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:
- 0cdd9e89cbecfe7485f2ff8608232fe5d8ef32228fd5e64a9c5ec43886bb4a6c
- Size of remote file:
- 17.7 MB
- SHA256:
- 12917da4dfc3f5c4ad90d2fe93c0cdd57e1e4c1df7f3d01b8c94b8952db28272
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.