Transformers
PyTorch
Safetensors
t5
text2text-generation
Math
Summarization
Mathematics
text-generation-inference
Instructions to use hyunjongkimmath/notation_summarizations_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hyunjongkimmath/notation_summarizations_model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hyunjongkimmath/notation_summarizations_model") model = AutoModelForSeq2SeqLM.from_pretrained("hyunjongkimmath/notation_summarizations_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f298dfb2271204cd51206b9303fc2c8a1797ec08ac81f02dd9a283dc6eeb11d
- Size of remote file:
- 243 MB
- SHA256:
- dfc185338928a08bc6c4180818836ac39940a5729ffde34bc25f1ae67ee64209
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.