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--- |
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language: |
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- en |
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- fr |
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- ro |
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- de |
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datasets: |
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- c4 |
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tags: |
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- int8 |
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- summarization |
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- translation |
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license: apache-2.0 |
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--- |
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## [t5-small](https://huggingface.co/t5-small) exported to the ONNX format and dynamically quantized. |
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## Model description |
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[T5](https://huggingface.co/docs/transformers/model_doc/t5#t5) is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format. |
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For more information, please take a look at the original paper. |
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Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) |
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Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* |
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## Usage example |
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You can use this model with Transformers *pipeline*. |
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```python |
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from transformers import AutoTokenizer, pipeline |
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from optimum.onnxruntime import ORTModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("echarlaix/t5-small-dynamic") |
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model = ORTModelForSeq2SeqLM.from_pretrained("echarlaix/t5-small-dynamic") |
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translator = pipeline("translation_en_to_fr", model=model, tokenizer=tokenizer) |
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text = "He never went out without a book under his arm, and he often came back with two." |
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results = translator(text) |
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print(results) |
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``` |
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