Transformers
PyTorch
TensorFlow
ONNX
Safetensors
English
t5
text2text-generation
summary
summarizer
Eval Results (legacy)
text-generation-inference
Instructions to use shorecode/t5-efficient-tiny-summarizer-general-purpose-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shorecode/t5-efficient-tiny-summarizer-general-purpose-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("shorecode/t5-efficient-tiny-summarizer-general-purpose-v2") model = AutoModelForSeq2SeqLM.from_pretrained("shorecode/t5-efficient-tiny-summarizer-general-purpose-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 192038bdccfbecc82e1e7d6477249b29cf8dd04a536f02d538a104c260aae237
- Size of remote file:
- 6.03 kB
- SHA256:
- e66348775f5c0c4e602e276cfabd86d6b08e208f328b80af2d3902749f42fb99
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.