| | --- |
| | language: en |
| | tags: |
| | - text-classification |
| | - sentiment-analysis |
| | - transformers |
| | - pytorch |
| | - multilingual |
| | - xlm-roberta |
| | - Siyovush Mirzoev |
| | - Tajik |
| | - Tajikistan |
| | license: mit |
| | --- |
| | |
| | # advexon/multilingual-sentiment-classifier |
| |
|
| | Multilingual text classification model trained on XLM-RoBERTa base for sentiment analysis across English, Russian, Tajik and other languages |
| |
|
| | ## Model Description |
| |
|
| | This is a multilingual text classification model based on XLM-RoBERTa. It has been fine-tuned for sentiment analysis across multiple languages and can classify text into positive, negative, and neutral categories. |
| |
|
| | ## Model Details |
| |
|
| | - **Base Model**: XLM-RoBERTa Base |
| | - **Model Type**: XLMRobertaForSequenceClassification |
| | - **Number of Labels**: 3 (Negative, Neutral, Positive) |
| | - **Languages**: Multilingual (English, Russian, Tajik, and others) |
| | - **Max Sequence Length**: 512 tokens |
| | - **Hidden Size**: 768 |
| | - **Attention Heads**: 12 |
| | - **Layers**: 12 |
| |
|
| | ## Performance |
| |
|
| | Based on training metrics: |
| | - **Training Accuracy**: 58.33% |
| | - **Validation Accuracy**: 100% |
| | - **Training Loss**: 0.94 |
| | - **Validation Loss**: 0.79 |
| |
|
| | ## Usage |
| |
|
| | ### Using the Model |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| | import torch |
| | |
| | # Load the model and tokenizer |
| | tokenizer = AutoTokenizer.from_pretrained("advexon/multilingual-sentiment-classifier") |
| | model = AutoModelForSequenceClassification.from_pretrained("advexon/multilingual-sentiment-classifier") |
| | |
| | # Example usage |
| | text = "This product is amazing!" |
| | inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) |
| | outputs = model(**inputs) |
| | predictions = torch.softmax(outputs.logits, dim=-1) |
| | predicted_class = torch.argmax(predictions, dim=1).item() |
| | |
| | # Class mapping: 0=Negative, 1=Neutral, 2=Positive |
| | sentiment_labels = ["Negative", "Neutral", "Positive"] |
| | predicted_sentiment = sentiment_labels[predicted_class] |
| | print(f"Predicted sentiment: {predicted_sentiment}") |
| | ``` |
| |
|
| | ### Example Predictions |
| |
|
| | - "I absolutely love this product!" → Positive |
| | - "This is terrible quality." → Negative |
| | - "It's okay, nothing special." → Neutral |
| | - "Отличный сервис!" → Positive (Russian) |
| | - "Хунуки хуб нест" → Negative (Tajik) |
| |
|
| | ## Training |
| |
|
| | This model was trained using: |
| | - **Base Model**: XLM-RoBERTa Base |
| | - **Optimizer**: AdamW |
| | - **Learning Rate**: 2e-5 |
| | - **Batch Size**: 16 |
| | - **Training Epochs**: 2 |
| | - **Languages**: English, Russian, Tajik |
| |
|
| | ## Model Architecture |
| |
|
| | The model uses the XLM-RoBERTa architecture with: |
| | - 12 transformer layers |
| | - 768 hidden dimensions |
| | - 12 attention heads |
| | - 3 classification heads for sentiment analysis |
| |
|
| | ## Limitations |
| |
|
| | - The model's performance may vary across different languages |
| | - It is recommended to fine-tune on domain-specific data for optimal performance |
| | - Maximum input length is 512 tokens |
| | - Performance may be lower on languages not well-represented in the training data |
| |
|
| | ## Citation |
| |
|
| | If you use this model in your research, please cite: |
| |
|
| | ```bibtex |
| | @misc{multilingual-text-classifier, |
| | title={Multilingual Text Classification Model}, |
| | author={Advexon}, |
| | year={2024}, |
| | publisher={Siyovush Mirzoev}, |
| | journal={Hugging Face Hub}, |
| | howpublished={\url{https://huggingface.co/advexon/multilingual-sentiment-classifier}}, |
| | } |
| | ``` |
| | ## Contact me |
| | mirzoevsish@gmail.com / +992 710707777 WhatsApp |
| |
|
