File size: 3,597 Bytes
21afc66 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | # 🧠 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.
---
## ✨ 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
---
## 🧠 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
---
## 🚫 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)
---
## 🏋️♂️ 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 |
---
## 📊 Evaluation Metrics
| Metric | Score |
|------------|---------|
| BLEU Score | 30.42 |
---
## 🔎 Output Details
- Input: English text string
- Output: Translated German text string
---
## 🚀 Usage
```python
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?"))
```
---
## 📁 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.
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