metadata
language: vi
tags:
- hate-speech-detection
- vietnamese
- phobert
license: apache-2.0
datasets:
- VN-HSD
metrics:
- accuracy
- f1
model-index:
- name: phobert-hsd
results:
- task:
type: text-classification
name: Hate Speech Detection
dataset:
name: VN-HSD
type: custom
metrics:
- name: Accuracy
type: accuracy
value: <INSERT_ACCURACY>
- name: F1 Score
type: f1
value: <INSERT_F1_SCORE>
base_model:
- vinai/phobert-base
pipeline_tag: text-classification
PhoBERT‑HSD: Hate Speech Detection for Vietnamese Text
Fine‑tuned from vinai/phobert-base on the VN‑HSD dataset.
Model Details
- Base Model:
vinai/phobert-base - Dataset: VN‑HSD (ViSoLex‑HSD unified hate speech corpus)
- Fine‑tuning: HuggingFace Transformers
Hyperparameters
- Batch size:
32 - Learning rate:
5e-5 - Epochs:
100 - Max sequence length:
256
Results
- Accuracy:
<INSERT_ACCURACY> - F1 Score:
<INSERT_F1_SCORE>
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("visolex/phobert-hsd")
model = AutoModelForSequenceClassification.from_pretrained("visolex/phobert-hsd")
text = "Đừng nói những lời thô tục như vậy!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
pred = model(**inputs).logits.argmax(dim=-1).item()
print(f"Label: {['CLEAN','OFFENSIVE','HATE'][pred]}")