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")# pip install -U transformers accelerate # 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
Download scheduler.pt from harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedSelectReplacePreTrain: direct link, hf CLI and curl.
- Browser
- Download file 623 Bytes
-
https://huggingface.co/harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedSelectReplacePreTrain/resolve/main/scheduler.pt
- Command line
-
hf download hf://harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedSelectReplacePreTrain/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/harish/AStitchInLanguageModels-Task2_EN_BERTTokenizedSelectReplacePreTrain/resolve/main/scheduler.pt
623 Bytes
- Xet hash:
- e7017224536fbd333a676757df8f20804b0410c424782cd4f0626bb5d271cc2f
- Size of remote file:
- 623 Bytes
- SHA256:
- 3c32fbcff9243ceb74c7eb970f8ac8f9c3d99e952f4e9e8bcbdaeac3b229555b
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