Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-nl32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-nl32 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-nl32") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-nl32") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 92421be084ba1de78c5aca280a6a9b12cf076f7711a160f949c0b448b1a1b798
- Size of remote file:
- 2.21 GB
- SHA256:
- a1e0ed5b511ef36cff66b872c44957f30fffc1f1062c9d2da6eeb2e314d1af1e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.