Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-nh32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-nh32 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-nh32") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-nh32") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab94156d010b9d25f56c5c6415d22e30bb1a4ad40d611bb4a1702f5806e022e1
- Size of remote file:
- 1.46 GB
- SHA256:
- 2d19d469c9a7db527bf63b31cc76014937897399443cc1f19208ced0fe065f52
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.