AventIQ-AI/Ai-Translate-Model-Eng-German

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

A sequence-to-sequence translation model fine-tuned on English–German sentence pairs. This model translates English text into German and is built using the Hugging Face MarianMTModel. It’s suitable for general-purpose translation, language learning, and formal or semi-formal communication across English and German.

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✨ Model Highlights

• 📌 Base Model: Helsinki-NLP/opus-mt-en-de
• 📚 Fine-tuned on a cleaned and tokenized parallel English-German dataset
• 🌍 Direction: English → German
• 🔧 Framework: Hugging Face Transformers + PyTorch

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🧠 Intended Uses

• ✅ Translating English content (emails, documentation, support text) into German
• ✅ Use in educational platforms for learning German
• ✅ Supporting cross-lingual customer service, product documentation, or semi-formal communications

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🚫 Limitations

• ❌ Not optimized for informal, idiomatic, or slang expressions
• ❌ Not ideal for legal, medical, or sensitive content translation
• 📏 Sentences longer than 128 tokens are truncated
• ⚠️ Domain-specific accuracy may vary (e.g., legal, technical)

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🏋️‍♂️ Training Details

Attribute	Value
Base Model	Helsinki-NLP/opus-mt-en-de
Dataset	WMT14 English-German
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	30.42

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🔎 Output Details

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

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🚀 Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
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")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

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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.