Token Classification
Transformers
PyTorch
Vietnamese
bert
capitalization
punctuation
capu
vietnamese
Instructions to use leakless/vibert-capu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leakless/vibert-capu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="leakless/vibert-capu")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("leakless/vibert-capu", dtype="auto") - Notebooks
- Google Colab
- Kaggle
vibert-capu
Consolidated CAPU model package for vit-stt.
This repository combines:
- CAPU fine-tuned model files from
dragonSwing/vibert-capu - ViBERT base tokenizer/config files from
FPTAI/vibert-base-cased, placed underbase_model/
The consolidation keeps the runtime model swappable with a single model
directory. config.json points to base_model through
pretrained_name_or_path.
Intended use
Vietnamese capitalization and punctuation restoration for vit-stt
postprocessing.
Provenance
- Source CAPU model: https://huggingface.co/dragonSwing/vibert-capu
- Source base model: https://huggingface.co/FPTAI/vibert-base-cased
The source CAPU model reports cc-by-sa-4.0 metadata on Hugging Face. The base
model repository did not expose license metadata through the Hugging Face API at
upload time.
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