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import json
from sentence_transformers import SentenceTransformer
from tqdm import tqdm
model = SentenceTransformer('thenlper/gte-large')
embed = lambda text: model.encode(text).tolist()
def parse_data(file_path):
papers = []
with open(file_path, 'r') as file:
paper = {}
for line in tqdm(file, desc="Parsing data"):
if line.startswith('#*'):
paper['title'] = line[2:].strip()
elif line.startswith('#@'):
paper['authors'] = line[2:].strip().split(',')
elif line.startswith('#t'):
paper['year'] = int(line[2:].strip())
elif line.startswith('#c'):
paper['venue'] = line[2:].strip()
elif line.startswith('#index'):
paper['index'] = int(line[6:].strip())
elif line.startswith('#%'):
if 'references' not in paper:
paper['references'] = []
paper['references'].append(int(line[2:].strip()))
elif line.startswith('#!'):
paper['abstract'] = line[2:].strip()
paper['embedding'] = embed(line[2:].strip())
if 'references' not in paper: paper['references'] = []
papers.append(paper)
paper = {}
elif line.strip() == '':
continue
return papers
file_path = '/home/ppxscal/Projects/kellis/neuralmaps/papers.txt'
papers = parse_data(file_path)
with open('papers.json', 'w') as json_file:
json.dump(papers, json_file)
with open('papers_formatted.json', 'w') as json_file:
json.dump(papers, json_file, indent=1) |