Instructions to use jmassot/bert-base-uncased-issues-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jmassot/bert-base-uncased-issues-128 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jmassot/bert-base-uncased-issues-128")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jmassot/bert-base-uncased-issues-128") model = AutoModelForMaskedLM.from_pretrained("jmassot/bert-base-uncased-issues-128", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba04cffa8f0d16febea6b2087a2e09001af03f1a8c734284d8e30bf1caad261c
- Size of remote file:
- 2.86 kB
- SHA256:
- 824c49f6dd5760803db08f33376cceddde78b946565a97bb55bc3bac8a502fcf
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