Feature Extraction
sentence-transformers
PyTorch
ONNX
English
bert
splade++
document-expansion
sparse representation
bag-of-words
passage-retrieval
knowledge-distillation
document encoder
sparse-encoder
sparse
splade
text-embeddings-inference
Instructions to use seerware/Splade_PP_en_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use seerware/Splade_PP_en_v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("seerware/Splade_PP_en_v2") 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
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<details>
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**1. Lexical search:**
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Lexical search with BOW based sparse vectors are strong baselines, but they famously suffer from vocabulary mismatch problem, as they can only do exact term matching. Here are the pros and cons:
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Please cite if you use our models or libraries. Citation info below.
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T.B.D
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<details>
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**1. Lexical search:**
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Lexical search with BOW based sparse vectors are strong baselines, but they famously suffer from vocabulary mismatch problem, as they can only do exact term matching. Here are the pros and cons:
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Please cite if you use our models or libraries. Citation info below.
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T.B.D
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