Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-ff9000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-ff9000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-ff9000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-ff9000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ebf1d88f79820ac1d481456811d83f9b17bbfdc3e3a3a56b7547491798fb93ab
- Size of remote file:
- 1.8 GB
- SHA256:
- 32b996257af5fc8e10aaa30d389ae72a043affeef9c1973fba47f4671a467305
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