Feature Extraction
sentence-transformers
Safetensors
English
distilbert
sparse-encoder
sparse
splade
Generated from Trainer
dataset_size:99000
loss:SpladeLoss
loss:SparseMultipleNegativesRankingLoss
loss:FlopsLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use sparse-encoder/example-splade-distilbert-base-uncased-nq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sparse-encoder/example-splade-distilbert-base-uncased-nq with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sparse-encoder/example-splade-distilbert-base-uncased-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
File size: 506 Bytes
240a804 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"activation": "gelu",
"architectures": [
"DistilBertForMaskedLM"
],
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"initializer_range": 0.02,
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"tie_weights_": true,
"torch_dtype": "float32",
"transformers_version": "4.50.3",
"vocab_size": 30522
}
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