Initial multilingual model deployment

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language: en
+tags:
+- text-classification
+- sentiment-analysis
+- transformers
+- pytorch
+- multilingual
+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 trained for sentiment analysis across multiple languages and can classify text into positive, negative, and neutral categories.
+
+## Model Details
+
+- **Base Model**: XLM-RoBERTa Base
+- **Number of Labels**: 3 (Positive, Negative, Neutral)
+- **Languages**: Multilingual (English, Russian, Tajik, and others)
+- **Max Sequence Length**: 512 tokens
+
+## 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
+
+## 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={Your Name},
+  year={2024},
+  publisher={Hugging Face},
+  journal={Hugging Face Hub},
+  howpublished={\url{https://huggingface.co/advexon/multilingual-sentiment-classifier}},
+}
+```

model.pt

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model_config.json

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+{
+  "model_name": "xlm-roberta-base",
+  "num_labels": 3,
+  "dropout_rate": 0.2,
+  "hidden_size": 768
+}

processor_config.json

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+{
+  "tokenizer_name": "xlm-roberta-base",
+  "max_length": 512,
+  "truncation": true,
+  "padding": true,
+  "label_mapping": {}
+}

special_tokens_map.json

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+  "unk_token": "<unk>"
+}

tokenizer.json

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tokenizer_config.json

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+  "tokenizer_class": "XLMRobertaTokenizer",
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+}