Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-el4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-el4 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-el4") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-el4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b62ae252e1db25f4f0354692b52bb550de8c28a511305118c15e213eeb8bec8
- Size of remote file:
- 665 MB
- SHA256:
- 1e0be7729ac4e9352ecfa8325a30b176e8ae943abcfa6bae0e88936bc464d976
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.