Instructions to use jzhoubu/dpr-nq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jzhoubu/dpr-nq with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import Retriever model = Retriever.from_pretrained("jzhoubu/dpr-nq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload Retriever
Browse files- config.json +4 -0
- pytorch_model.bin +3 -0
config.json
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{
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"alias": "dpr",
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"device": null,
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"dropout": 0.1,
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"encoder_p": {
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},
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"sequence_length": 256,
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"shared_encoder": false,
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"transformers_version": "4.29.2"
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}
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{
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"alias": "dpr",
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"architectures": [
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"Retriever"
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],
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"device": null,
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"dropout": 0.1,
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"encoder_p": {
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},
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"sequence_length": 256,
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"shared_encoder": false,
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"torch_dtype": "float32",
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"transformers_version": "4.29.2"
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:ea87504cf729d80b3c8e48e38555acc2be43d65a52db990dee7ebd8931bd1641
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size 871274537
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