Feature Extraction
sentence-transformers
Safetensors
English
sparse-encoder
sparse
asymmetric
inference-free
splade
Generated from Trainer
dataset_size:99000
loss:SpladeLoss
loss:SparseMultipleNegativesRankingLoss
loss:FlopsLoss
Eval Results (legacy)
Instructions to use sparse-encoder-testing/inference-free-splade-bert-tiny-nq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sparse-encoder-testing/inference-free-splade-bert-tiny-nq with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sparse-encoder-testing/inference-free-splade-bert-tiny-nq") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| { | |
| "types": { | |
| "query_0_SparseStaticEmbedding": "sentence_transformers.sparse_encoder.models.SparseStaticEmbedding.SparseStaticEmbedding", | |
| "document_0_MLMTransformer": "sentence_transformers.sparse_encoder.models.MLMTransformer.MLMTransformer", | |
| "document_1_SpladePooling": "sentence_transformers.sparse_encoder.models.SpladePooling.SpladePooling" | |
| }, | |
| "structure": { | |
| "query": [ | |
| "query_0_SparseStaticEmbedding" | |
| ], | |
| "document": [ | |
| "document_0_MLMTransformer", | |
| "document_1_SpladePooling" | |
| ] | |
| }, | |
| "parameters": { | |
| "default_route": "query", | |
| "allow_empty_key": true | |
| } | |
| } |