Metric	Mean ± std
Test Macro-F1	0.5909 ± 0.0370
Test accuracy	0.8699 ± 0.0015
Test macro precision	0.6603 ± 0.0074
Test macro recall	0.5740 ± 0.0416
Development Macro-F1	0.5923 ± 0.0489

Per-seed results

seed	dev_macro_f1	test_macro_f1	test_accuracy
22.000000	0.555701	0.560737	0.870509
42.000000	0.573480	0.579729	0.868114
202.000000	0.647849	0.632238	0.870958

Label mapping

json

{
  "0": "CLEAN",
  "1": "OFFENSIVE",
  "2": "HATE"
}

Usage

python

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_id = "BaoNhan/bn-newsbert-ViHSD"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

# PhoBERT-style checkpoint: segment raw Vietnamese with VnCoreNLP first.
text = "Đây là văn_bản tiếng_Việt đã được chuẩn_hóa ."

inputs = tokenizer(
    text,
    return_tensors="pt",
    truncation=True,
    max_length=256,
)

with torch.no_grad():
    probabilities = model(**inputs).logits.softmax(dim=-1)[0]

predicted_id = int(probabilities.argmax())
print(model.config.id2label[predicted_id], probabilities.tolist())

Limitations

ViHSD is class-imbalanced and reflects Vietnamese social-media language from a particular collection period. Performance may not transfer directly to new platforms, dialects, code-switching patterns, irony, or emerging slang. Predictions should not be the sole basis for moderation or punitive decisions.

Dataset citation

bibtex

@InProceedings{10.1007/978-3-030-79457-6_35,
  author={Luu, Son T. and Nguyen, Kiet Van and Nguyen, Ngan Luu-Thuy},
  title={A Large-Scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts},
  booktitle={Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices},
  year={2021},
  publisher={Springer International Publishing},
  pages={415--426},
  doi={10.1007/978-3-030-79457-6_35}
}