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T5 Efficient Base Ff9000

Developed by google
T5-Efficient-BASE-FF9000 is a variant of Google's original T5, adopting a deep narrow architecture that delivers superior performance on downstream tasks with similar parameter scales.
Downloads 16
Release Time : 3/2/2022

Model Overview

This is a pre-trained model based on the T5 architecture, utilizing a deep narrow design strategy that prioritizes increasing model depth for enhanced efficiency. The model is pre-trained on the English C4 dataset and is suitable for various English NLP tasks.

Model Features

Deep Narrow Architecture
Adopts a tall and thin (deep and narrow) model design, which is more efficient than the base model, excelling in three key efficiency metrics: parameter count, FLOPs, and speed.
Efficient Pre-training
Pre-trained for 524,288 steps on the large-scale cleaned Common Crawl (C4) dataset using a masked language modeling objective with spans.
Flexible Fine-tuning
Can serve as a base model for fine-tuning on various downstream tasks such as summarization, question answering, and text classification.

Model Capabilities

Text Generation
Text Summarization
Question Answering
Text Classification

Use Cases

Text Generation
Automatic Summarization
Automatically generate concise summaries from long documents
Question Answering
Open-domain Question Answering
Answer user questions based on given text
Text Classification
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