distilled_bert_with_french

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

{
  "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

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

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:

@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 [[email protected]].