--- pipeline_tag: question-answering datasets: - squad metrics: - exact_match - f1 tags: - distilbert --- **task**: `question-answering` Fixed parameters: * **model_name_or_path**: `distilbert-base-uncased-distilled-squad` * **dataset**: * **path**: `squad` * **name**: `None` * **calibration_split**: `train` * **eval_split**: `validation` * **data_keys**: `{'question': 'question', 'context': 'context'}` * **ref_keys**: `['answers']` * **max_seq_length**: `128` * **node_exclusion**: `[]` * **per_channel**: `False` * **calibration**: * **method**: `minmax` * **num_calibration_samples**: `20` * **calibration_histogram_percentile**: `None` * **calibration_moving_average**: `None` * **calibration_moving_average_constant**: `None` * **framework**: `onnxruntime` * **framework_args**: * **opset**: `15` * **optimization_level**: `1` * **aware_training**: `False` Benchmarked parameters: * **quantization_approach**: `dynamic`, `static` * **operators_to_quantize**: `['Add', 'MatMul']`, `['Add']` ## Evaluation Below, time metrics for * Batch size: 8 * Input length: 128 | quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) | | exact_match (original) | exact_match (optimized) | | f1 (original) | f1 (optimized) | | :-------------------: | :-------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: | :-: | :--------------------: | :---------------------: | :-: | :-----------: | :------------: | | `dynamic` | `['Add', 'MatMul']` | \| | 247.65 | 75.80 | \| | 4.50 | 13.50 | \| | 90.000 | 85.000 | \| | 90.000 | 86.667 | | `dynamic` | `['Add']` | \| | 234.61 | 191.78 | \| | 4.50 | 5.50 | \| | 90.000 | 90.000 | \| | 90.000 | 90.000 | | `static` | `['Add', 'MatMul']` | \| | 238.04 | 131.81 | \| | 4.50 | 8.00 | \| | 90.000 | 50.000 | \| | 90.000 | 59.362 | | `static` | `['Add']` | \| | 241.84 | 257.85 | \| | 4.50 | 4.00 | \| | 90.000 | 70.000 | \| | 90.000 | 76.333 |