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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 997 Bytes
f8d4def | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | ---
license: cc-by-sa-4.0
language:
- vi
library_name: transformers
pipeline_tag: token-classification
tags:
- capitalization
- punctuation
- capu
- vietnamese
---
# 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 under `base_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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