metadata
language: vi
license: mit
tags:
- text-classification
- sentiment-analysis
- vietnamese
- finance
- phobert
base_model: vinai/phobert-base-v2
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
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.