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:
- df353202379194d5e3c191e02649ec9c5c3d1ca65b75adb955a9aecb12b4811a
- Size of remote file:
- 438 MB
- SHA256:
- 9df2de5505a9db1226128e4a1b9dd8407c1cd4cd7862c30f4f573a93167b0529
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