Instructions to use harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedSelectReplacePreTrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedSelectReplacePreTrain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedSelectReplacePreTrain")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedSelectReplacePreTrain") model = AutoModelForMaskedLM.from_pretrained("harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedSelectReplacePreTrain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c2548a916b0068add4342416afc9fb94e0007e43a8f5c82889729391445976a
- Size of remote file:
- 433 MB
- SHA256:
- fee3f33e23cd28e9651c2994b4f6ca8b94852a6618f1670ae6af897b1b5e4575
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