This story was originally published on HackerNoon at: https://hackernoon.com/effective-anomaly-detection-pipeline-for-amazon-reviews-references-and-appendix.
Explore findings from a study on an anomaly detection pipeline for Amazon reviews using MPNet embeddings.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #transformers, #anomaly-detection, #nlp-for-anomaly-detection, #explainability-in-ml, #machine-learning-classifiers, #text-specific-ad-models, #text-encoding-techniques, #explainable-ai, and more.

This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page, and for more stories, please visit hackernoon.com.

This study introduces an effective pipeline for detecting anomalous Amazon reviews using MPNet embeddings. It evaluates SHAP, term frequency, and GPT-3 for explainability, revealing user preferences and computational challenges. Future research may explore broader surveys and integrating GPT-3 throughout the pipeline for enhanced performance.

This story was originally published on HackerNoon at: https://hackernoon.com/breaking-down-gpu-vram-consumption.
What factors influence VRAM consumption? How does it vary with different model settings? I dug into the topic and conducted my measurements.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #llms, #vram, #machine-learning, #deep-learning, #gpus, #gpu-vram, #gpus-for-machine-learning, #gpu-optimization, and more.

This story was written by: @furiousteabag. Learn more about this writer by checking @furiousteabag's about page, and for more stories, please visit hackernoon.com.

I’ve always been curious about the GPU VRAM required for training and fine-tuning transformer-based language models. What factors influence VRAM consumption? How does it vary with different model settings? I dug into the topic and conducted my measurements.

This story was originally published on HackerNoon at: https://hackernoon.com/effective-anomaly-detection-pipeline-for-amazon-reviews-references-and-appendix.
Explore findings from a study on an anomaly detection pipeline for Amazon reviews using MPNet embeddings.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #transformers, #anomaly-detection, #nlp-for-anomaly-detection, #explainability-in-ml, #machine-learning-classifiers, #text-specific-ad-models, #text-encoding-techniques, #explainable-ai, and more.

This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page, and for more stories, please visit hackernoon.com.

This study introduces an effective pipeline for detecting anomalous Amazon reviews using MPNet embeddings. It evaluates SHAP, term frequency, and GPT-3 for explainability, revealing user preferences and computational challenges. Future research may explore broader surveys and integrating GPT-3 throughout the pipeline for enhanced performance.