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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93945f6c189bacc2997eb4a3d4413f87036b2febb5651732f664e02d0e28e0dd
- Size of remote file:
- 35.4 MB
- SHA256:
- 56f3d4b0dc5d9162cedaef7ce24293392b826376597d38f00b4e75b6f7432cd6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.