Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-nl16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-nl16 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-nl16") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-nl16") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c6129e92f91ff0b072711191629e25a7af473c3f0b5b6a8f2efed46aba5d909
- Size of remote file:
- 1.16 GB
- SHA256:
- 86edfbf31a0df1b93396d0c92b0cd1b515f5e11a096108b0e2ec0b9dad9b5206
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.