Text Classification
Transformers
Safetensors
English
Indonesian
bert
fill-mask
token-classification
cybersecurity
named-entity-recognition
Instructions to use codechrl/bert-base-cybersecurity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codechrl/bert-base-cybersecurity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="codechrl/bert-base-cybersecurity")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("codechrl/bert-base-cybersecurity") model = AutoModelForMaskedLM.from_pretrained("codechrl/bert-base-cybersecurity", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from codechrl/bert-base-cybersecurity: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/codechrl/bert-base-cybersecurity/resolve/main/model.safetensors
- Command line
-
hf download hf://codechrl/bert-base-cybersecurity/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/codechrl/bert-base-cybersecurity/resolve/main/model.safetensors
433 MB
- Xet hash:
- 65565133b5cf87467297e213934f8f89e831eb66f841401e0acdb0a36f1a64f6
- Size of remote file:
- 433 MB
- SHA256:
- 7cf5af4f8435c00c5c01ecd73f491fc7c728eb4a69ac2d05e510218a3d2d14a4
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