Instructions to use OpenMatch/condenser-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMatch/condenser-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="OpenMatch/condenser-large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("OpenMatch/condenser-large") model = AutoModelForMaskedLM.from_pretrained("OpenMatch/condenser-large") - Notebooks
- Google Colab
- Kaggle
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license: mit
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This model has been pretrained on BookCorpus and English Wikipedia following the approach described in the paper **Condenser: a Pre-training Architecture for Dense Retrieval**. The model can be used to reproduce the experimental results
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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 BookCorpus and English Wikipedia following the approach described in the paper **Condenser: a Pre-training Architecture for Dense Retrieval**. The model can be used to reproduce the experimental results within the GitHub repository 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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