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:
- 686c413d1e1d137c5838d230e97d66bbde27873b426435ebd1ddc5cb2d748e4a
- Size of remote file:
- 595 MB
- SHA256:
- ba7875f53fda1f8548da0634ed9b6e75c2b4f99eff118a69cd28178ef5a88bfb
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