Instructions to use alexanderfalk/danbert-small-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alexanderfalk/danbert-small-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="alexanderfalk/danbert-small-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("alexanderfalk/danbert-small-cased") model = AutoModelForMaskedLM.from_pretrained("alexanderfalk/danbert-small-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 02b83b4b9c291fe569ab6630c8bc8dada7d35f9d5d10e65ec3a51aa3cbbec933
- Size of remote file:
- 336 MB
- SHA256:
- 50f13e6f1a7d9cee4002ed8c89cd77b6f7c71afdde16924d7e822dce7d8c29c2
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