Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-dm2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-dm2000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-dm2000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-dm2000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a207b307d1b145d97ddb31a42654443a0296ed4a2b3548608265708ad95bd45a
- Size of remote file:
- 2.38 GB
- SHA256:
- 1a8ca33d0456c0be66163c9b961e26e3394a377b57e39199e02ce016b43f471f
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