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") - Notebooks
- Google Colab
- Kaggle
File size: 758 Bytes
966075e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"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
}
} |