Instructions to use cj-mills/bert-base-uncased-issues-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cj-mills/bert-base-uncased-issues-128 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="cj-mills/bert-base-uncased-issues-128")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("cj-mills/bert-base-uncased-issues-128") model = AutoModelForMaskedLM.from_pretrained("cj-mills/bert-base-uncased-issues-128", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- faf540cb670ea7a43deb8c625f5441b576bf5886516a947f07f6c7f3931b7967
- Size of remote file:
- 3.06 kB
- SHA256:
- 033582fca25a1cebf05c03880285c71f988383c0789f0601e712bb7b378e5606
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