Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small-dm1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small-dm1000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small-dm1000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small-dm1000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f5bf35b98d9471921761dc0d85f0acc1df1e1cc559db07010ab81c42428e0dd
- Size of remote file:
- 484 MB
- SHA256:
- 4ebb2d98774ad439c323434322fabb80055b63d9c9f2d975b4db15c50001f8ae
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