Flan-T5 is a large language model (LLM) developed by Google researchers in 2022 and has been fine-tuned on multiple tasks.
Used in artificial intelligence (AI) applications, Flan-T5 can be used in various natural language processing (NLP) tasks, such as summarizing long conversations into more concise summaries, or to classify text into different categories, such as spam or not-spam, positive or negative, as well as politics, sports, or entertainment. This may be helpful in applications designed for content moderation, customer support, r personalized recommendations. It can also be used to create applications to extract information from electronic health records and other healthcare documents.
Flan-T5 is an open-source language model, licensed under the Apache 2.0 license, although it may be used for commercial applications.
Flan-T5 is based on Google's T-5 (Test-To-Text Transfer Transformer) model, which used a text-to-text approach.
Unlike GPT-3, Flan-T5 does not require large devices or a huge computing budget because its smaller models and checkpoints are created for the common citizen. Flan-T5 is also capable of detecting sarcasm, and is very intuitive; it is able to reinterpret the questions.
Currently, Google has released five versions: Flan-T5-small, Flan-T5-base, Flan-T5-large, Flan-T5-XL, and Flan-T5-XXL.
Flan-T5 is capable of translating between more than sixty languages. It can answer general questions, as well as historical questions and speculations as to the future. Flan-T5 is also capable of solving math problems when giving the reasoning.
Necessary to achieve the full potential of Flan-T5, fine-tuning adapts the model to specific tasks, allowing for customization of the model to better suit a user's specific needs and data. As this can be done on a local workstation with a CPU, Flan-T5 is accessible to a wider range of users. Fine-tuning requires a variety of libraries and tools, which may be found through some of the resources listed below.
FLAN stands for "Fine-tuned LAnguage Net," while T-5 stands for "Text-To-Text Transfer Transformer."
This portion of our web guide focuses on FLAN-T5.
Recommended Resources
Datacamp is an online platform offering courses in data science and artificial intelligence skills, including programming languages. Hosted on Datacamp and created by Zoumana Keita, this is a guide to fine-tuning a FLAN-T5 model for question-answering tasks using a transformers library and running optimized inference on a real-world scenario. An overview of FLAN-T5 is provided, along with potential applications of fine-tuned FLAN-T5, and instructions for fine-tuning it.
https://www.datacamp.com/tutorial/flan-t5-tutorial
Flan-T5 is a fine-tuned version of the T5 language model and an open-source alternative to large language models like GPT-3 and GPT-4. In this Paperspace notebook, you'll learn to use Flan-T5 for common NLP tasks, such as text generation, sentiment analysis, advanced named entity recognition, question answering, intent classification, summarization, and text classification. With an existing Flan-T5 application based on Hugging Face, only 2 lines of code need to be changed to run it on IPUs.
https://github.com/graphcore/flan-t5
FLAN-T5-base is a pre-trained language model developed by Google researchers that is based on the T5 model. It is an encoder-decoder model that has been pre-trained on a mixture of unsupervised and supervised tasks, with the goal of learning mappings between sequences of text, such as text-to-text and has been fine-tuned on more than a thousand tasks covering multiple languages. Instructions are provided and accessible through a table of contents. Text-to-text generation examples are provided.
https://huggingface.co/google/flan-t5-base
FLAN-T5-small is a language model developed by Google AI, which has been trained on more than 1,000 additional tasks covering multiple languages. The T5-small model has 80 million parameters and is capable of performing better than the original T55 model in zero-shot and few-shot learning NLP tasks. It has been trained on TOU v3 or TPU v4 pods, using T5x codebase together with Jax. Text-to-text generation examples in its Inference API are available online.
https://huggingface.co/google/flan-t5-small
FLAN-T5 XXL is a model card built on top of PyTorch to train advanced deep-learning models. The largest version of FLAN-T5 contains 11 billion parameters. The model is based on pre-trained T5 and fine-tuned with instructions for better zero-shot and few-shot performance. There is one fine-tuned FLAN model per T5 model size, and this model has been trained on TPU v3 or TPU v4 pods using the T5x codebase together with Jax. Inference API text-to-text generation examples are provided.
https://huggingface.co/google/flan-t5-xxl
What is FLAN-T5? Is FLAN-T5 a better alternative to GPT-3?
Exemplary AI is a hub where humans and AI collaborate to transform content creation, making it accessible, personalized, and globally relevant with the human touch. This article discusses the attributes of FLAN-T5, and, in particular, comparisons with FLAN-T5 and other Large Language Models and their models, particularly GPT-3. An overview of FLAN-T5 is given, including its use of prompting techniques, examples of potential use-cases, and its limitations or drawbacks.
https://exemplary.ai/blog/flan-t5
