Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-el16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-el16 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-el16") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-el16") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b7c3e92cbd617240455277f838365133a594f9341792664db7253b2aaf6ab3d
- Size of remote file:
- 1 GB
- SHA256:
- 218ac23c437558da87c4eef27e80cb89ebed762d13aa6de192510d5ec63dd73d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.