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Add script to evaluate accuracy of BERT/DistilBERT/Roberta models on question-answering task. By default, pretrained model `bert-large-uncased-whole-word-masking-finetuned-squad` will be used if model name is not specified. If onnx path is not specified, optimum will be used to export an ONNX model for testing. Example usage: * Evaluate with CPU execution provider: `python eval_squad.py` * Evaluate with CUDA execution provider: `python eval_squad.py --use_gpu` * Evaluate an optimized onnx model for 'distilbert-base-cased-distilled-squad' with sequence lengths 128/192/256/384 on first 100 samples: `python eval_squad.py -m distilbert-base-cased-distilled-squad --use_gpu -s 128 192 256 384 --onnx_path ./optimized_fp16.onnx -t 100` |
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →
Get Started
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
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Data/Telemetry
Windows distributions of this project may collect usage data and send it to Microsoft to help improve our products and services. See the privacy statement for more details.
Contributions and Feedback
We welcome contributions! Please see the contribution guidelines.
For feature requests or bug reports, please file a GitHub Issue.
For general discussion or questions, please use GitHub Discussions.
Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
License
This project is licensed under the MIT License.