NLP
Conditional Text Generation by Fine Tuning Gretel GPT
Augment machine learning datasets with synthetically generated text and labels using an open-source implementation of GPT-3.
Read more...Innovating With FastText and Table Headers
Look at how FastText word embeddings can help to quickly understand new datasets, and build more consistent labels for your own data.
Read more...Automated Data Exposure Detection with Gretel Outpost
Gretel Outpost is a free integration architecture that automates the steps that a security team would take in assessing the risk or exposure to data.
Read more...Automate Detecting Sensitive Personally Identifiable Information (PII)
Use Gretel.ai's APIs to continuously detect and protect sensitive data including credit cards, credentials, names, and addresses.
Read more...Exploring NLP Part 2: A New Way to Measure the Quality of Synthetic Text
By merging breakthrough research on text metrics with new types of embeddings, we produce a reliable metric that is highly correlated with human ratings.
Read more...Exploring NLP Part 1: Why Should a Privacy Engineering Company Care About NLP?
There is a lot of hype around NLP. In this post, we explore some of the criticisms and how you can use this technology responsibly.
Read more...Got text? Use Named Entity Recognition (NER) to label PII in your data
Use Gretel’s NLP setting to label PII including people names and geographic locations in free text.
Read more...Unlocking Adapted LLMs on Enterprise Data
Gretel GPT supports new, state-of-the-art LLMs, and makes it easier for you to trust the privacy and accuracy of LLMs for enterprise use-cases.
Read more...Fine-tune a MPT-7B LLM with Gretel GPT
Learn how to fine-tune and prompt mpt-7b to generate responses matching popular Twitter personalities with Gretel GPT.
Read more...Synthesizing dialogs for better conversational AI
Create high-quality synthetic datasets of conversational dialogs safely tuned on your private, sensitive data with Gretel.
Read more...