Feature Extraction
sentence-transformers
Safetensors
bert
sparse-encoder
sparse
splade
Generated from Trainer
dataset_size:415
loss:CachedSpladeLoss
loss:SparseMultipleNegativesRankingLoss
loss:FlopsLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use i1j/retriever-sparse-splade with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use i1j/retriever-sparse-splade with sentence-transformers:
from sentence_transformers import SparseEncoder model = SparseEncoder("i1j/retriever-sparse-splade") queries = ["Which planet is known as the Red Planet?"] documents = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) - Notebooks
- Google Colab
- Kaggle
Download modules.json from i1j/retriever-sparse-splade: direct link, hf CLI and curl.
- Browser
- Download file 307 Bytes
-
https://huggingface.co/i1j/retriever-sparse-splade/resolve/main/modules.json
- Command line
-
hf download hf://i1j/retriever-sparse-splade/modules.json
-
curl -L -o modules.json https://huggingface.co/i1j/retriever-sparse-splade/resolve/main/modules.json
307 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.sparse_encoder.modules.mlm_transformer.MLMTransformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_SpladePooling", | |
| "type": "sentence_transformers.sparse_encoder.modules.splade_pooling.SpladePooling" | |
| } | |
| ] |