Instructions to use madanagrawal/masked_language_modeling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use madanagrawal/masked_language_modeling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="madanagrawal/masked_language_modeling")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("madanagrawal/masked_language_modeling") model = AutoModelForMaskedLM.from_pretrained("madanagrawal/masked_language_modeling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c1011a42f37548cc2641b98c97e8a3c2a135632ef87e7638dd73d96b0e404aa4
- Size of remote file:
- 4.98 kB
- SHA256:
- ad22f4e2821371dcc6cf1ee1a65b173a54f969aad3681ef30357127413163088
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