Instructions to use vantaa32/bert-base-uncased-issues-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vantaa32/bert-base-uncased-issues-128 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vantaa32/bert-base-uncased-issues-128")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vantaa32/bert-base-uncased-issues-128") model = AutoModelForMaskedLM.from_pretrained("vantaa32/bert-base-uncased-issues-128") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 18e2e1a1e9660432c27c8281d6d1b3bd959120465641886cb256abea9852ac59
- Size of remote file:
- 4.92 kB
- SHA256:
- 0b518cf4b4c25a0c90d60d0a2934de5a81134017a189121cc45979137dda04b9
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