from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("Marky12345/BigData_2026")
model = AutoModelForSequenceClassification.from_pretrained("Marky12345/BigData_2026")

text = "Cổ phiếu VNM tăng mạnh, nhà đầu tư lạc quan về triển vọng quý 4"
inputs = tokenizer(text, return_tensors="pt", max_length=256,
                   truncation=True, padding="max_length")

with torch.no_grad():
    logits = model(**inputs).logits
    pred_id = logits.argmax().item()

labels = ["Bullish", "Bearish", "Uncertainty"]
print(labels[pred_id])

Training

Fine-tuned on Vietnamese financial news articles labeled by LLM silver labels. The model predicts the primary 3-class sentiment. Rare market-tone labels are kept in the dataset for audit and should be handled as separate tags when enough examples are available.