Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55959a2b0d0125f4ae53069cafea3ffd47dc69e704fb2948ce923ba80f71182c
- Size of remote file:
- 242 MB
- SHA256:
- 0f91a766f4e874fc5f2af4716b8c3ab07145cc7573cad0851d423587ab63cfb3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.