RoBERT-v2 Dutch Base MQA Fine-tuned
| Property | Value |
|---|---|
| Model Type | Sentence Transformer |
| Embedding Dimension | 768 |
| Base Model | pdelobelle/robbert-v2-dutch-base |
| Training Data | 1M Dutch FAQ pairs |
| Author | jegormeister |
| Model Hub | HuggingFace |
What is robbert-v2-dutch-base-mqa-finetuned?
This is a specialized Dutch language model fine-tuned for generating semantic sentence embeddings. Built upon the RoBERT-v2 Dutch base model, it has been specifically trained on 1 million FAQ question-answer pairs to optimize its performance in understanding and representing Dutch text semantically.
Implementation Details
The model implements a sentence transformer architecture that maps text to a 768-dimensional dense vector space. It utilizes mean pooling on token embeddings and was trained using MultipleNegativesRankingLoss with a scale of 20.0 and cosine similarity as the similarity function. The training process involved 3 epochs with AdamW optimizer, learning rate of 2e-05, and 10,000 warmup steps.
- Maximum sequence length: 512 tokens
- Batch size: 80
- Weight decay: 0.01
- Pooling strategy: Mean tokens
Core Capabilities
- Semantic search in Dutch content
- Text clustering and similarity analysis
- FAQ matching and retrieval
- Cross-encoder tasks for Dutch language understanding
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in Dutch language understanding and has been fine-tuned on a large dataset of FAQ pairs, making it particularly effective for question-answering and semantic search tasks in Dutch. The combination of RoBERT-v2's strong base architecture with task-specific fine-tuning creates a powerful tool for Dutch NLP applications.
Q: What are the recommended use cases?
The model is ideal for applications requiring semantic understanding of Dutch text, such as building search engines, implementing FAQ systems, clustering similar documents, or finding semantic similarities between texts. It's particularly well-suited for production environments where Dutch language processing is required.
