Transformers
PyTorch
TensorBoard
t5
text2text-generation
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Instructions to use dead-owwl/custom_billsum_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dead-owwl/custom_billsum_model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dead-owwl/custom_billsum_model") model = AutoModelForSeq2SeqLM.from_pretrained("dead-owwl/custom_billsum_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4283e1ffae82ea2702b28d98a202eae0d8fc4bbac3c2496110c3e9095fbdf294
- Size of remote file:
- 4.09 kB
- SHA256:
- e8c13c75d7421bf0067467b948f251996f88ddbc5d2078e14e0a29e4404c88af
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.