File size: 1,699 Bytes
f672783 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | 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()
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