OpenOranje
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TweeTaal-nl-en-0.6B

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 ---
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 ---
 library_name: transformers
+tags:
+- en-nl-translation
+- translation
+license: apache-2.0
+datasets:
+- OpenOranje/ReOpus-ApolloBooks-EN-NL-1M
+language:
+- nl
+metrics:
+- bleu
+- rouge
+base_model:
+- Qwen/Qwen3-0.6B
 ---
+# OpenOranje/TweeTaal-nl-en-0.6B
+## Model Description
+The TweeTaal-en-nl model has been fine-tuned on Dutch-English translation pairs to provide accurate, fluent translations. The compact 0.6B parameter size makes it suitable for deployment in resource-constrained environments while maintaining strong translation quality.
+### Intended Use
+**Primary Use Case**: Translating Dutch text to English across various domains
+**Recommended Applications**:
+- General-purpose Dutch-to-English translation
+- Content localization
+- Cross-lingual communication tools
+- Educational language learning applications
 ## Training Details
 ### Training Procedure
+**Method**: Supervised Fine-Tuning (SFT)
+- The model was trained on parallel Dutch-English text pairs
+- Standard cross-entropy loss optimization
+- The base Qwen3-0.6b model was adapted specifically for translation tasks
+### Training Data
+The model was trained on Dutch-English parallel corpora. (Note: Specify your actual dataset details, such as:
+- Dataset name and source
+- Number of training examples
+- Domain coverage (general, technical, literary, etc.)
+- Data preprocessing steps)
+## Usage
+### Basic Usage Example
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+# Load model and tokenizer
+model_name = "OpenOranje/qwen3-0.6b-dutch-english"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+# Prepare input
+dutch_text = "Hallo, hoe gaat het met je?"
+prompt = f"Translate from Dutch to English:\n{dutch_text}"
+message = [{"role":"user", "content": prompt}]
+# Generate translation
+inputs = tokenizer.apply_chat_template(message, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.7)
+translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(translation)
+```
+### Prompt Format
+The model expects input in the following format:
+```
+Translate the following text from Dutch to English:\n{dutch_text}"
+```
+### Inference Parameters
+Recommended generation parameters:
+- **Temperature**: 0.7 (adjust for creativity vs. consistency)
+- **Max tokens**: Set based on expected translation length
+- **Top-p**: 0.9 (nucleus sampling)
+## Performance
+### Benchmark Results
+##### WMT-2024 Translations
+| Metric | WMT-24 (Finetuned)| WMT-24 (Base)|
+|--------|-------------------| -------------|
+| BLEU   |      46.3         |       32.1   |
+| Rouge  |      73.1         |       58.3   |
+##### Long Context SQuAD Translations
+| Metric | TweeTaal | Base |
+|--------|--------  | -----|
+| BLEU   |  58.3    |      |
+| Rouge  |  77.9    |      |
+## Limitations
+- **Unidirectional**: Trained specifically for Dutch→English;
+- **Context Length**: Trained on 4096 Tokens
+- **Rare Words**: May struggle with highly specialized terminology or rare vocabulary not well-represented in training data
+- **Informal Language**: Performance on slang, dialects, or very informal Dutch may vary
+## Ethical Considerations
+- **Training Data Bias**: The model may reflect biases present in the training data
+- **Cultural Nuances**: Some cultural expressions may not translate perfectly
+## Contact
+For questions or issues, please contact: [theaisarth@proton.me][[email protected]]
+---
+## Additional Resources
+- **Base Model**: [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
+- **Training Code**: [TBD]
+- **Dataset**: [Data](https://huggingface.co/datasets/OpenOranje/ReOpus-ApolloBooks-EN-NL-1M)
+## Version History
+- **v1.0** (2025-10-24): Initial release
+---
+**License**: [Apache 2.0]