Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-large with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-large") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c8a31564f4023b935520ba1361977326f9117c3b194e3b0705ee1f250094a7ce
- Size of remote file:
- 2.95 GB
- SHA256:
- 5c6c8e910de60ab33abb9ca6e47b8c538f338d11e120a9e4bd0b1ca775bca162
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.