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683a8cb | 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 | import tempfile
from pathlib import Path
from adapters import AutoAdapterModel
from optimum.onnxruntime import (
ORTModelForFeatureExtraction,
ORTModelForSequenceClassification,
)
from transformers import AutoTokenizer
def export_to_onnx():
output_dir = Path("/app/models/")
output_dir.mkdir(exist_ok=True)
print("\n--- 1. Exporting BGE-Small (Bi-Encoder) ---")
bge_id = "BAAI/bge-small-en-v1.5"
bge_dir = output_dir / "bge_small"
bge_path = bge_dir / "model.onnx"
if bge_path.exists():
print(f"BGE-Small ONNX model already exists at {bge_path}. Skipping.")
else:
print(f"Downloading and converting {bge_id}...")
bge_model = ORTModelForFeatureExtraction.from_pretrained(bge_id, export=True)
bge_tokenizer = AutoTokenizer.from_pretrained(bge_id)
bge_model.save_pretrained(bge_dir)
bge_tokenizer.save_pretrained(bge_dir)
print(f"BGE-Small successfully saved to {bge_dir}\n")
print("\n--- 2. Exporting PubMedBERT (Cross-Encoder) ---")
# --- SWAPPED TO PUBMEDBERT HERE ---
pubmed_id = "pritamdeka/PubMedBERT-MNLI-MedNLI"
pubmed_dir = output_dir / "pubmedbert"
pubmed_path = pubmed_dir / "model.onnx"
if pubmed_path.exists():
print(
f"PubMedBERT ONNX model already exists at {pubmed_path}. Skipping export."
)
else:
print(f"Downloading and converting {pubmed_id}...")
# ORTModelForSequenceClassification keeps the classification head
pubmed_model = ORTModelForSequenceClassification.from_pretrained(
pubmed_id, export=True
)
pubmed_tokenizer = AutoTokenizer.from_pretrained(pubmed_id)
pubmed_model.save_pretrained(pubmed_dir)
pubmed_tokenizer.save_pretrained(pubmed_dir)
print(f"PubMedBERT successfully saved to {pubmed_dir}\n")
if __name__ == "__main__":
export_to_onnx()
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