Difference between revisions of "Benchmarks"
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* [[Evaluation Measures - Classification Performance]] - [[Evaluation Measures - Classification Performance#Accuracy|Accuracy]], [[Evaluation Measures - Classification Performance#Precision & Recall (Sensitivity)|Precision & Recall (Sensitivity)]], and [[Evaluation Measures - Classification Performance#Specificity|Specificity]] | * [[Evaluation Measures - Classification Performance]] - [[Evaluation Measures - Classification Performance#Accuracy|Accuracy]], [[Evaluation Measures - Classification Performance#Precision & Recall (Sensitivity)|Precision & Recall (Sensitivity)]], and [[Evaluation Measures - Classification Performance#Specificity|Specificity]] | ||
* [[Datasets]] | * [[Datasets]] | ||
| + | * [http://towardsdatascience.com/benchmarking-simple-machine-learning-models-with-feature-extraction-against-modern-black-box-80af734b31cc Benchmarking simple models with feature extraction against modern black-box methods | Martin Dittgen - Towards Data Science] | ||
* [http://dawn.cs.stanford.edu//benchmark/index.html DAWNBench | Stanford] - an End-to-End Deep Learning Benchmark and Competition | * [http://dawn.cs.stanford.edu//benchmark/index.html DAWNBench | Stanford] - an End-to-End Deep Learning Benchmark and Competition | ||
Revision as of 10:46, 24 December 2019
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- Evaluation Measures - Classification Performance - Accuracy, Precision & Recall (Sensitivity), and Specificity
- Datasets
- Benchmarking simple models with feature extraction against modern black-box methods | Martin Dittgen - Towards Data Science
- DAWNBench | Stanford - an End-to-End Deep Learning Benchmark and Competition
GLUE
The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems. GLUE consists of: A benchmark of nine sentence- or sentence-pair language understanding tasks built on established existing datasets and selected to cover a diverse range of dataset sizes, text genres, and degrees of difficulty, A diagnostic dataset designed to evaluate and analyze model performance with respect to a wide range of linguistic phenomena found in natural language, and A public leaderboard for tracking performance on the benchmark and a dashboard for visualizing the performance of models on the diagnostic set.
