | Returns: 83 | a dict containing the probability score and f-score, averaged for the corpus 84 | """ 85 | assert any([questionss, aswss]) == all([questionss, aswss] 86 | ), "questionss/aswss should be None if the other is None" 87 | 88 | # if questionss is None initialize a question generator 89 | if not questionss: 90 | question_generator = QG_masked() 91 | # initialize the metric with a QA model 92 | qa_metric = QA_Metric(model) 93 | 94 | global_score = {"avg_prob": 0, "avg_fscore": 0} 95 | 96 | for i, (src, gen) in enumerate(zip(srcs, gens)): 97 | 98 | # if questionss is None, generate the questions and answers else get the corrisponding ones. 99 | if not questionss: 100 | masked_questions, masked_question_asws = question_generator.get_questions( 101 | src) 102 | else: 103 | masked_questions, masked_question_asws = questionss[i], aswss[i] 104 | 105 | # compute the metric 106 | gen_score = qa_metric.compute( 107 | masked_questions, masked_question_asws, gen) 108 | global_score['avg_prob'] += gen_score['avg_prob'] 109 | global_score['avg_fscore'] += gen_score['avg_fscore'] 110 | 111 | # average it 112 | global_score['avg_prob'] = global_score['avg_prob'] / len(srcs) 113 | global_score['avg_fscore'] = global_score['avg_fscore'] / len(srcs) 114 | 115 | return global_score 116 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. For the purposes of this definition, 18 | "control" means (i) the power, direct or indirect, to cause the 19 | direction or management of such entity, whether by contract or 20 | otherwise, or (ii) ownership of fifty percent (50%) or more of the 21 | outstanding shares, or (iii) beneficial ownership of such entity. 22 | 23 | "You" (or "Your") shall mean an individual or Legal Entity 24 | exercising permissions granted by this License. 25 | 26 | "Source" form shall mean the preferred form for making modifications, 27 | including but not limited to software source code, documentation 28 | source, and configuration files. 29 | 30 | "Object" form shall mean any form resulting from mechanical 31 | transformation or translation of a Source form, including but 32 | not limited to compiled object code, generated documentation, 33 | and conversions to other media types. 34 | 35 | "Work" shall mean the work of authorship, whether in Source or 36 | Object form, made available under the License, as indicated by a 37 | copyright notice that is included in or attached to the work 38 | (an example is provided in the Appendix below). 39 | 40 | "Derivative Works" shall mean any work, whether in Source or Object 41 | form, that is based on (or derived from) the Work and for which the 42 | editorial revisions, annotations, elaborations, or other modifications 43 | represent, as a whole, an original work of authorship. For the purposes 44 | of this License, Derivative Works shall not include works that remain 45 | separable from, or merely link (or bind by name) to the interfaces of, 46 | the Work and Derivative Works thereof. 47 | 48 | "Contribution" shall mean any work of authorship, including 49 | the original version of the Work and any modifications or additions 50 | to that Work or Derivative Works thereof, that is intentionally 51 | submitted to Licensor for inclusion in the Work by the copyright owner 52 | or by an individual or Legal Entity authorized to submit on behalf of 53 | the copyright owner. For the purposes of this definition, "submitted" 54 | means any form of electronic, verbal, or written communication sent 55 | to the Licensor or its representatives, including but not limited to 56 | communication on electronic mailing lists, source code control systems, 57 | and issue tracking systems that are managed by, or on behalf of, the 58 | Licensor for the purpose of discussing and improving the Work, but 59 | excluding communication that is conspicuously marked or otherwise 60 | designated in writing by the copyright owner as "Not a Contribution." 61 | 62 | "Contributor" shall mean Licensor and any individual or Legal Entity 63 | on behalf of whom a Contribution has been received by Licensor and 64 | subsequently incorporated within the Work. 65 | 66 | 2. Grant of Copyright License. Subject to the terms and conditions of 67 | this License, each Contributor hereby grants to You a perpetual, 68 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 69 | copyright license to reproduce, prepare Derivative Works of, 70 | publicly display, publicly perform, sublicense, and distribute the 71 | Work and such Derivative Works in Source or Object form. 72 | 73 | 3. Grant of Patent License. Subject to the terms and conditions of 74 | this License, each Contributor hereby grants to You a perpetual, 75 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 76 | (except as stated in this section) patent license to make, have made, 77 | use, offer to sell, sell, import, and otherwise transfer the Work, 78 | where such license applies only to those patent claims licensable 79 | by such Contributor that are necessarily infringed by their 80 | Contribution(s) alone or by combination of their Contribution(s) 81 | with the Work to which such Contribution(s) was submitted. If You 82 | institute patent litigation against any entity (including a 83 | cross-claim or counterclaim in a lawsuit) alleging that the Work 84 | or a Contribution incorporated within the Work constitutes direct 85 | or contributory patent infringement, then any patent licenses 86 | granted to You under this License for that Work shall terminate 87 | as of the date such litigation is filed. 