source languages: nl; target languages: en; OPUS readme: nl-en; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.
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.
Parameters—
Context—
Weights593.8 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads7.6k
Model Card
By Hugging Face Optimum, published under apache-2.0, revision c5c8ea1bc3f5.
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 You can use this model with Transformers pipeline.
Read Hugging Face Optimum's full model card
t5-small exported to the ONNX format
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.
from transformers import AutoTokenizer, pipeline
from optimum.onnxruntime import ORTModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("optimum/t5-small")
model = ORTModelForSeq2SeqLM.from_pretrained("optimum/t5-small")
translator = pipeline("translation_en_to_fr", model=model, tokenizer=tokenizer)
results = translator("My name is Eustache and I have a pet raccoon")
print(results)
Configuration
- Architecture
- T5ForConditionalGeneration
- Vocabulary size
- 32,128
- Model type
- t5
Identity and Version
- Repository
- optimum/t5-small
- Publisher
- Hugging Face Optimum
- Task
- Translation
- Modality
- Text
- Library
- transformers
- Parameters
- Not stated by the source
- Languages
- en, fr, ro, de
- Revision
- c5c8ea1bc3f5940643d88d792094e851fa80e06f
- First published
- 2022-06-20
- Last updated
- 2023-01-19
Files and Weights
9 files, 596.2 MB in total. The weights are 3 files totalling 593.8 MB in onnx.
Weights3 files · 593.8 MB
Configuration2 files · 3.6 KB
Tokenizer2 files · 2.4 MB
Documentation1 file · 1.3 KB
Repository1 file · 1.2 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| decoder_model.onnx | Weights | 232.5 MB | 0a1451011d61 |
| decoder_with_past_model.onnx | Weights | 219.9 MB | 898caba53668 |
| encoder_model.onnx | Weights | 141.4 MB | 41d326633f1b |
| config.json | Configuration | 1.4 KB | — |
| special_tokens_map.json | Configuration | 2.2 KB | — |
| README.md | Documentation | 1.3 KB | — |
| .gitattributes | Repository | 1.2 KB | — |
| tokenizer.json | Tokenizer | 2.4 MB | — |
| tokenizer_config.json | Tokenizer | 2.3 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 593.8 MB
Download from Hugging Face Optimum
Released by Hugging Face Optimum through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:1910.10683
- Trained on (disclosed) c4
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 593.8 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About t5-small
Can I use t5-small commercially?
Yes. t5-small is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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