Instructions to use jx7789/bert-base-uncased-issues-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jx7789/bert-base-uncased-issues-128 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jx7789/bert-base-uncased-issues-128")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jx7789/bert-base-uncased-issues-128") model = AutoModelForMaskedLM.from_pretrained("jx7789/bert-base-uncased-issues-128", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jx7789/bert-base-uncased-issues-128: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/jx7789/bert-base-uncased-issues-128/resolve/main/training_args.bin
- Command line
-
hf download hf://jx7789/bert-base-uncased-issues-128/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jx7789/bert-base-uncased-issues-128/resolve/main/training_args.bin
3.45 kB
- Xet hash:
- 04ed36fce2240c21313e9266c8d7153bb159058a6f6f16f072379b2aaa209ec0
- Size of remote file:
- 3.45 kB
- SHA256:
- 938396205baf7dfb1c19a8210b32ad6758a6678a8e3668d1a633e7ca805ddef1
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