Instructions to use kunalr63/longformerMLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kunalr63/longformerMLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kunalr63/longformerMLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kunalr63/longformerMLM") model = AutoModelForMaskedLM.from_pretrained("kunalr63/longformerMLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 540ffda4d74ebd5959ba1007b31bab04cb357b748c62e67d7e0e755016e7f305
- Size of remote file:
- 3.39 kB
- SHA256:
- feed3775cae4886d08411b6afc4ac09ccaad8531b620317ddcd7250da6003f9d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.