Instructions to use schreon/xnext-lhm_queries_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use schreon/xnext-lhm_queries_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="schreon/xnext-lhm_queries_encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("schreon/xnext-lhm_queries_encoder") model = AutoModel.from_pretrained("schreon/xnext-lhm_queries_encoder") - Notebooks
- Google Colab
- Kaggle
Upload DPRQuestionEncoder
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
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"_name_or_path": "/home/ma/s/schroederl/XNEXT/xnext/deepfind/dpr/data/2023-01-
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"architectures": [
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"DPRQuestionEncoder"
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"_name_or_path": "/home/ma/s/schroederl/XNEXT/xnext/deepfind/dpr/data/2023-01-19_germnandpr_lhm_large_questions_queries_encoder",
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"architectures": [
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"DPRQuestionEncoder"
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pytorch_model.bin
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oid sha256:ccb720ba4e3e0e6b62afad16237ceb6271b3391b5f43fbe32fdf932c149d7209
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size 437421669
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