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")# pip install -U transformers accelerate # 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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download document_1_SpladePooling/config.json from naver/efficient-splade-VI-BT-large-query: direct link, hf CLI and curl.
- Browser
- Download file 106 Bytes
-
https://huggingface.co/naver/efficient-splade-VI-BT-large-query/resolve/main/document_1_SpladePooling/config.json
- Command line
-
hf download hf://naver/efficient-splade-VI-BT-large-query/document_1_SpladePooling/config.json
-
curl -L -o config.json https://huggingface.co/naver/efficient-splade-VI-BT-large-query/resolve/main/document_1_SpladePooling/config.json
106 Bytes
| { | |
| "pooling_strategy": "max", | |
| "activation_function": "relu", | |
| "word_embedding_dimension": null | |
| } |