Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-dm1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-dm1000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-dm1000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-dm1000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f20ce811a440646778bef8e4fe8a2d59f514f0391a182602033d43eadfa0a973
- Size of remote file:
- 1.19 GB
- SHA256:
- 7bea94f224c4016f0aaa7624d88126a879fc3ba57f974c9c9f24e8dd02fe9eed
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