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")# 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-399/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/main/checkpoint-399/rng_state.pth
- Command line
-
hf download hf://codechrl/bert-micro-cybersecurity/checkpoint-399/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/codechrl/bert-micro-cybersecurity/resolve/main/checkpoint-399/rng_state.pth
14.5 kB
- Xet hash:
- 7682f45280116452307d7d048b8148c4798d69803818c7cc308c513a520075d7
- Size of remote file:
- 14.5 kB
- SHA256:
- 1e30ff8827cc04d333a3515bde51c8e701e5b0a041c94195a2ecf5dd4f023f92
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