Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-large-nl20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-large-nl20 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-large-nl20") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-large-nl20", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4bb4628b285e77a89b99799198026aa1de6a15e99cbd761e273b87ecf33c35e
- Size of remote file:
- 2.48 GB
- SHA256:
- faaf6e8e7faf63ac0774d25a3bde76f338b22136ba03fe529782ca227ed40f6b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.