Feature Extraction
sentence-transformers
Safetensors
English
bert
sparse-encoder
sparse
splade
Generated from Trainer
dataset_size:1000000
loss:SpladeLoss
loss:SparseMarginMSELoss
loss:FlopsLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use rasyosef/splade-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rasyosef/splade-mini with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rasyosef/splade-mini") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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- `Distillation Dataset:` https://huggingface.co/datasets/yosefw/msmarco-train-distil-v2
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- `Code:` https://github.com/rasyosef/splade-tiny-msmarco
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## Usage
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### Direct Usage (Sentence Transformers)
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- `Distillation Dataset:` https://huggingface.co/datasets/yosefw/msmarco-train-distil-v2
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- `Code:` https://github.com/rasyosef/splade-tiny-msmarco
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## Performance
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The splade models were evaluated on 55 thousand queries and 8 million documents from the [MSMARCO](https://huggingface.co/datasets/microsoft/ms_marco) dataset.
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||Size (# Params)|MRR@10 (MS MARCO dev)|
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|`BM25`|-|18.6|-|-|
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|`rasyosef/splade-tiny`|4.4M|30.8|
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|`rasyosef/splade-mini`|11.2M|32.8|
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|`naver/splade-v3-distilbert`|67.0M|38.7|
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## Usage
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### Direct Usage (Sentence Transformers)
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