t5 11b

Introduction

The Text-To-Text Transfer Transformer (T5) is a language model developed by Google researchers, featuring a unified text-to-text framework. It allows for the application of the same model, loss function, and hyperparameters across various NLP tasks such as machine translation, summarization, and question answering. The T5-11B model variant contains 11 billion parameters and supports multiple languages, including English, French, Romanian, and German.

Architecture

T5 reframes NLP tasks into a text-to-text format, contrasting with BERT-style models that output class labels or input spans. This approach simplifies using a single model architecture for diverse tasks. It includes a comprehensive training setup that uses both unsupervised and supervised objectives, leveraging datasets like C4 and Wiki-DPR.

Training

The T5 model is pre-trained on the Colossal Clean Crawled Corpus (C4) and involves a multi-task approach. It combines unsupervised denoising objectives with supervised text-to-text language modeling tasks. The training encompasses several datasets across different NLP tasks, such as sentiment analysis, paraphrasing, natural language inference, and question answering.

Guide: Running Locally

1. Install Requirements: Ensure you have Python and PyTorch installed. You can use pip to install the Hugging Face Transformers library:

   pip install transformers
   

2. Load the Model: Load the T5-11B model using the Transformers library. Due to its size, ensure you have sufficient memory or employ model parallelism or DeepSpeed's ZeRO-Offload:

   from transformers import T5ForConditionalGeneration
   
   model = T5ForConditionalGeneration.from_pretrained('t5-11b')
   

3. GPU Requirements: A single GPU typically lacks sufficient memory for T5-11B. Utilize cloud GPUs like Google Cloud TPU Pods or AWS instances for better performance.

   • Consider model parallelism or DeepSpeed's ZeRO-Offload techniques to manage memory usage.

4. Additional Resources: Consult the Hugging Face T5 documentation and the Colab Notebook for implementation details.

License

The T5 model is released under the Apache 2.0 License, allowing for broad use and modification with proper attribution.