{"id":317,"date":"2021-08-31T14:39:51","date_gmt":"2021-08-31T14:39:51","guid":{"rendered":"https:\/\/fde.cat\/?p=317"},"modified":"2021-08-31T14:39:51","modified_gmt":"2021-08-31T14:39:51","slug":"a-deep-dive-on-text-classification-at-salesforce","status":"publish","type":"post","link":"https:\/\/fde.cat\/index.php\/2021\/08\/31\/a-deep-dive-on-text-classification-at-salesforce\/","title":{"rendered":"A Deep Dive on Text Classification at Salesforce"},"content":{"rendered":"

published on Towards Data\u00a0Science<\/a><\/h4>\n

Putting from a Sand Trap (Image by\u00a0Author<\/em>)<\/p>\n

We\u2019re excited to announce that Noah Burbank<\/a>, a Principal Data Scientist in Sales Cloud, has recently published a deep dive into text classification at Salesforce on Towards Data Science. The article, How to choose the right model for text classification in an organizational setting<\/a>, applies counterintuitive advice from golf to Data Science: you might be better off skipping the sand wedge and using a putter to get out of sand\u00a0trap.<\/p>\n

In the article Noah goes through the technical trade-offs of three different text classification approaches: Regular Expressions, Machine Learning, and Deep Learning. But after the technical deep dive he reveals his key takeaway: the best model isn\u2019t the one with the highest F1-score, the best model\u00a0is<\/p>\n

one that you can deploy with the resources you have, trained with the data you can get, in the amount of time that your boss has patience\u00a0forRead the full post<\/a> on Towards Data\u00a0Science<\/p>\n

If you enjoy the post, you might want to read some of Noah\u2019s other blog posts on the Salesforce Engineering Blog about <\/em>designing NLP systems<\/em><\/a> or about <\/em>using open source data to preserve customer\u00a0privacy<\/em><\/a>.<\/em><\/p>\n

A Deep Dive on Text Classification at Salesforce<\/a> was originally published in Salesforce Engineering<\/a> on Medium, where people are continuing the conversation by highlighting and responding to this story.<\/p>\n

Read More<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

published on Towards Data\u00a0Science Putting from a Sand Trap (Image by\u00a0Author) We\u2019re excited to announce that Noah Burbank, a Principal Data Scientist in Sales Cloud, has recently published a deep dive into text classification at Salesforce on Towards Data Science. The article, How to choose the right model for text classification in an organizational setting,… Continue reading A Deep Dive on Text Classification at Salesforce<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spay_email":"","footnotes":""},"categories":[7],"tags":[],"class_list":["post-317","post","type-post","status-publish","format-standard","hentry","category-technology","entry"],"jetpack_featured_media_url":"","jetpack-related-posts":[{"id":228,"url":"https:\/\/fde.cat\/index.php\/2021\/02\/02\/realtime-predictions-in-a-multitenant-environment\/","url_meta":{"origin":317,"position":0},"title":"Realtime Predictions in a Multitenant Environment","date":"February 2, 2021","format":false,"excerpt":"Real-time Predictions in a Multitenant EnvironmentIntroductionThe Einstein Vision and Language Platform Team at Salesforce enables data management, training, and prediction for deep learning-based Vision and Language use cases. 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