Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-large-nl36 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-large-nl36 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-large-nl36") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-large-nl36") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c97d97096321dd444c6faf2e673e0f6a2a48cf463a261e35863b67253582521d
- Size of remote file:
- 4.36 GB
- SHA256:
- edb91e09545d698a1f055177827f38cc6520b3d78ce4177315c17ee6fbe893b4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.