Instructions to use melll-uff/longformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use melll-uff/longformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="melll-uff/longformer")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("melll-uff/longformer") model = AutoModel.from_pretrained("melll-uff/longformer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6a70fca409cff0403019255e4afce89f76ec9013f0ae5b3bd74a44ee06b95caf
- Size of remote file:
- 447 MB
- SHA256:
- 7c7819b9a1cc042b05686f70c135fc164a69bc24db4cadef7b24ed1ba2c704f1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.