Instructions to use kjunelee/bert-base-uncased-issues-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kjunelee/bert-base-uncased-issues-128 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kjunelee/bert-base-uncased-issues-128")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kjunelee/bert-base-uncased-issues-128") model = AutoModelForMaskedLM.from_pretrained("kjunelee/bert-base-uncased-issues-128", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ccc7f8c1448f4d1f0ac75d2493177cb814232ea87791a4e15e47f122886a79b
- Size of remote file:
- 438 MB
- SHA256:
- a31479cec30559a2c276dd37544a1c422ed157f537cdc438098ec82135da346d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.