The T5 models are encoder-decoder models pre-trained on a mixture of
unsupervised and supervised tasks.1 These models work well on a variety of
tasks by prepending task-specific prefixes to the input, e.g.:
translate English to German: …, summarize: …., etc.
This example also supports the FLAN-T5 models variants.2
Generate text with:
python t5.py --model t5-small --prompt "translate English to German: A tasty apple"This should give the output: Ein leckerer Apfel
To see a list of options run:
python t5.py --helpThe <model> can be any of the following:
| Model Name | Model Size |
|---|---|
| t5-small | 60 million |
| t5-base | 220 million |
| t5-large | 770 million |
| t5-3b | 3 billion |
| t5-11b | 11 billion |
The FLAN variants can be specified with google/flan-t5-small,
google/flan-t5-base, etc. See the Hugging Face
page for a
complete list of models.
Footnotes
-
For more information on T5 see the original paper or the Hugging Face page. ↩
-
For more information on FLAN-T5 see the original paper. ↩