| language: | |
| - en | |
| - fr | |
| - ro | |
| - de | |
| - multilingual | |
| license: apache-2.0 | |
| tags: | |
| - int8 | |
| - summarization | |
| - translation | |
| datasets: | |
| - c4 | |
| ## [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) | |
| ``` | |