AI EVALUATION, IN CONTEXT.BY EQUALITY MACHINE

Claire Radar

DistilBERT ONNX Quantization Benchmark

An end-to-end ONNX quantization benchmark for a DistilBERT sentiment classifier, comparing PyTorch FP32, ONNX FP32, dynamic INT8, and static INT8 variants on accuracy, latency, and model size using a fixed 200-sample SST-2 validation set.

Evidence available for this benchmark

Numeric results have not been collected for this source record. Use the paper, code or dataset below to check the task and experiments; this does not imply that the original work contains no results.

Start with these resources

Cited documents and dates (1)

Catalog collected on 2026-10-14; resources and fields reflect that saved source.

Recorded attention

Observed on: 2026-10-14

GitHub stars
0
Star change · 30 days before observation
0

parth-udawant/distilbert-onnx-quantization-benchmark · View source snapshot

Dated attention observations, not live counts or benchmark scores.

Evaluation context and evidence

Source
Claire Radar
Publisher
Not recorded
Benchmark release
2026-09-06
Collected
2026-10-14
Modality
Not recorded

These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.

Reported results 0

This source record has no numeric results in our collection. That does not mean a score of zero or that no public results exist. Consult the original resources above.