Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-ff12000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-ff12000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-ff12000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-ff12000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 700577d00b947777ba3f730ed7cb458f6051b04fe4470686a2e57019cb8ee191
- Size of remote file:
- 2.25 GB
- SHA256:
- bd862c7b261468bb8bc3b5aa320fc2467cd80563cc088e8ac052d37b6875ef16
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.