Instructions to use OpenMatch/cocodr-base-msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMatch/cocodr-base-msmarco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="OpenMatch/cocodr-base-msmarco")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("OpenMatch/cocodr-base-msmarco") model = AutoModelForMaskedLM.from_pretrained("OpenMatch/cocodr-base-msmarco") - Inference
- Notebooks
- Google Colab
- Kaggle
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This model has been first pretrained on the BEIR corpus and fine-tuned on MS MARCO dataset following the approach described in the paper **COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning**. The associated GitHub repository is available here https://github.com/OpenMatch/COCO-DR.
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This model is trained with BERT-base as the backbone with 110M hyperparameters.
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## Usage
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This model has been first pretrained on the BEIR corpus and fine-tuned on MS MARCO dataset following the approach described in the paper **COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning**. The associated GitHub repository is available here https://github.com/OpenMatch/COCO-DR.
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This model is trained with BERT-base as the backbone with 110M hyperparameters. See the paper https://arxiv.org/abs/2210.15212 for details.
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## Usage
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