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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 508b73d52974d95b45e19b4fe52433fb5f0d6b71721e62652e9fb481f7ad01b6
- Size of remote file:
- 125 MB
- SHA256:
- 6158d4d080034a0fa0ea12f0a468535d22b4c3e8a956f498dadb27c1cd56613f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.