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license: apache-2.0 |
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# Graphcore/t5-small-ipu |
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Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore). |
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Through HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project. |
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## Model description |
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Text-to-Text Transfer Transformer (T5), is a Transformer based model that uses a text-to-text approach for translation, question answering, and classification. It introduces an unified framework that converts all text-based language problems into a text-to-text format for transfer learning for NLP. This allows for the use of the same model, loss function, hyperparameters, etc. across our diverse set of tasks. |
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Paper link :[Exploring the Limits of Transfer Learning with a Unified |
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Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) |
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## Intended uses & limitations |
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This model contains just the `IPUConfig` files for running the T5 Small model (e.g. [HuggingFace/t5-small](https://huggingface.co/t5-small)) on Graphcore IPUs. |
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**This model contains no model weights, only an IPUConfig.** |
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## Usage |
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``` |
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from optimum.graphcore import IPUConfig |
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ipu_config = IPUConfig.from_pretrained("Graphcore/t5-small-ipu") |
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``` |
