visolex/visobert-spam-classification

ViSoBERT-Spam-MultiClass

Fine-tuned from uitnlp/visobert on ViSpamReviews for multi-class spam classification.

• Task: 4-way classification (SpamLabel: 0=NO-SPAM, 1=SPAM-1, 2=SPAM-2, 3=SPAM-3)

• Dataset: ViSpamReviews

• Hyperparameters

  • Batch size: 32
  • LR: 3e-5
  • Epochs: 100
  • Max seq len: 256

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("visolex/visobert-spam-classification")
model = AutoModelForSequenceClassification.from_pretrained("visolex/visobert-spam-classification")

text = "Chỉ nói về thương hiệu thôi."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
pred = model(**inputs).logits.argmax(dim=-1).item()
label_map = {0: "NO-SPAM",1: "SPAM-1",2: "SPAM-2",3: "SPAM-3"}
print(label_map[pred])