Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-ff6000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-ff6000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-ff6000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-ff6000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f3ac896738664db7b4a75d87a3c33ce976e9d8774b58b5f2f2891359779f9a78
- Size of remote file:
- 1.34 GB
- SHA256:
- afd838ad7c0de990184fc095304ecd5566bcfc9c021a587ca06a238877282708
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