Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-ff2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-ff2000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-ff2000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-ff2000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81934d55facfcde6b377c5c3cf1e22d78dfea12bfa3e8b832e0122bc60566fed
- Size of remote file:
- 741 MB
- SHA256:
- cc851e39955844e1ff08332297bf4f4f088cc5195b2dc262ae3e553a1d3bb6f0
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