Update README.md

README.md

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----
-language: en
-tags:
-- text-classification
-- sentiment-analysis
-- transformers
-- pytorch
-- multilingual
-- xlm-roberta
-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={Hugging Face},
-  journal={Hugging Face Hub},
-  howpublished={\url{https://huggingface.co/advexon/multilingual-sentiment-classifier}},
-}
-```
+---
+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