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Description
This repository showcases an implementation of model parallel autoregressive transformers utilizing GPUs, leveraging the capabilities of the DeepSpeed library. It serves as a record of EleutherAI's framework designed for training extensive language models on GPU architecture. Currently, it builds upon NVIDIA's Megatron Language Model, enhanced with advanced techniques from DeepSpeed alongside innovative optimizations. Our goal is to create a centralized hub for aggregating methodologies related to the training of large-scale autoregressive language models, thereby fostering accelerated research and development in the field of large-scale training. We believe that by providing these resources, we can significantly contribute to the progress of language model research.
Description
We introduce T5, a model that transforms all natural language processing tasks into a consistent text-to-text format, ensuring that both inputs and outputs are text strings, unlike BERT-style models which are limited to providing either a class label or a segment of the input text. This innovative text-to-text approach enables us to utilize the same model architecture, loss function, and hyperparameter settings across various NLP tasks such as machine translation, document summarization, question answering, and classification, including sentiment analysis. Furthermore, T5's versatility extends to regression tasks, where it can be trained to output the textual form of a number rather than the number itself, showcasing its adaptability. This unified framework greatly simplifies the handling of diverse NLP challenges, promoting efficiency and consistency in model training and application.
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Free
Open source
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Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
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Training Docs
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Vendor Details
Company Name
EleutherAI
Founded
2020
Website
github.com/EleutherAI/gpt-neox
Vendor Details
Company Name
Founded
1998
Country
United States
Website
ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html