AventIQ-AI/MarianMT-Text-Translation-AI-Model-en-fr

🧠 MarianMT-Text-Translation-AI-Model-"en-fr"

A sequence-to-sequence translation model fine-tuned on English–French sentence pairs. This model translates English text into French and is built using the Hugging Face MarianMTModel. It’s ideal for general-purpose translation, educational use, and light regulatory or formal communication tasks between English and French.

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✨ Model Highlights

• 📌 Based on Helsinki-NLP/opus-mt-en-fr
• 🔍 Fine-tuned on a cleaned parallel corpus of English-French sentence pairs
• ⚡ Translates from English → French
• 🧠 Built using Hugging Face Transformers and PyTorch

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🧠 Intended Uses

• ✅ Translating English feedback, emails, or documents into French
• ✅ Cross-lingual support for customer service or regulatory communication
• ✅ Educational platforms and language learning

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🚫 Limitations

• ❌ Not suitable for informal slang or code-mixed inputs
• 📏 Inputs longer than 128 tokens will be truncated
• 🤔 May produce less accurate translations for highly specialized or domain-specific language
• ⚠️ Not intended for legal, medical, or safety-critical translations without expert review

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🏋️‍♂️ Training Details

Attribute	Value
Base Model	Helsinki-NLP/opus-mt-en-fr
Dataset	Parallel English-French corpus
Task Type	Translation
Max Token Length	128
Epochs	3
Batch Size	16
Optimizer	AdamW
Loss Function	CrossEntropyLoss
Framework	PyTorch + Transformers
Hardware	CUDA-enabled GPU

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📊 Evaluation Metrics

Metric	Score
BLEU Score	27.82

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🔎 Output Details

• Input: English text string
• Output: Translated French text string

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🚀 Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/MarianMT-Text-Translation-AI-Model-en-fr"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    finetuned_model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = finetuned_model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("Hello, how are you?"))

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📁 Repository Structure

finetuned-model/
├── config.json               ✅ Model architecture & config
├── pytorch_model.bin         ✅ Model weights
├── tokenizer_config.json     ✅ Tokenizer settings
├── tokenizer.json            ✅ Tokenizer vocabulary (JSON format)
├── source.spm                ✅ SentencePiece model for source language
├── target.spm                ✅ SentencePiece model for target language
├── special_tokens_map.json   ✅ Special tokens mapping
├── generation_config.json    ✅ (Optional) Generation defaults
├── README.md                 ✅ Model card

🤝 Contributing

Contributions are welcome! Feel free to open an issue or pull request to improve the model, training scripts, or documentation.