
## [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)
```

{"language": ["en", "fr", "ro", "de", "multilingual"], "license": "apache-2.0", "tags": ["int8", "summarization", "translation"], "datasets": ["c4"]}
transformers onnx t5 text2text-generation int8 summarization translation en fr ro de multilingual dataset:c4 arxiv:1910.10683 license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #onnx #t5 #text2text-generation #int8 #summarization #translation #en #fr #ro #de #multilingual #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


## t5-small exported to the ONNX format and dynamically quantized. 

## Model description

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

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*.



## t5-small exported to the ONNX format and dynamically quantized. ## Model description

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

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*.
TAGS
#transformers #onnx #t5 #text2text-generation #int8 #summarization #translation #en #fr #ro #de #multilingual #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ## t5-small exported to the ONNX format and dynamically quantized. ## Model description

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

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*.
passage: TAGS
#transformers #onnx #t5 #text2text-generation #int8 #summarization #translation #en #fr #ro #de #multilingual #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
## t5-small exported to the ONNX format and dynamically quantized.## Model description

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

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*.