Downloads · 30 days
43
13% of all-time downloads
justinchuby/onnx-genai-example-prot-bert
onnx-genai-example-prot-bert is a machine learning model from justinchuby. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as other.
Private real-weight ONNX package produced by Mobius from Rostlab/protbert at immutable revision 7a894481acdc12202f0a415dd567f6cfdb698908. Source license: not declared in pinned upstream model card/repository.
Downloads · 30 days
43
13% of all-time downloads
All-time downloads
341
Public
Repo size
1.7 GB
Likes
0
Public
Click a slice to open those files.
.data1.7 GB · 100%
From the Hugging Face model README
Private real-weight ONNX package produced by Mobius from
Rostlab/prot_bert at immutable revision
7a894481acdc12202f0a415dd567f6cfdb698908. Source license: not declared in pinned upstream model card/repository.
inference_metadata.yamlrequest.json and output.json real runtime evidencegraph_report.json, performance.json, source.json, and provenance.jsonObserved output: embedding norm 6.334549427032471
hf download justinchuby/onnx-genai-example-prot-bert --repo-type model --local-dir ./prot-bert
python - <<'PY'
from pathlib import Path
import onnxruntime as ort
root = Path("prot-bert")
for relative_path in ['model.onnx']:
session = ort.InferenceSession(
str(root / relative_path),
providers=['CPUExecutionProvider'],
)
print(relative_path, session.get_providers(), [x.name for x in session.get_inputs()])
PY
The exact successful probe request, output, versions, providers, and timings are
preserved in request.json, output.json, and performance.json.
Review inference_metadata.annotated.yaml for inline explanations of this package's workflow, tensor/state/cache contracts, and fail-closed omissions. inference_metadata.yaml remains the canonical machine-authored contract; automated validation confirms both files parse to the same metadata object.