COCO-DR Large MS MARCO
| Property | Value |
|---|---|
| Parameter Count | 335M |
| Model Type | Dense Retrieval |
| License | MIT |
| Paper | Research Paper |
What is cocodr-large-msmarco?
COCO-DR Large MS MARCO is an advanced dense retrieval model built on BERT-large architecture, specifically designed to address distribution shifts in zero-shot dense retrieval scenarios. The model combines contrastive and distributionally robust learning approaches to enhance retrieval performance across varying data distributions.
Implementation Details
The model utilizes a BERT-large backbone with 335M parameters and implements a two-stage training process: pretraining on the BEIR corpus followed by fine-tuning on the MS MARCO dataset. It can be easily integrated using the HuggingFace transformers library.
- Built on BERT-large architecture
- Pretrained on BEIR corpus
- Fine-tuned on MS MARCO dataset
- Implements contrastive and distributionally robust learning
Core Capabilities
- Zero-shot dense retrieval
- Handling distribution shifts in retrieval tasks
- Text embedding generation
- Document ranking and retrieval
Frequently Asked Questions
Q: What makes this model unique?
The model's unique strength lies in its ability to combat distribution shifts in zero-shot dense retrieval scenarios through a combination of contrastive and distributionally robust learning techniques. This makes it particularly effective for applications where the target data distribution differs from the training data.
Q: What are the recommended use cases?
This model is particularly well-suited for information retrieval tasks, document ranking, and search applications where robust performance across different data distributions is required. It's especially valuable in scenarios where zero-shot capabilities are needed.
