metadata
license: apache-2.0
datasets:
- MIMEDIS/newton-media-unlabeled
language:
- sk
base_model:
- gerulata/slovakbert
pipeline_tag: text-classification
tags:
- Stance
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}")