Instructions to use cvnberk/bert-base-uncased-issues-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cvnberk/bert-base-uncased-issues-128 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="cvnberk/bert-base-uncased-issues-128", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("cvnberk/bert-base-uncased-issues-128") model = AutoModelForMaskedLM.from_pretrained("cvnberk/bert-base-uncased-issues-128", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66f708aa92161a7e8d1f85ab7546a3a084ce50beb3a280a880a97b0e6ed6cba7
- Size of remote file:
- 4.03 kB
- SHA256:
- df1da0abf2907fb05b0a982f0fc0fcfb22aa6461979a9c4b14f9307a6db34365
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