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 SparseEncoder model = SparseEncoder("seerware/Splade_PP_en_v2") 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
Add backlink to V1
Browse files
README.md
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This work stands on the shoulders of 2 robust researches: [Naver's From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective paper](https://arxiv.org/pdf/2205.04733.pdf) and [Google's SparseEmbed](https://storage.googleapis.com/gweb-research2023-media/pubtools/pdf/79f16d3b3b948706d191a7fe6dd02abe516f5564.pdf).
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Props to both the teams for such a robust work.
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## 1. What are Sparse Representations and Why learn one?
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**Beginner ?** expand this. **Expert in Sparse & Dense representations ?** feel free skip to next section 2,
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This work stands on the shoulders of 2 robust researches: [Naver's From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective paper](https://arxiv.org/pdf/2205.04733.pdf) and [Google's SparseEmbed](https://storage.googleapis.com/gweb-research2023-media/pubtools/pdf/79f16d3b3b948706d191a7fe6dd02abe516f5564.pdf).
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Props to both the teams for such a robust work.
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**This is a 2nd iteration in this series. Try V1 here:** [prithivida/Splade_PP_en_v1](https://huggingface.co/prithivida/Splade_PP_en_v1)
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## 1. What are Sparse Representations and Why learn one?
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**Beginner ?** expand this. **Expert in Sparse & Dense representations ?** feel free skip to next section 2,
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