Google on English AI Terms Dictionary

Gemma

Sat, 18 Jul 2026 09:59:20 +0000

Definition

Gemma models are designed to be efficient and accessible for researchers and developers. They come in various sizes, including 2B and 7B parameter versions, allowing for deployment on diverse hardware. The models leverage the advanced techniques used in the larger Gemini series but are optimized for lower computational costs. This makes them suitable for tasks like text generation, coding assistance, and general reasoning while maintaining high performance relative to their size.

Dataset:Natural Questions

Sat, 18 Jul 2026 09:53:44 +0000

Definition

Natural Questions (NQ) is a benchmark dataset introduced by Google to advance research in open-domain question answering. It maps real, anonymized search queries from Google to long-form answers found within Wikipedia articles. The dataset includes both ‘short answers’ (specific spans of text) and ’long answers’ (paragraphs containing the short answer). It is essential for training models that can retrieve and synthesize information from vast knowledge bases to answer complex, real-world questions.

Dataset:Gooaq

Sat, 18 Jul 2026 09:53:29 +0000

Definition

GooAQ is a dataset compiled from the Google Answers service, featuring a massive collection of user-submitted questions along with detailed, paid responses. It serves as a valuable resource for training models in open-domain question answering and information retrieval. The diversity of topics and the structured nature of the Q&A pairs allow researchers to develop systems capable of understanding complex user intents and retrieving relevant factual information from vast corpora.

Bert

Sat, 18 Jul 2026 09:48:19 +0000

Definition

BERT is a transformer-based machine learning technique for NLP pre-training developed by Google. It uses masked language modeling and next sentence prediction to learn bidirectional representations from text. This allows BERT to understand context from both left and right directions simultaneously, significantly improving performance on tasks like question answering and sentiment analysis compared to unidirectional models.

Summary

Bidirectional Encoder Representations from Transformers is a pre-trained natural language processing model.