Fill-Mask
Transformers
Safetensors
PyTorch
English
Indonesian
bert
text-classification
token-classification
cybersecurity
named-entity-recognition
tensorflow
masked-language-modeling
Instructions to use codechrl/bert-micro-cybersecurity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codechrl/bert-micro-cybersecurity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="codechrl/bert-micro-cybersecurity")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("codechrl/bert-micro-cybersecurity") model = AutoModelForMaskedLM.from_pretrained("codechrl/bert-micro-cybersecurity", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-3000/rng_state.pth from codechrl/bert-micro-cybersecurity: direct link, hf CLI and curl.
- Browser
- Download file 14.5 kB
-
https://huggingface.co/codechrl/bert-micro-cybersecurity/resolve/ba6ca06935cd700689928d269183aff2e5bd5af2/checkpoint-3000/rng_state.pth
- Command line
-
hf download hf://codechrl/bert-micro-cybersecurity@ba6ca06935cd700689928d269183aff2e5bd5af2/checkpoint-3000/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/codechrl/bert-micro-cybersecurity/resolve/ba6ca06935cd700689928d269183aff2e5bd5af2/checkpoint-3000/rng_state.pth
14.5 kB
- Xet hash:
- c639d92cd2c2f72ac8b075f60bec951f6a003df6f81bcb2cedb1345b0fb5b03c
- Size of remote file:
- 14.5 kB
- SHA256:
- 614cb7ee07bcc630aff3eb7dd33cd92ae6119a7d8316ea8ffddb9e98386fb60c
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