---
base_model:
- microsoft/mdeberta-v3-base
library_name: transformers
license: cc-by-4.0
languages:
- ar
- de
- en
- it
- bg
- el
- pl
- ro
- uk
metrics:
- accuracy
- f1
pipeline_tag: text-classification
tags:
- generated_from_trainer
- deberta
- multilingual
- subjectivity-detection
model-index:
- name: mdeberta-v3-base-subjectivity-sentiment-multilingual-no-arabic
  results: []
---

# mdeberta-v3-base-subjectivity-sentiment-multilingual-no-arabic

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) for **Subjectivity Detection in News Articles**, as presented in the paper [AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings with Sentiment for Subjectivity Detection in News Articles](https://huggingface.co/papers/2507.11764). It was developed by AI Wizards for their participation in the CLEF 2025 CheckThat! Lab Task 1.

**Code:** [https://github.com/MatteoFasulo/clef2025-checkthat](https://github.com/MatteoFasulo/clef2025-checkthat)

It achieves the following results on the evaluation set:
- Loss: 0.8900
- Macro F1: 0.7969
- Macro P: 0.7988
- Macro R: 0.7953
- Subj F1: 0.7450
- Subj P: 0.7560
- Subj R: 0.7342
- Accuracy: 0.8102

## Model description

This model is designed to classify sentences as **subjective** (opinion-laden) or **objective** in news articles. This task is a key component in combating misinformation, improving fact-checking pipelines, and supporting journalists.

The primary strategy of this model, based on `mDeBERTaV3-base`, involves enhancing transformer-based embeddings by integrating sentiment scores derived from an auxiliary model. This sentiment-augmented architecture significantly boosts performance, especially the subjective F1 score. The model also employs decision threshold calibration, optimized on the development set, to effectively address class imbalance prevalent across the datasets.

**Key Contributions from the paper:**
1.  **Sentiment-Augmented Fine-Tuning**: Enriches embedding-based models by integrating sentiment scores from an auxiliary model, notably improving subjective sentence detection.
2.  **Diverse Model Coverage**: Benchmarked across multilingual BERT variants (like mDeBERTaV3-base) for all official CLEF languages.
3.  **Threshold Calibration for Imbalance**: A simple yet effective method to tune decision thresholds on each language's development data to enhance macro-F1 performance.

## Intended uses & limitations

**Intended Uses:**
This model is intended for subjectivity detection in news articles, classifying sentences as subjective or objective. It has been evaluated across:
*   **Monolingual settings:** Arabic, German, English, Italian, and Bulgarian.
*   **Zero-shot transfer:** Tested on unseen languages such as Greek, Polish, Romanian, and Ukrainian.
*   **Multilingual training.**

Its capabilities make it suitable for applications that require discerning factual statements from opinions, such as:
*   Combating misinformation by identifying biased or opinionated content.
*   Improving automated fact-checking pipelines.
*   Assisting journalists in content analysis and report generation.

**Limitations:**
*   The model's performance is optimized for news articles; its effectiveness might vary on text from other domains (e.g., social media, conversational text) with different linguistic characteristics or content styles.
*   While decision threshold calibration is used to mitigate class imbalance, extreme imbalances in specific datasets might still affect performance.
*   The paper notes an initial submission error where an incorrect train/dev mix was used, leading to under-calibrated thresholds and lower initial scores in the multilingual track. While corrected results are reported, this highlights sensitivity to data preparation and calibration.

## Training and evaluation data

The model was fine-tuned on datasets provided for the **CLEF 2025 CheckThat! Lab Task 1: Subjectivity Detection in News Articles**.

*   **Training and development datasets** were provided for monolingual settings in Arabic, German, English, Italian, and Bulgarian.
*   **Final evaluation** included additional unseen languages such as Greek, Romanian, Polish, and Ukrainian to assess the model's generalization capabilities.

The training process involved enhancing sentence representations by integrating sentiment scores from an auxiliary model. Class imbalance, which was a notable characteristic across these languages, was addressed through decision threshold calibration during training.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Macro P | Macro R | Subj F1 | Subj P | Subj R | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|:-------:|:-------:|:------:|:------:|:--------:|
| No log        | 1.0   | 249  | 0.4619          | 0.7825   | 0.7790  | 0.7920  | 0.7421  | 0.6919 | 0.8    | 0.7901   |
| No log        | 2.0   | 498  | 0.4544          | 0.7877   | 0.7856  | 0.8021  | 0.7537  | 0.6846 | 0.8384 | 0.7932   |
| 0.4852        | 3.0   | 747  | 0.5219          | 0.7931   | 0.7899  | 0.8052  | 0.7572  | 0.6970 | 0.8288 | 0.7994   |
| 0.4852        | 4.0   | 996  | 0.7055          | 0.7935   | 0.7975  | 0.7904  | 0.7385  | 0.7605 | 0.7178 | 0.8082   |
| 0.2503        | 5.0   | 1245 | 0.8536          | 0.7883   | 0.7937  | 0.7844  | 0.7306  | 0.7592 | 0.7041 | 0.8040   |
| 0.2503        | 6.0   | 1494 | 0.8900          | 0.7969   | 0.7988  | 0.7953  | 0.7450  | 0.7560 | 0.7342 | 0.8102   |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0

## How to use

You can use this model directly with the Hugging Face `transformers` library for text classification:

```python
from transformers import pipeline

model_name = "MatteoFasulo/mdeberta-v3-base-subjectivity-sentiment-multilingual-no-arabic"
classifier = pipeline("text-classification", model=model_name)

# Example usage
text_subjective = "This is a truly amazing product and I highly recommend it!"
result_subjective = classifier(text_subjective)
print(f"'{text_subjective}' -> {result_subjective}")
# Expected output: [{'label': 'SUBJ', 'score': 0.99...}]

text_objective = "The capital of France is Paris."
result_objective = classifier(text_objective)
print(f"'{text_objective}' -> {result_objective}")
# Expected output: [{'label': 'OBJ', 'score': 0.98...}]
```

For more detailed usage, including training and evaluation scripts, please refer to the [GitHub repository](https://github.com/MatteoFasulo/clef2025-checkthat).

## Citation

If you find this model or the associated research useful, please consider citing the original paper:

```bibtex
@article{aiwizards2025checkthat,
  title={AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings with Sentiment for Subjectivity Detection in News Articles},
  author={AI Wizards team}, # Authors not fully listed in provided context, please refer to the full paper.
  journal={arXiv preprint arXiv:2507.11764},
  year={2025},
  url={https://arxiv.org/abs/2507.11764}
}
```