Instructions to use nglaura/skimformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nglaura/skimformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nglaura/skimformer")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("nglaura/skimformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dc6c5b3d5d2c3ad915f399255d00a76addc7e5f68ef3a219c12178435f5c5525
- Size of remote file:
- 454 MB
- SHA256:
- ac2da0e8bd3c7ba652cb100512523051cc2305d0822d4794ccd0effab3c642b5
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