Merge pull request #68 from whdduq52/mlperf-inference-results-scc24

Results on system <JetStream>

Author: arjunsuresh

open/scc122/code/mobilebert-base_quant-none-bert-99/README.md

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+TBD

open/scc122/measurements/scc122_cpu0.novalocal-reference-cpu-deepsparse_v1.8.0-default_config/README.md

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+| Model   | Scenario   | Accuracy   | Throughput   | Latency (in ms)   | Power Efficiency (in samples/J)   | TEST01   | TEST05   | TEST04   |
+|---------|------------|------------|--------------|-------------------|-----------------------------------|----------|----------|----------|

open/scc122/measurements/scc122_cpu0.novalocal-reference-cpu-deepsparse_v1.8.0-default_config/mobilebert-base_quant-none-bert-99/offline/README.md

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+This experiment is generated using the [MLCommons Collective Mind automation framework (CM)](https://github.com/mlcommons/cm4mlops).
+
+*Check [CM MLPerf docs](https://docs.mlcommons.org/inference) for more details.*
+
+## Host platform
+
+* OS version: Linux-6.1.110-1.el9.elrepo.x86_64-x86_64-with-glibc2.34
+* CPU version: x86_64
+* Python version: 3.9.18 (main, Aug 23 2024, 00:00:00) 
+[GCC 11.4.1 20231218 (Red Hat 11.4.1-3)]
+* MLCommons CM version: 3.2.7
+
+## CM Run Command
+
+See [CM installation guide](https://docs.mlcommons.org/inference/install/).
+
+```bash
+pip install -U cmind
+
+cm rm cache -f
+
+cm pull repo mlcommons@cm4mlops --checkout=358347c4a4876921fca310d9c54ed6495d50b7d1
+
+cm run script \
+	- \
+	- \
+	a \
+	d \
+	r \
+	. \
+	p \
+	y \
+	t \
+	h \
+	o \
+	n \
+	. \
+	n \
+	a \
+	m \
+	e \
+	= \
+	m \
+	l \
+	p \
+	e \
+	r \
+	f
+```
+*Note that if you want to use the [latest automation recipes](https://docs.mlcommons.org/inference) for MLPerf (CM scripts),
+ you should simply reload mlcommons@cm4mlops without checkout and clean CM cache as follows:*
+
+```bash
+cm rm repo mlcommons@cm4mlops
+cm pull repo mlcommons@cm4mlops
+cm rm cache -f
+
+```
+
+## Results
+
+Platform: scc122_cpu0.novalocal-reference-cpu-deepsparse_v1.8.0-default_config
+
+Model Precision: int8
+
+### Accuracy Results 
+`F1`: `90.81269`, Required accuracy for closed division `>= 89.96526`
+
+### Performance Results 
+`Samples per second`: `16.1242`