---
license: cc-by-4.0
language:
- hu
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---

## Model description

Experimental model for sentiment classification in case of Hungarian news.

## Intended uses & limitations

* Label "0": Positive
* Label "1": Negative

## Training

Fine-tuned version of the original huBERT model (`SZTAKI-HLT/hubert-base-cc`), trained on news texts.

## Eval results

| Class | Precision | Recall | F-Score |
|-----|------------|------------|------|
| **Positive** | **0.86** | **0.89** | **0.88**|
| **Negative** | **0.93** | **0.91** | **0.92**|
| **accuracy** | | | **0.91**|
| **macro avg** | **0.9** | **0.9** | **0.9**|
| **weighted avg** | **0.91** | **0.91** | **0.91**|

## Usage

```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("poltextlab/HunMediBERT2")
model = AutoModelForSequenceClassification.from_pretrained("poltextlab/HunMediBERT2")
```