88 | 89 | 4. Redistribution. You may reproduce and distribute copies of the 90 | Work or Derivative Works thereof in any medium, with or without 91 | modifications, and in Source or Object form, provided that You 92 | meet the following conditions: 93 | 94 | (a) You must give any other recipients of the Work or 95 | Derivative Works a copy of this License; and 96 | 97 | (b) You must cause any modified files to carry prominent notices 98 | stating that You changed the files; and 99 | 100 | (c) You must retain, in the Source form of any Derivative Works 101 | that You distribute, all copyright, patent, trademark, and 102 | attribution notices from the Source form of the Work, 103 | excluding those notices that do not pertain to any part of 104 | the Derivative Works; and 105 | 106 | (d) If the Work includes a "NOTICE" text file as part of its 107 | distribution, then any Derivative Works that You distribute must 108 | include a readable copy of the attribution notices contained 109 | within such NOTICE file, excluding those notices that do not 110 | pertain to any part of the Derivative Works, in at least one 111 | of the following places: within a NOTICE text file distributed 112 | as part of the Derivative Works; within the Source form or 113 | documentation, if provided along with the Derivative Works; or, 114 | within a display generated by the Derivative Works, if and 115 | wherever such third-party notices normally appear. The contents 116 | of the NOTICE file are for informational purposes only and 117 | do not modify the License. You may add Your own attribution 118 | notices within Derivative Works that You distribute, alongside 119 | or as an addendum to the NOTICE text from the Work, provided 120 | that such additional attribution notices cannot be construed 121 | as modifying the License. 122 | 123 | You may add Your own copyright statement to Your modifications and 124 | may provide additional or different license terms and conditions 125 | for use, reproduction, or distribution of Your modifications, or 126 | for any such Derivative Works as a whole, provided Your use, 127 | reproduction, and distribution of the Work otherwise complies with 128 | the conditions stated in this License. 129 | 130 | 5. Submission of Contributions. Unless You explicitly state otherwise, 131 | any Contribution intentionally submitted for inclusion in the Work 132 | by You to the Licensor shall be under the terms and conditions of 133 | this License, without any additional terms or conditions. 134 | Notwithstanding the above, nothing herein shall supersede or modify 135 | the terms of any separate license agreement you may have executed 136 | with Licensor regarding such Contributions. 137 | 138 | 6. Trademarks. This License does not grant permission to use the trade 139 | names, trademarks, service marks, or product names of the Licensor, 140 | except as required for reasonable and customary use in describing the 141 | origin of the Work and reproducing the content of the NOTICE file. 142 | 143 | 7. Disclaimer of Warranty. Unless required by applicable law or 144 | agreed to in writing, Licensor provides the Work (and each 145 | Contributor provides its Contributions) on an "AS IS" BASIS, 146 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or 147 | implied, including, without limitation, any warranties or conditions 148 | of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A 149 | PARTICULAR PURPOSE. You are solely responsible for determining the 150 | appropriateness of using or redistributing the Work and assume any 151 | risks associated with Your exercise of permissions under this License. 152 | 153 | 8. Limitation of Liability. In no event and under no legal theory, 154 | whether in tort (including negligence), contract, or otherwise, 155 | unless required by applicable law (such as deliberate and grossly 156 | negligent acts) or agreed to in writing, shall any Contributor be 157 | liable to You for damages, including any direct, indirect, special, 158 | incidental, or consequential damages of any character arising as a 159 | result of this License or out of the use or inability to use the 160 | Work (including but not limited to damages for loss of goodwill, 161 | work stoppage, computer failure or malfunction, or any and all 162 | other commercial damages or losses), even if such Contributor 163 | has been advised of the possibility of such damages. 164 | 165 | 9. Accepting Warranty or Additional Liability. While redistributing 166 | the Work or Derivative Works thereof, You may choose to offer, 167 | and charge a fee for, acceptance of support, warranty, indemnity, 168 | or other liability obligations and/or rights consistent with this 169 | License. However, in accepting such obligations, You may act only 170 | on Your own behalf and on Your sole responsibility, not on behalf 171 | of any other Contributor, and only if You agree to indemnify, 172 | defend, and hold each Contributor harmless for any liability 173 | incurred by, or claims asserted against, such Contributor by reason 174 | of your accepting any such warranty or additional liability. 175 | 176 | END OF TERMS AND CONDITIONS 177 | 178 | APPENDIX: How to apply the Apache License to your work. 179 | 180 | To apply the Apache License to your work, attach the following 181 | boilerplate notice, with the fields enclosed by brackets "[]" 182 | replaced with your own identifying information. (Don't include 183 | the brackets!) The text should be enclosed in the appropriate 184 | comment syntax for the file format. We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | --------------------------------------------------------------------------------