Token Classification
Transformers
Safetensors
Malay
Brunei
xlm-roberta
brunei-malay
standard-malay
text-normalization
constrained-editing
Instructions to use ssh2025/brunei-malay-normalizer-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ssh2025/brunei-malay-normalizer-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ssh2025/brunei-malay-normalizer-v3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ssh2025/brunei-malay-normalizer-v3") model = AutoModelForTokenClassification.from_pretrained("ssh2025/brunei-malay-normalizer-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ssh2025/brunei-malay-normalizer-v3: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/ssh2025/brunei-malay-normalizer-v3/resolve/main/tokenizer.json
- Command line
-
hf download hf://ssh2025/brunei-malay-normalizer-v3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ssh2025/brunei-malay-normalizer-v3/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- b62fc8e93e17072cc571d19841e10735c4387198b1bb40fa13b91fb51e6adea2
- Size of remote file:
- 17.1 MB
- SHA256:
- 794764476bf529c8802b8153ebf942a8b33998c15181144ef2d6eeb6ec47202f
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