bi-encoder_msmarco_bert-base_german
| Property | Value |
|---|---|
| License | MIT |
| Paper | mMARCO Paper |
| Primary Task | Semantic Search & Document Retrieval |
| Language | German |
What is bi-encoder_msmarco_bert-base_german?
This is a state-of-the-art German language model specifically designed for semantic search and document retrieval tasks. Built on the BERT architecture, it was trained on the machine-translated MSMARCO dataset using hard negatives and Margin MSE loss, making it particularly effective for asymmetric search scenarios.
Implementation Details
The model leverages the BEIR Benchmark Framework and is trained on approximately 500,000 questions and 8.8 million passages from the MSMARCO dataset, translated to German. The training process incorporates sophisticated hard negatives mining techniques and uses the Margin MSE loss function, which allows for more nuanced learning of semantic relationships.
- Based on deepset/gbert-base architecture
- Trained for 10 epochs with batch size of 75
- Maximum sequence length of 350 tokens
- Implements warmup steps and regular evaluation
Core Capabilities
- Achieves NDCG@1 score of 0.53 on germanDPR benchmark
- Outperforms existing German language models by up to 14 percentage points
- Efficient single-encoder architecture that reduces computational requirements
- Optimized for both sentence similarity and passage retrieval tasks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its superior performance on German language retrieval tasks while using a single encoder architecture, making it more efficient than dual-encoder approaches while still achieving better results. It combines the benefits of MSMARCO training data with sophisticated loss functions and hard negative mining.
Q: What are the recommended use cases?
The model is ideal for semantic search applications, document retrieval systems, and question-answering scenarios in German. It's particularly effective for applications requiring high-precision matching between queries and documents, such as enterprise search or content recommendation systems.
