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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bengali-code-mix-sentiment |
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results: [] |
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datasets: |
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- Swarnadeep-28/bn_code_mix_sentiment_dataset |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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--- |
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# Bengali-English Code-Mixed Sentiment Model |
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## Model Summary |
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This model is a **fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)** for **sentiment analysis** on **Bengali–English code-mixed text** (social media posts, comments, and tweets). |
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- **Task**: Text Classification (Sentiment Analysis) |
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- **Languages**: Bengali (Romanized) + English |
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- **Classes**: `0`, `1`, `2`, `3` |
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- **Fine-tuning method**: Full fine-tuning |
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- **Dataset**: [Bengali-English Code-Mixed Sentiment Dataset](https://huggingface.co/datasets/Swarnadeep-28/bn_code_mix_sentiment_dataset) |
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This model provides strong baseline performance for code-mixed sentiment classification and can be directly applied to social media analysis and low-resource NLP research. |
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--- |
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## How to Use |
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### Inference Example |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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model_id = "Swarnadeep-28/bengali-code-mix-sentiment" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForSequenceClassification.from_pretrained(model_id) |
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text = "Aaj match ta khub bhalo chilo! Loved it." |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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pred = torch.argmax(logits, dim=-1).item() |
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labels = ["0", "1", "2", "3"] |
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print("Predicted label:", labels[pred]) |
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``` |
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--- |
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## Training Details |
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- **Base model**: `xlm-roberta-base` |
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- **Method**: Full fine-tuning (all parameters updated) |
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- **Optimizer**: AdamW |
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- **Learning Rate**: 2e-5 |
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- **Epochs**: 3 |
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- **Batch Size**: 16 (train), 32 (eval) |
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- **Hardware**: Trained on a single GPU (Colab T4 / equivalent) |
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--- |
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## Evaluation |
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### Classification Report |
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| Label | Precision | Recall | F1-Score | Support | |
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|-------|-----------|--------|----------|---------| |
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| 0 | 0.80 | 0.73 | 0.77 | 528 | |
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| 1 | 0.73 | 0.73 | 0.73 | 617 | |
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| 2 | 0.69 | 0.76 | 0.72 | 675 | |
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| 3 | 0.67 | 0.57 | 0.62 | 182 | |
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### Overall Metrics |
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- **Accuracy**: 0.73 |
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- **Macro Avg**: Precision = 0.72, Recall = 0.70, F1 = 0.71 |
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- **Weighted Avg**: Precision = 0.73, Recall = 0.73, F1 = 0.73 |
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- **Total Samples**: 2002 |
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--- |
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## Applications |
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- Sentiment classification of Bengali-English social media text |
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- Research in **code-mixed NLP for Indic languages** |
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- Benchmark for parameter-efficient fine-tuning (compare with LoRA model) |
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## Limitations |
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- Heavily Romanized or slang-heavy Bengali may reduce accuracy |
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- Trained primarily on short-form text (tweets, comments, reviews) |
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- Not designed for abusive/toxic content moderation or safety-critical use cases |
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--- |
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## Ethical Considerations |
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- Data reflects natural biases from social media sources |
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- Misclassifications may occur in sarcasm or offensive text |
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- Should not be the sole basis for critical decision-making |
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--- |
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## Citation |
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If you use this model, please cite: |
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```bibtex |
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@model{das2025_bn_code_mix_sentiment, |
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author = {Swarnadeep Das}, |
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title = {Bengali-English Code-Mixed Sentiment Model}, |
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year = {2025}, |
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url = {https://huggingface.co/Swarnadeep-28/bengali-code-mix-sentiment} |
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} |
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``` |
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--- |
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## Acknowledgements |
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- **Dataset**: [Bengali-English Code-Mixed Sentiment Dataset](https://huggingface.co/datasets/Swarnadeep-28/bn_code_mix_sentiment_dataset) |
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- **Base model**: [`xlm-roberta-base`](https://huggingface.co/xlm-roberta-base) |
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- **Frameworks**: [Transformers](https://huggingface.co/docs/transformers), [Datasets](https://huggingface.co/docs/datasets) |
