Electra-Bert-Base-Discrim-Google: Optimized for Qualcomm Devices
ELECTRABERT is a lightweight BERT model designed for efficient self-supervised learning of language representations. It can be used for identify unnatural or artificially modified text and as a backbone for various NLP tasks.
This is based on the implementation of Electra-Bert-Base-Discrim-Google found here. This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here.
Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up to run these models on a hosted Qualcomm® device.
Getting Started
There are two ways to deploy this model on your device:
Option 1: Download Pre-Exported Models
Below are pre-exported model assets ready for deployment.
| Runtime | Precision | Chipset | SDK Versions | Download |
|---|---|---|---|---|
| ONNX | float | Universal | QAIRT 2.42, ONNX Runtime 1.24.1 | Download |
| QNN_DLC | float | Universal | QAIRT 2.43 | Download |
| TFLITE | float | Universal | QAIRT 2.43, TFLite 2.17.0 | Download |
For more device-specific assets and performance metrics, visit Electra-Bert-Base-Discrim-Google on Qualcomm® AI Hub.
Option 2: Export with Custom Configurations
Use the Qualcomm® AI Hub Models Python library to compile and export the model with your own:
- Custom weights (e.g., fine-tuned checkpoints)
- Custom input shapes
- Target device and runtime configurations
This option is ideal if you need to customize the model beyond the default configuration provided here.
See our repository for Electra-Bert-Base-Discrim-Google on GitHub for usage instructions.
Model Details
Model Type: Model_use_case.text_generation
Model Stats:
- Model checkpoint: google/electra-base-discriminator
- Input resolution: 1x384
- Number of parameters: 109M
- Model size (float): 417 MB
Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |
|---|---|---|---|---|---|---|
| Electra-Bert-Base-Discrim-Google | ONNX | float | Snapdragon® X Elite | 23.323 ms | 220 - 220 MB | NPU |
| Electra-Bert-Base-Discrim-Google | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 16.175 ms | 0 - 490 MB | NPU |
| Electra-Bert-Base-Discrim-Google | ONNX | float | Qualcomm® QCS8550 (Proxy) | 22.641 ms | 0 - 227 MB | NPU |
| Electra-Bert-Base-Discrim-Google | ONNX | float | Qualcomm® QCS9075 | 28.611 ms | 0 - 3 MB | NPU |
| Electra-Bert-Base-Discrim-Google | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 10.878 ms | 0 - 481 MB | NPU |
| Electra-Bert-Base-Discrim-Google | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 7.681 ms | 0 - 479 MB | NPU |
| Electra-Bert-Base-Discrim-Google | ONNX | float | Snapdragon® X2 Elite | 8.495 ms | 220 - 220 MB | NPU |
| Electra-Bert-Base-Discrim-Google | QNN_DLC | float | Snapdragon® X Elite | 18.146 ms | 0 - 0 MB | NPU |
| Electra-Bert-Base-Discrim-Google | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 11.972 ms | 0 - 497 MB | NPU |
| Electra-Bert-Base-Discrim-Google | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 66.956 ms | 0 - 419 MB | NPU |
| Electra-Bert-Base-Discrim-Google | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 17.268 ms | 0 - 2 MB | NPU |
| Electra-Bert-Base-Discrim-Google | QNN_DLC | float | Qualcomm® SA8775P | 98.824 ms | 0 - 419 MB | NPU |
| Electra-Bert-Base-Discrim-Google | QNN_DLC | float | Qualcomm® QCS9075 | 22.03 ms | 0 - 2 MB | NPU |
| Electra-Bert-Base-Discrim-Google | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 24.905 ms | 0 - 449 MB | NPU |
| Electra-Bert-Base-Discrim-Google | QNN_DLC | float | Qualcomm® SA7255P | 66.956 ms | 0 - 419 MB | NPU |
| Electra-Bert-Base-Discrim-Google | QNN_DLC | float | Qualcomm® SA8295P | 26.531 ms | 0 - 374 MB | NPU |
| Electra-Bert-Base-Discrim-Google | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 8.145 ms | 0 - 446 MB | NPU |
| Electra-Bert-Base-Discrim-Google | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 5.732 ms | 0 - 450 MB | NPU |
| Electra-Bert-Base-Discrim-Google | QNN_DLC | float | Snapdragon® X2 Elite | 6.911 ms | 0 - 0 MB | NPU |
| Electra-Bert-Base-Discrim-Google | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 11.95 ms | 0 - 496 MB | NPU |
| Electra-Bert-Base-Discrim-Google | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 66.942 ms | 0 - 420 MB | NPU |
| Electra-Bert-Base-Discrim-Google | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 17.259 ms | 0 - 3 MB | NPU |
| Electra-Bert-Base-Discrim-Google | TFLITE | float | Qualcomm® SA8775P | 21.36 ms | 0 - 420 MB | NPU |
| Electra-Bert-Base-Discrim-Google | TFLITE | float | Qualcomm® QCS9075 | 22.27 ms | 0 - 214 MB | NPU |
| Electra-Bert-Base-Discrim-Google | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 24.972 ms | 0 - 449 MB | NPU |
| Electra-Bert-Base-Discrim-Google | TFLITE | float | Qualcomm® SA7255P | 66.942 ms | 0 - 420 MB | NPU |
| Electra-Bert-Base-Discrim-Google | TFLITE | float | Qualcomm® SA8295P | 26.475 ms | 0 - 375 MB | NPU |
| Electra-Bert-Base-Discrim-Google | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 8.084 ms | 0 - 452 MB | NPU |
| Electra-Bert-Base-Discrim-Google | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 5.751 ms | 0 - 453 MB | NPU |
License
- The license for the original implementation of Electra-Bert-Base-Discrim-Google can be found here.
References
- ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
- Source Model Implementation
Community
- Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
- For questions or feedback please reach out to us.
