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Marky12345/BigData_2026

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Model Card

FiNTA Sentiment Model

Task: Sentiment Analysis — Vietnamese Financial News Base model: vinai/phobert-base-v2 Accuracy: N/A | Macro F1: N/A

Labels (sentiment chính 3-class)

ID	Label	Mô tả	Score range
0	Bullish	Lạc quan, tích cực	+0.5 → +1.0
1	Bearish	Bi quan, tiêu cực	-1.0 → -0.5
2	Uncertainty	Không rõ xu hướng	-0.2 → +0.2

Raw LLM labels are merged for the primary model:

—Hype/FOMO -> Bullish
—Panic -> Bearish
—Contrarian -> Uncertainty

Usage

python

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.