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:
- b41b8672b1588dca3385ea8cdfc654f5281a354d07b9bc0bc0c7dc66e13d2ea7
- Size of remote file:
- 3.25 kB
- SHA256:
- 2483e7452ccfc74f345ec8ff58b76238ec67ae8b938a896e3004085f825fe1fc
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