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 checkpoint-3/optimizer.pt from codechrl/bert-base-cybersecurity: direct link, hf CLI and curl.
- Browser
- Download file 867 MB
-
https://huggingface.co/codechrl/bert-base-cybersecurity/resolve/a44511b613112e3fbd51117ded5ddbb732a625ad/checkpoint-3/optimizer.pt
- Command line
-
hf download hf://codechrl/bert-base-cybersecurity@a44511b613112e3fbd51117ded5ddbb732a625ad/checkpoint-3/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/codechrl/bert-base-cybersecurity/resolve/a44511b613112e3fbd51117ded5ddbb732a625ad/checkpoint-3/optimizer.pt
867 MB
- Xet hash:
- 9b1ed6b82a28228de4186926cbb11d1e9811ae8f08ea84e9378dd83eabec94f6
- Size of remote file:
- 867 MB
- SHA256:
- 65d51d6a8cc86ee1cc8a9067b99263cb7b6d4cabb74ab12b1554587a9f9ffdf9
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