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---
language:
- en
- bn
tags:
- sentiment-analysis
- cross-lingual
- xlm-roberta
- text-classification
datasets:
- glue
- sepidmnorozy/Bengali_sentiment
metrics:
- accuracy
- f1
library_name: transformers
pipeline_tag: text-classification
---
# arif481/crosslingual-sentiment-model
A cross-lingual sentiment analysis model fine-tuned on XLM-RoBERTa for binary sentiment classification (positive/negative) across en, bn.
## Model Description
This model performs sentiment classification across multiple languages using transfer learning. It was trained using the **combined** strategy.
### Supported Languages
- English (en)
- Bengali (bn)
### Training Mode: combined
Trained on combined English and Bengali data for multilingual learning.
## Usage
```python
from transformers import pipeline
classifier = pipeline("sentiment-analysis", model="arif481/crosslingual-sentiment-model")
# English
result = classifier("This movie is absolutely fantastic!")
print(result) # [{'label': 'positive', 'score': 0.99}]
# Bengali
result = classifier("এই সিনেমাটি অসাধারণ ছিল!")
print(result) # [{'label': 'positive', 'score': 0.95}]
```
## Training
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("arif481/crosslingual-sentiment-model")
tokenizer = AutoTokenizer.from_pretrained("arif481/crosslingual-sentiment-model")
```
## Metrics
| Metric | Value |
|--------|-------|
| Accuracy | N/A |
| Macro F1 | N/A |
| Precision | N/A |
| Recall | N/A |
## Limitations
- Binary classification only (positive/negative)
- May not perform well on neutral sentiment
- Bengali performance may be lower than English due to limited training data
## Citation
If you use this model, please cite:
```bibtex
@misc{crosslingual-sentiment,
author = {Cross-Lingual Sentiment Team},
title = {Cross-Lingual Sentiment Analysis Model},
year = {2024},
publisher = {Hugging Face},
url = {https://huggingface.co/arif481/crosslingual-sentiment-model}
}
```
## License
This model is released under the MIT License.