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encode-pubmed.py

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+import h5py as h5
+import os
+import json
+import pandas as pd
+import torch
+import time
+import fire
+from glob import glob
+from sentence_transformers import SentenceTransformer
+torch.set_num_threads(48)
+modelname = 'sentence-transformers/all-MiniLM-L6-v2'
+model = SentenceTransformer(modelname)
+
+
+def encode(filename, outname):
+    print(f"encoding {filename} -> {outname}")
+    content = []
+    title = []
+    PMID = []
+    with open(filename) as f:
+        for line in f.readlines():
+            d = json.loads(line)
+            content.append(d["content"])
+            title.append(d["title"])
+            PMID.append(d["PMID"])
+            #d["ID"], d["PMID"]
+
+    print("encoding 'content' -- {} entries".format(len(content)))
+    st = time.time()
+    Xcontent = model.encode(content)
+    print("finished in {}s".format(time.time() - st))
+    print("encoding 'title' -- {} entries".format(len(title)))
+    st = time.time()
+    Xtitle = model.encode(title)
+    print("finished in {}s".format(time.time() - st))
+    with h5.File(outname, "w") as f:
+        f["model"] = modelname
+        f["content"] = Xcontent
+        f["title"] = Xtitle
+        f["PMID"] = PMID
+
+
+def encode_pubmed(files, outdir="pubmed-embeddings"):
+    os.makedirs(outdir, exist_ok=True)
+
+    with open(files) as f:
+        for filename in f.readlines():
+            filename = filename.rstrip()
+            outname = "{}/{}.h5".format(outdir, os.path.basename(filename).replace(".jsonl", ""))
+            if os.path.isfile(outname):
+                print(f"{outname} already exists")
+            else:
+                encode(filename, outname)
+
+
+def main():
+    fire.Fire(encode_pubmed)
+
+
+if __name__ == "__main__":
+    main()