MIMEDIS/stance-headlines-model

Stance Detection Model for Slovak

This model is fine-tuned from gerulata/slovakbert for stance detection on Slovak text. It classifies text into three stance categories: Negative, Neutral, and Positive.

Model Details

• Base Model: gerulata/slovakbert
• Task: Stance Detection / Sentiment Classification
• Language: Slovak (sk)
• Number of Labels: 3

Label Mappings

Label ID	Stance
0	Negative
1	Neutral
2	Positive

Usage

Quick Start

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "MIMEDIS/stance-headlines-model"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example text
text = "Toto je skvelý nápad a plne ho podporujem!"

# Tokenize and predict
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
outputs = model(**inputs)
predictions = torch.softmax(outputs.logits, dim=-1)

# Get predicted label
label_id = torch.argmax(predictions, dim=-1).item()
label_map = {0: "Negative", 1: "Neutral", 2: "Positive"}

print(f"Text: {text}")
print(f"Predicted stance: {label_map[label_id]}")
print(f"Confidence scores: Negative={predictions[0][0]:.3f}, Neutral={predictions[0][1]:.3f}, Positive={predictions[0][2]:.3f}")