Contriever-base-msmarco
| Property | Value |
|---|---|
| Author | nthakur |
| Architecture | BERT-based with Mean Pooling |
| Vector Dimension | 768 |
| Max Sequence Length | 509 tokens |
| Model Hub | HuggingFace |
What is contriever-base-msmarco?
Contriever-base-msmarco is a specialized sentence transformer model designed for generating dense vector representations of text. It's specifically trained on the MS MARCO dataset, making it particularly effective for information retrieval and semantic search applications. The model converts sentences and paragraphs into 768-dimensional vectors that capture semantic meaning, enabling efficient similarity comparisons and clustering.
Implementation Details
The model implements a two-stage architecture consisting of a BERT-based transformer followed by a mean pooling layer. It can be easily used through either the sentence-transformers library or HuggingFace's transformers library, offering flexibility in implementation.
- Utilizes mean pooling strategy for generating sentence embeddings
- Supports both sentence-level and paragraph-level encoding
- Handles sequences up to 509 tokens in length
- Implements attention masking for accurate averaging
Core Capabilities
- Dense vector generation for text similarity tasks
- Semantic search implementation
- Document clustering and organization
- Cross-lingual information retrieval
- Efficient text matching and comparison
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimization on the MS MARCO dataset and its efficient implementation of the Contriever architecture, making it particularly effective for information retrieval tasks. The 768-dimensional output vectors provide a good balance between computational efficiency and semantic representation power.
Q: What are the recommended use cases?
The model is best suited for applications requiring semantic search, document similarity comparison, clustering of text data, and information retrieval systems. It's particularly effective when you need to compare or match text passages based on their meaning rather than exact keyword matches.
