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README.md

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+# DistilBERT French Multilingual Sequence Classification
+
+This is a distilled version of BERT fine-tuned for multilingual sequence classification, with a focus on French text processing. The model demonstrates strong performance across French, Hindi, and English languages.
+
+## Model Details
+
+- **Model Type**: DistilBERT for Sequence Classification
+- **Base Architecture**: 6-layer transformer with 12 attention heads
+- **Hidden Size**: 768
+- **Vocabulary Size**: 119,547 tokens
+- **Max Sequence Length**: 512 tokens
+- **Languages**: Multilingual (French, Hindi, English)
+
+## Performance Metrics
+
+The model achieved the following performance on evaluation datasets:
+
+### Validation Set (Overall)
+- **Accuracy**: 96.75%
+- **F1 Score**: 96.78%
+- **Precision**: 95.77%
+- **Recall**: 97.82%
+
+### Language-Specific Performance
+
+#### French
+- **Accuracy**: 77.32%
+- **F1 Score**: 80.88%
+- **Precision**: 69.46%
+- **Recall**: 96.81%
+- **Samples**: 4,267
+
+#### Hindi
+- **Accuracy**: 80.17%
+- **F1 Score**: 83.17%
+- **Precision**: 72.14%
+- **Recall**: 98.17%
+- **Samples**: 2,500
+
+#### English
+- **Accuracy**: 97.22%
+- **Samples**: 3,233
+
+### External Dataset Performance
+- **TURNS2K Dataset**: 90.25% accuracy, 90.96% F1 score
+
+## Model Configuration
+
+```json
+{
+  "model_type": "distilbert",
+  "architectures": ["DistilBertForSequenceClassification"],
+  "n_layers": 6,
+  "n_heads": 12,
+  "dim": 768,
+  "hidden_dim": 3072,
+  "max_position_embeddings": 512,
+  "vocab_size": 119547,
+  "activation": "gelu",
+  "attention_dropout": 0.1,
+  "dropout": 0.1,
+  "seq_classif_dropout": 0.2
+}
+```
+
+## Files Included
+
+- `config.json`: Model configuration
+- `tokenizer_config.json`: Tokenizer configuration  
+- `tokenizer.json`: Fast tokenizer file
+- `vocab.txt`: Vocabulary file
+- `special_tokens_map.json`: Special tokens mapping
+- `bert_model.onnx`: ONNX model for inference
+- `bert_model_optimized.onnx`: Optimized ONNX model
+- `bert_model_optimized_dynamic_int8.onnx`: INT8 quantized ONNX model
+- `metrics.yaml`: Detailed performance metrics
+
+## Usage
+
+### With Transformers
+
+```python
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model = DistilBertForSequenceClassification.from_pretrained("your-username/distilled_bert_french_12")
+tokenizer = DistilBertTokenizer.from_pretrained("your-username/distilled_bert_french_12")
+
+# Example inference
+text = "Votre texte français ici"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    
+print(f"Predictions: {predictions}")
+```
+
+### With ONNX Runtime
+
+```python
+import onnxruntime as ort
+from transformers import DistilBertTokenizer
+import numpy as np
+
+# Load tokenizer and ONNX model
+tokenizer = DistilBertTokenizer.from_pretrained("your-username/distilled_bert_french_12")
+session = ort.InferenceSession("bert_model_optimized.onnx")
+
+# Prepare input
+text = "Votre texte français ici"
+inputs = tokenizer(text, return_tensors="np", truncation=True, padding=True, max_length=512)
+
+# Run inference
+outputs = session.run(None, {
+    "input_ids": inputs["input_ids"],
+    "attention_mask": inputs["attention_mask"]
+})
+
+predictions = outputs[0]
+print(f"Predictions: {predictions}")
+```
+
+## Training Details
+
+- **Training Steps**: 8,000
+- **Epochs**: 2
+- **Framework**: PyTorch/Transformers
+- **Optimizer**: AdamW (inferred)
+- **Learning Rate Schedule**: Cosine with warmup (inferred)
+
+## Optimization
+
+The model includes three ONNX variants for different deployment scenarios:
+
+1. **Standard ONNX** (`bert_model.onnx`): Full precision model
+2. **Optimized ONNX** (`bert_model_optimized.onnx`): Graph optimizations applied
+3. **INT8 Quantized** (`bert_model_optimized_dynamic_int8.onnx`): Quantized for faster inference
+
+## License
+
+Please ensure you comply with the original BERT license and any dataset licenses used during training.
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{distilled_bert_french_12,
+  title={DistilBERT French Multilingual Sequence Classification},
+  author={Your Name},
+  year={2024},
+  howpublished={Hugging Face Model Hub},
+  url={https://huggingface.co/your-username/distilled_bert_french_12}
+}
+```
+
+## Contact
+
+For questions or issues, please open an issue in the model repository or contact [your-email@example.com].