Upload graphing.py

script I used to embed abstracts

graphing.py

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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)