Instructions to use mahfooz/bert-base-cased-dv-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mahfooz/bert-base-cased-dv-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mahfooz/bert-base-cased-dv-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mahfooz/bert-base-cased-dv-v2") model = AutoModelForMaskedLM.from_pretrained("mahfooz/bert-base-cased-dv-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e1052461856621920bb2adfba87a25263587ef850aa957f2c5e38df4a62e5c8
- Size of remote file:
- 433 MB
- SHA256:
- 94879b475e9e2a86d679b581e64b577727343e2d75e6c6ee5b94fdaec0773947
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