Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-mini-nl12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-mini-nl12 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-mini-nl12") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-mini-nl12") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6620533d4aebeee7da467dc6523701c5ccebbf9aeb6c1945dcab7a33925a5b18
- Size of remote file:
- 276 MB
- SHA256:
- e96e2ad8e489c055695e14b0d3a93a8b22a772a3aa9ffcdda70e45e71d1f2598
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.