metadata
language: vi
tags:
- hate-speech-detection
- vietnamese
- transformer
license: apache-2.0
datasets:
- visolex/ViHOS
metrics:
- precision
- recall
- f1
model-index:
- name: visobert-hsd-span
results:
- task:
type: token-classification
name: Hate Speech Span Detection
dataset:
name: ViHOS
type: custom
metrics:
- name: Precision
type: precision
value: <INSERT_PRECISION>
- name: Recall
type: recall
value: <INSERT_RECALL>
- name: F1 Score
type: f1
value: <INSERT_F1>
base_model:
- uitnlp/visobert
pipeline_tag: token-classification
ViSoBERT-HSD-Span
This model is fine-tuned from uitnlp/visobert on the visolex/ViHOS dataset for span-level hate/offensive detection in Vietnamese comments.
Model Details
- Base Model:
uitnlp/visobert - Dataset: visolex/ViHOS
- Fine-tuning: HuggingFace Transformers
Hyperparameters
- Batch size:
16 - Learning rate:
5e-5 - Epochs:
100 - Max sequence length:
128 - Early stopping:
5
Usage
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("visolex/visobert-hsd-span")
model = AutoModelForTokenClassification.from_pretrained("visolex/visobert-hsd-span")
text = "Nói cái lol . t thấy thô tục vl"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits # [batch, seq_len, num_labels]
# For binary: use sigmoid, for multi-class: use softmax+argmax
probs = torch.sigmoid(logits)
preds = (probs > 0.5).long().squeeze().tolist() # [seq_len]
tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])
span_labels = [p[0] for p in preds]
# Lấy token có nhãn span = 1, loại bỏ <s> và </s> nếu muốn
span_tokens = [token for token, label in zip(tokens, span_labels) if label == 1 and token not in ['<s>', '</s>']]
print("Span tokens:", span_tokens)
print("Span text:", tokenizer.convert_tokens_to_string(span_tokens))