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