Instructions to use nsadeq/InformBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nsadeq/InformBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nsadeq/InformBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nsadeq/InformBERT") model = AutoModelForMaskedLM.from_pretrained("nsadeq/InformBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 23ef7a07e93bd50984aa5c5fa5672b9b74f959a294ee4d68998b2b9de75892b0
- Size of remote file:
- 499 MB
- SHA256:
- 1fa3d52f9dcb5c20e085415792544b1af59e04b73ac48d8c4eb3d9885d434ccf
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