Semanlink - Information retrieval AND Text Embeddings

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Information retrieval AND Text Embeddings

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DSSM (Deep Semantic Similarity Model)

23 Documents

Nils Reimers sur X : "Embeddings can store only 1 aspect/topic per embedding well."

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2024-03-13 A propos

Jerry Liu sur X : "One major way to improve your RAG system is to fine-tune your embedding model"

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2023-08-25 A propos

[2305.12517] Retrieving Texts based on Abstract Descriptions

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2023-06-15 A propos

@yoavgo sur Twitter : "searching by description can be very useful, but current embedding models will give you texts that are *similar* to the description, rather than texts that *adhere to it*..."

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2023-05-24 A propos

Domain Adaptation with Generative Pseudo-Labeling (GPL) | Pinecone

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2023-04-09 A propos

Classifying long textual documents (up to 25 000 tokens) using BERT | by Sinequa | (2020)

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2023-04-07 A propos

Jo Kristian Bergum sur Twitter : "Do GTP embeddings work for our domain?"

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2023-04-07 A propos

Multilingual Sentence Transformers | Pinecone

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2023-01-13 A propos

[2205.11498] Domain Adaptation for Memory-Efficient Dense Retrieval

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2022-09-26 A propos

NAVER LABS Europe : "@Nils_Reimers of @huggingface on 'Unsupervised domain adaptation for neural search'"

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2022-03-09 A propos

[2112.07577] GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

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An unsupervised domain adaptation technique for dense retrieval models

1. synthetic queries
are generated for each passage from the target corpus (using an existing pre-trained [T5](tag:text_to_text_transfer_transformer)
encoder-decoder)
2. the generated queries are used for mining negative
passages (retrieving the most similar
paragraphs using an existing dense retrieval
model == hard negatives!)
3. the query-passage pairs are labeled by a cross-encoder and used to train the domain-adapted
dense retriever (using method described in [Hofstätter et al.,
2020](doc:2021/12/2010_02666_improving_efficien))

[Nils Reimers sur Twitter](doc:2021/12/nils_reimers_sur_twitter_do_), [GitHub](https://github.com/UKPLab/gpl),  by the author of [TSDAE](doc:2021/09/2104_06979_tsdae_using_trans)

Claims to improve "Doc2Query" [Document Expansion by Query Prediction](doc:2022/01/1904_08375_document_expansion): ([src](https://twitter.com/KexinWang2049/status/1471435779415150598))

> - GPL: Uses doc2query to construct synthetic data and does knowledge distillation (i.e. training) on that data.
> - Doc2query: Generates queries to extend the documents and use BM25 on top of them w/o training.

2021-12-15 A propos

Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine

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2021-12-05 A propos

Unsupervised Training for Sentence Transformers | Pinecone

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2021-11-24 A propos

Multilingual Sentence Transformers | Pinecone

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2021-11-04 A propos

Next-Gen Sentence Embeddings with Multiple Negatives Ranking Loss | Pinecone

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2021-10-27 A propos

Sentence Embeddings and Transformers | Pinecone

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2021-10-23 A propos

Building a sentence embedding index with fastText and BM25 | by David Mezzetti | Towards Data Science

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2021-09-30 A propos

Nils Reimers sur Twitter : "New models for Neural Information Retrieval..."

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2021-04-17 A propos

[1910.03524] Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs

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2019-10-09 A propos

[1908.10084] Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

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2019-08-28 A propos

Sent2Vec: An unsupervised approach towards learning sentence embeddings | RARE Technologies

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2019-03-25 A propos

Learning Deep Structured Semantic Models for Web Search using Clickthrough Data - Microsoft Research (2013)

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2017-12-30 A propos

DSSM ("Deep Semantic Similarity Model") - Microsoft Research

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DSSM (Deep Semantic Similarity Model)

2017-12-30 A propos