Instructions to use harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedNoPreTrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedNoPreTrain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedNoPreTrain")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedNoPreTrain") model = AutoModelForMaskedLM.from_pretrained("harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedNoPreTrain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 40990b092b8f7da5035ce9bc34d3fcf9fadd5e2dbc7790024a4c0e45f9afe494
- Size of remote file:
- 433 MB
- SHA256:
- 2c43930b6172fe3b5a49807a17c6ce211569f5e1fbb6a8b585cc78f586848c06
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