Instructions to use PrimeQA/open-nq-colbert-xlmr-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PrimeQA/open-nq-colbert-xlmr-large with Transformers:
# Load model directly from transformers import AutoTokenizer, HF_ColBERT tokenizer = AutoTokenizer.from_pretrained("PrimeQA/open-nq-colbert-xlmr-large") model = HF_ColBERT.from_pretrained("PrimeQA/open-nq-colbert-xlmr-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Description
This is a retriever model based on ColBERT v2 with bert-base-uncased language model.
This model was trained with the OpenNQ data.
The architecture of the model and hyper parameters are described in the paper ‘Relevance-guided Supervision for OpenQA with ColBERT’.
Intended uses & limitations
This model uses the xlm-roberta-large LM. Biases associated with the pre-trained language model we used may be present in this ColBERT v2 model.
Usage
This model can be used with PrimeQA’s ColBERT engine.
BibTeX entry and citation info
@article{Khattab2021RelevanceguidedSF,
title={Relevance-guided Supervision for OpenQA with ColBERT},
author={O. Khattab and Christopher Potts and Matei A. Zaharia},
journal={Transactions of the Association for Computational Linguistics},
year={2021},
}
@article{Lee2019LatentRF,
title={Latent Retrieval for Weakly Supervised Open Domain Question Answering},
author={Kenton Lee and Ming-Wei Chang and Kristina Toutanova},
journal={ACL},
year={2019}
}
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