Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-ff1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-ff1000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-ff1000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-ff1000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e8ae59c930126e683c420a599bff97227e57b05032f2c4a40645fed8c670c06
- Size of remote file:
- 590 MB
- SHA256:
- e0a378d0cccde54640a4ec73588a8e12ef2c1909907e71fcad922c20211abb21
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