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")# pip install -U transformers accelerate # 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-300/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/4cd0124bf6bc0fa7db083c484527a64fd177a27d/checkpoint-300/rng_state.pth
- Command line
-
hf download hf://codechrl/bert-micro-cybersecurity@4cd0124bf6bc0fa7db083c484527a64fd177a27d/checkpoint-300/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/codechrl/bert-micro-cybersecurity/resolve/4cd0124bf6bc0fa7db083c484527a64fd177a27d/checkpoint-300/rng_state.pth
14.5 kB
- Xet hash:
- 7a517d7b7db73979788f19eb8ea21569302cabe94385e85e8f3cae49f6369674
- Size of remote file:
- 14.5 kB
- SHA256:
- 546db949bfe6c0e73d6de4a9d457835aad76014faad51918fc8737a3923f2a04
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.