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 pytorch_model.bin from jx7789/bert-base-uncased-issues-128: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/jx7789/bert-base-uncased-issues-128/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jx7789/bert-base-uncased-issues-128/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jx7789/bert-base-uncased-issues-128/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 2d457d7f23dd0ce3bce6590c6a8e62bc3df54ef3197f0818c530ea39ca0837e5
- Size of remote file:
- 438 MB
- SHA256:
- f63a79b77a558916ac50589b2746739e84895ff8eb03ee2f97fa1430a869bf6a
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