Instructions to use OpenMatch/co-condenser-large-msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMatch/co-condenser-large-msmarco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="OpenMatch/co-condenser-large-msmarco")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("OpenMatch/co-condenser-large-msmarco") model = AutoModelForMaskedLM.from_pretrained("OpenMatch/co-condenser-large-msmarco") - Notebooks
- Google Colab
- Kaggle
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license: mit
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This model has been pretrained on MS MARCO first, then fine-tuned on
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This model is trained with BERT-large as the backbone with 335M hyperparameters.
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license: mit
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This model has been pretrained on MS MARCO passages first, then fine-tuned on the MS MARCO training set following the approach described in the paper **Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval**. The model can be used to reproduce the experimental results associated GitHub repository is available here https://github.com/OpenMatch/COCO-DR.
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This model is trained with BERT-large as the backbone with 335M hyperparameters.
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