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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
| { | |
| "types": { | |
| "": "sentence_transformers.sparse_encoder.models.MLMTransformer.MLMTransformer", | |
| "query_1_SpladePooling": "sentence_transformers.sparse_encoder.models.SpladePooling.SpladePooling", | |
| "document_0_MLMTransformer": "sentence_transformers.sparse_encoder.models.MLMTransformer.MLMTransformer", | |
| "document_1_SpladePooling": "sentence_transformers.sparse_encoder.models.SpladePooling.SpladePooling" | |
| }, | |
| "structure": { | |
| "query": [ | |
| "", | |
| "query_1_SpladePooling" | |
| ], | |
| "document": [ | |
| "document_0_MLMTransformer", | |
| "document_1_SpladePooling" | |
| ] | |
| }, | |
| "parameters": { | |
| "default_route": "query", | |
| "allow_empty_key": true | |
| } | |
| } |