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---
pipeline_tag: token-classification
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
tags:
- distilbert
---
**task**: `token-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.m5.2xlarge', 'supported_instructions': 'avx512'}`
**Number of evaluation samples:** `100`
Fixed parameters:
* **model_name_or_path**: `elastic/distilbert-base-uncased-finetuned-conll03-english`
* **dataset**:
* **path**: `conll2003`
* **eval_split**: `validation`
* **data_keys**: `{'primary': 'tokens'}`
* **ref_keys**: `['ner_tags']`
* **calibration_split**: `train`
* **node_exclusion**: `[]`
* **per_channel**: `False`
* **calibration**:
* **method**: `minmax`
* **num_calibration_samples**: `100`
* **framework**: `onnxruntime`
* **framework_args**:
* **opset**: `11`
* **optimization_level**: `1`
* **aware_training**: `False`
Benchmarked parameters:
* **quantization_approach**: `dynamic`, `static`
* **operators_to_quantize**: `['Add', 'MatMul']`, `['Add']`
# Evaluation
## Non-time metrics
| quantization_approach | operators_to_quantize | | precision (original) | precision (optimized) | | recall (original) | recall (optimized) | | f1 (original) | f1 (optimized) | | accuracy (original) | accuracy (optimized) |
| :-------------------: | :-------------------: | :-: | :------------------: | :-------------------: | :-: | :---------------: | :----------------: | :-: | :-----------: | :------------: | :-: | :-----------------: | :------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 0.974 | 0.974 | \| | 0.955 | 0.949 | \| | 0.964 | 0.962 | \| | 0.990 | 0.989 |
| `dynamic` | `['Add']` | \| | 0.974 | 0.974 | \| | 0.955 | 0.955 | \| | 0.964 | 0.964 | \| | 0.990 | 0.990 |
| `static` | `['Add', 'MatMul']` | \| | 0.974 | 0.081 | \| | 0.955 | 0.222 | \| | 0.964 | 0.118 | \| | 0.990 | 0.467 |
| `static` | `['Add']` | \| | 0.974 | 0.073 | \| | 0.955 | 0.182 | \| | 0.964 | 0.105 | \| | 0.990 | 0.290 |
## Time metrics
Time benchmarks were run for 3 seconds per config.
Below, time metrics for batch size = 1, input length = 64.
| quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 59.35 | 21.91 | \| | 17.00 | 45.67 |
| `dynamic` | `['Add']` | \| | 59.18 | 29.24 | \| | 17.00 | 34.33 |
| `static` | `['Add', 'MatMul']` | \| | 59.25 | 28.31 | \| | 17.00 | 35.33 |
| `static` | `['Add']` | \| | 58.77 | 31.80 | \| | 17.33 | 31.67 |
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