Taylor @TryTaylor_AI
Use our API to label and build products around your text | 🟧 Backed by @ycombinator & @generalcatalyst | https://t.co/n8yRPvGWB3 trytaylor.ai San Francisco Joined June 2023-
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We developed a taxonomy mapping algorithm. Build connections to any taxonomy. Happy Friday ✨ trytaylor.ai/blog/crosswalk
Content Classification with IAB, IPTC, and GARM Brand Safety tags
✨ Introducing Data Tables ✨ Transform & build on unstructured text by running multiple text classifiers and entity extractions simultaneously
we've been cooking on entity resolution 👩🍳 in this demo, we extract all the majors/degrees from a messy resume and resolve them to the government CIP taxonomy 🤯 i think we are accidentally building a resume parser RIP 💀
With Taylor, you can build as many classifiers and entity extractions as you want for free. You only pay for usage once you're satisfied with results 😁
The biggest challenge for us has been making classifiers as frictionless as possible for both business & eng teams. The latest updates to our classifier creation form is 1 more step towards breaking down the barrier between ML & business users!
the AI race has never been faster. if you're not shipping, you're falling behind OpenAI. that's why we also shipped a blog post today! 😉 check it out to learn how we automatically extract skills from job postings & resolve to a skills taxonomy. link below 🚀🚀 🚀
no one's talking about the 86M param moderation model meta released this week!! so i decided to talk about it. read my yapping about PromptGuard below 😁
If you want to extract key information from documents, but you're struggling to deduplicate misspellings, alternative names for the same thing, etc. this might be just what you need! Our entity extraction deduplicates and matches entities to your set of unique labels.
It's been awesome watching @andersonbcdefg cook using Modal! "The ability to spawn jobs from one Python environment that run in a totally different Python environment (different packages, more CPUs, GPUs, etc.) is a great benefit to machine learning engineers."
It's been awesome watching @andersonbcdefg cook using Modal! "The ability to spawn jobs from one Python environment that run in a totally different Python environment (different packages, more CPUs, GPUs, etc.) is a great benefit to machine learning engineers." https://t.co/lWZyV1sGvv
We are so excited to share more about our partnership with @modal_labs , a serverless platform for high-performance computing. Check out our blog for how we use Modal to build & deploy high accuracy text classification models for our users. trytaylor.ai/blog/modal
So excited for everyone in @ycombinator's S24 batch. Congrats and good luck! You got this 💪 In honor of S24, here's a frame from our S23 application video. @andersonbcdefg
Here's a case study on job aggregators who use the Taylor API to classify millions of job postings every month! trytaylor.ai/blog/jds
Introducing our entity extraction ⛏️ We'll help you manage, deduplicate, and add new entities as they occur.
✅ We can sort messy text into structured codes 😣 We cannot sort out dad jokes Happy Father's Day!
The most common feature request we received in May: Mapping 2 taxonomies together. If you want to connect 2 taxonomies, we got you 😁 We''ll build a many-to-many mapping between taxonomy A and taxonomy B based on lexical and semantic similarity.
Making your text data usable, queryable, and productizable in less than 10 min 😯 Examples: 🛡 T&S classifications (e.g. labeling content & messages) 📃 Labeling documents (e.g. categorizing contracts, financial docs) 💼 Labeling job postings by O*NET SOC code
One of my biggest pet peeves is leaving my laptop open for a long job 🙄 But using Taylor, any classification job can be run as a "batch job"—upload a file, close your computer, and come back when it's done. We're so fast your computer might not even have time to sleep... 😴
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