language data
Purchase TAUS's exclusive data collection, featuring close to 7.4 billion words, covering 483 language pairs, now available at discounts exceeding 95% of the original value.
Explore the crucial role of language data in training and fine-tuning LLMs and GenAI, ensuring high-quality, context-aware translations, fostering the symbiosis of human and machine in the localization sector.
Domain Adaptation can be classified into three types - supervised, semi-supervised, and unsupervised - and three methods - model-centric, data-centric, or hybrid.
Machine learning and AI applications need data in order to work. And in order to get good results and output, the cleaner the data, the better.
Text Summarization can be categorized under two types: Extraction and Abstraction. With the power of AI, summarization is becoming more popular and accessible.
Synthetic parallel data generation by back-translation as a solution for the problem of translating low-resource languages and texts from low-resource domains.
The implementation of AI & ML algorithms and computation techniques are helping to improve the accuracy of recognizing speech into text
It is crucial to choose the right audio transcription type between verbatim, edited, intelligent, and phonetic, to best suit your transcription project needs
Natural Language Technologies are on the rise: making optimal use of NLT and its subcategories is crucial to remain up-to-date with the latest AI solutions
What can word clouds driven by NLP tell you about your training datasets? Here is how we create word clouds on TAUS Data Marketplace.
The next logical translation solution: Data Enhanced Machine Translation (DEMT)
Which language data for AI trends you should expect to rise in 2022: expansion of multilingual AI data and models, more companies joining the data market, data diversity and lifelong learning machines.
A thorough overview of the paper by six Google researchers: Data Cascades in High-Stakes AI with a focus on why data-centric AI matters.
Explaining what Explainable AI (XAI) entails and diving into five major XAI techniques for Natural Language Processing (NLP).
A brief definition of what training data is.
Reasons why training data is important for AI and ML practices.
A brief introduction to types of training data including structured, unstructured, and semi-structured data.
Here are some pointers on how much training data do you need to train your ML models.
Data cleaning and data anonymization are very critical in training ML models. Here are the reasons why.
Training data can be sourced via synthetic data generation, public datasets, data marketplaces, and crowd-sourced platforms.