Instructions to use SRDdev/HingMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SRDdev/HingMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SRDdev/HingMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SRDdev/HingMaskedLM") model = AutoModelForMaskedLM.from_pretrained("SRDdev/HingMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a0883e8e450728131ff6794b1b91954e93bd8051818b2d7d2bfa9d0ae4bec389
- Size of remote file:
- 268 MB
- SHA256:
- 77092e88d5a9dba5c10ea95d6c104c056e8e9016c5c4ee63853b674ccd48be76
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