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generate.py
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generate.py
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import re
import json
import urllib.parse
import urllib.request
from operator import attrgetter, itemgetter
from tqdm import tqdm
from natsort import natsorted
arxiv_prefix = 'https://arxiv.org/abs/'
arxiv_prefix_len = len(arxiv_prefix)
github_prefix = 'https://github.com/'
github_prefix_len = len(github_prefix)
arxiv_papers = set()
def get_and_sort_meta_info(json_file):
with open(json_file) as f:
meta_info = json.load(f)
meta_info = natsorted(meta_info, key=itemgetter('paper_link'))
with open(json_file, 'w') as f:
f.write(json.dumps(meta_info, indent=4))
return meta_info
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.__dict__ = self
def find_nth(haystack, needle, n):
start = haystack.find(needle)
while start >= 0 and n > 1:
start = haystack.find(needle, start+len(needle))
n -= 1
return start
def fancy_code(code, no_attrs=True):
if no_attrs:
attrs = ''
else:
attrs = []
if 'unofficial' in code:
attrs.append('unofficial')
else:
if 'pretrained' in code and not code['pretrained']:
attrs.append('not pretrained')
if 'load_pretrained' in code:
attrs.append('load pretrained')
if 'pretrained' in code and 'unofficial' in code:
attrs.append('pretrained')
if 'email_for_pretrained' in code:
attrs.append('email for pretrained')
if 'no_training_code' in code:
attrs.append('no training code')
if len(attrs) == 0:
attrs = ''
else:
attrs = '({})'.format(', '.join(attrs))
if code['link'].startswith(github_prefix):
tmp = code['link'][github_prefix_len:]
if tmp.endswith('/'):
tmp = tmp[:-1]
if len(re.findall('/', tmp)) > 2:
tmp = tmp[:find_nth(tmp, '/', 2)]
github_stars = ' '.format(tmp)
else:
github_stars = ''
return '[{training_language}]({code_link} ){attrs}{github_stars}'.format(
training_language=code['language'], code_link=code['link'], attrs=attrs, github_stars=github_stars)
def query_semantic_scholar(query):
if query == '':
return 'N/A', '-'
try:
global arxiv_papers
if query in arxiv_papers:
raise ValueError('Duplicate Paper {}'.format(query))
arxiv_papers.add(query)
res = json.loads(urllib.request.urlopen("https://api.semanticscholar.org/v1/paper/" + query).read())
count = len(res['citations'])
return (str(count) if count < 999 else '999+'), str(res['year']) + '/??'
except:
return 'N/A', '-'
def fetch_common_parts(paper):
is_arxiv = paper['paper_link'].startswith(arxiv_prefix)
if is_arxiv:
arxiv_id = paper['paper_link'][arxiv_prefix_len:]
arxiv_date = arxiv_id.split('.')[0]
date_part = '20' + arxiv_date[:2] + '/' + arxiv_date[2:]
citation_part, _ = query_semantic_scholar('arXiv:{}'.format(arxiv_id))
else:
citation_part, date_part = query_semantic_scholar(paper.get('doi', paper.get('s2_paper_id', '')))
paper_part = '[{paper_title}]({paper_link})'.format(paper_title=paper['paper_title'],
paper_link=paper['paper_link'])
return citation_part, date_part, paper_part
def generate_word_embedding_table():
header = ['|date|paper|citation count|training code|pretrained models|',
'|:---:|:---:|:---:|:---:|:---:|']
generated_lines = []
meta_info = get_and_sort_meta_info('word-embedding.json')
for paper in tqdm(meta_info, desc='word embedding'):
citation_part, date_part, paper_part = fetch_common_parts(paper)
if 'code' in paper:
training_code_part = fancy_code(paper['code'][0])
else:
training_code_part = '-'
pretrained_part = '-' if 'name' not in paper else '[{name}]({pretrained_link} ){broken_link}' \
.format(name=paper['name'], pretrained_link=paper['pretrained_link'],
broken_link='(broken)' if paper.get('broken_link', False) else '')
generated_lines.append(
AttrDict(date_part=date_part, paper_part=paper_part,
training_code_part=training_code_part,
pretrained_part=pretrained_part, citation_part=citation_part))
generated_lines = sorted(generated_lines, key=attrgetter('date_part', 'citation_part'))
generated_lines = [
'|{date_part}|{paper_part}|{citation_part}|{training_code_part}|{pretrained_part}|'.format(**x) for x in
generated_lines]
return '\n'.join(header + generated_lines)
def generate_contextualized_table():
header = ['|date|paper|citation count|code|pretrained models|',
'|:---:|:---:|:---:|:---:|:---:|']
generated_lines = []
meta_info = get_and_sort_meta_info('contextualized.json')
for paper in tqdm(meta_info, desc='contextualized'):
citation_part, date_part, paper_part = fetch_common_parts(paper)
if 'code' in paper:
training_code_part = '<br>'.join([fancy_code(code) for code in paper['code']])
else:
training_code_part = '-'
if 'pretrained_models' in paper:
if len(paper['pretrained_models']) == 1:
pretrained_models = '[{name}]({pretrained_link} )'.format(name=paper['model_name'],
pretrained_link=paper['pretrained_models'][0][
'link'])
else:
pretrained_links = ', '.join(
['[{name}]({link})'.format(name=x['name'], link=x['link']) for x in paper['pretrained_models']])
pretrained_models = '{name}({pretrained_link})'.format(name=paper['model_name'],
pretrained_link=pretrained_links)
else:
pretrained_models = '-'
generated_lines.append(
AttrDict(date_part=date_part, paper_part=paper_part, training_code_part=training_code_part,
pretrained_models=pretrained_models, citation_part=citation_part))
generated_lines = sorted(generated_lines, key=attrgetter('date_part', 'citation_part'))
generated_lines = [
'|{date_part}|{paper_part}|{citation_part}|{training_code_part}|{pretrained_models}|'.format(**x) for x in
generated_lines]
return '\n'.join(header + generated_lines)
def generate_encoder_table():
header = ['|date|paper|citation count|code|model_name|',
'|:---:|:---:|:---:|:---:|:---:|']
generated_lines = []
meta_info = get_and_sort_meta_info('encoder.json')
for paper in tqdm(meta_info, 'encoder'):
citation_part, date_part, paper_part = fetch_common_parts(paper)
if 'code' in paper:
training_code_part = '<br>'.join([fancy_code(code) for code in paper['code']])
else:
training_code_part = '-'
model_name = paper['name']
generated_lines.append(
AttrDict(date_part=date_part, paper_part=paper_part, training_code_part=training_code_part,
model_name=model_name, citation_part=citation_part))
generated_lines = sorted(generated_lines, key=attrgetter('date_part', 'citation_part'))
generated_lines = [
'|{date_part}|{paper_part}|{citation_part}|{training_code_part}|{model_name}|'.format(**x) for x in
generated_lines]
return '\n'.join(header + generated_lines)
if __name__ == '__main__':
with open('README_BASE.md', 'r') as f:
readme = f.read()
readme = readme.replace('{{{word-embedding-table}}}', generate_word_embedding_table())
readme = readme.replace('{{{contextualized-table}}}', generate_contextualized_table())
readme = readme.replace('{{{encoder-table}}}', generate_encoder_table())
with open('README.md', 'w') as f:
f.write(readme)