Fill-Mask
Transformers
PyTorch
Safetensors
English
bert
splade
query-expansion
document-expansion
bag-of-words
passage-retrieval
knowledge-distillation
document encoder
Instructions to use naver/efficient-splade-VI-BT-large-query with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use naver/efficient-splade-VI-BT-large-query with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="naver/efficient-splade-VI-BT-large-query")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("naver/efficient-splade-VI-BT-large-query") model = AutoModelForMaskedLM.from_pretrained("naver/efficient-splade-VI-BT-large-query") - Inference
- Notebooks
- Google Colab
- Kaggle
Add sentence-transformers library_name and filtering tags
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by tomaarsen HF Staff - opened
README.md
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- passage-retrieval
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- knowledge-distillation
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- document encoder
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datasets:
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- ms_marco
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- passage-retrieval
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- knowledge-distillation
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- document encoder
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- sparse-encoder
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- sparse
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pipeline_tag: feature-extraction
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library_name: sentence-transformers
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datasets:
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- ms_marco
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