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@jobergum - Arxiv Doc - Benchmark - Cannibalizing LLMs - Chris Manning - Clustering - Domain adaptation - Domain adaptation (NLP) - Embedding Model Fine-tuning - Entities - Few-shot learning - Few-shot text classification - Good - GPL (Generative Pseudo Labeling) - Information retrieval - Knowledge Graphs and NLP - Laure Soulier - LLM - Long documents - Medical Data - Moshe Wasserblat - Neural Search - N-grams - Nils Reimers - NLP: Text Representation - NLP@Facebook - NLP@Stanford - Rare words (NLP) - Representation learning - Sanjeev Arora - sBERT fine-tuning - Sentence-BERT - Sentence Embeddings - SIF embeddings - Synthetic Data - Synthetic QA data - Text Classification - Text Embeddings - Tomas Mikolov - Tree structure - Tweet - Unsupervised Domain Adaptation (NLP) - Unsupervised Sentence Embedding Learning - Vector database - Word embeddings - Yoav Goldberg -

Favoris ; AI girls and guys AND NLP: Text Representation

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45 Documents

Cohere int8 & binary Embeddings - Scale Your Vector Database to Large Datasets

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2024-03-19 About

Jo Kristian Bergum sur X : "...helping people understand the shortcomings of text embedding models for their data. The most powerful has been demonstrating how the embedding models' tokenizers work."

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2024-03-16 About

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

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2024-03-13 About

Announcing Vespa Long-Context ColBERT | Vespa Blog

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2024-03-03 About

[2401.18059] RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

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2024-02-03 About

long document ranking · Issue #11 · stanford-futuredata/ColBERT

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2024-01-31 About

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 About

[2305.12517] Retrieving Texts based on Abstract Descriptions

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2023-06-15 About

@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 About

[2210.07316] MTEB: Massive Text Embedding Benchmark

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2022-10-17 About

Nils Reimers sur Twitter : "MTEB - Massive Text Embedding Benchmark ..."

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2022-10-17 About

[2205.11498] Domain Adaptation for Memory-Efficient Dense Retrieval

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2022-09-26 About

[2209.11055] Efficient Few-Shot Learning Without Prompts

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2022-09-23 About

Christopher Manning sur Twitter : "This seems like an important contribution to the external validity of the (big) recent line of work on long-context transformer models"

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2022-07-18 About

Nils Reimers sur Twitter : "GPL goes multi-lingual..."

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2022-06-01 About

[2205.04260] EASE: Entity-Aware Contrastive Learning of Sentence Embedding

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2022-05-11 About

Ramsri Goutham Golla sur Twitter : "Hi @Nils_Reimers For GPL you used "msmarco-distilbert-base-tas-b" model and ..."

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2022-04-27 About

Sentence Transformer Fine-Tuning (SetFit): Outperforming GPT-3 on few-shot Text-Classification while being 1600 times smaller | by Moshe Wasserblat (2021-12)

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2022-03-31 About

Nils Reimers sur Twitter : "Great post on SetFit"

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2022-03-31 About

[2004.09813] Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation

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2022-03-18 About

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

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2022-03-09 About

Nils Reimers sur Twitter : "Creating intent classes for chatbots is challenging This tutorial shows how to use sentence-transformers to find potentially overlapping intent classes and how to improve your data annotation work." / Twitter

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2022-02-19 About

Nils Reimers sur Twitter : "how to use the fast clustering algorithm from sentence-transformers..."

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2022-02-19 About

sentence-transformers/fast_clustering.py at master · UKPLab/sentence-transformers

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2022-02-18 About

[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 About

[2104.06979] TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning

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> The most
successful previous approaches like InferSent (Conneau
et al., 2017), Universial Sentence Encoder
(USE) (Cer et al., 2018) and SBERT (Reimers and
Gurevych, 2019) heavily relied on labeled data to
train sentence embedding models.
>
> TSDAE can
achieve up to 93.1% of the performance of indomain
supervised approaches. Further, we
show that TSDAE is **a strong domain adaptation
and pre-training method for sentence
embeddings**, significantly outperforming other
approaches like Masked Language Model.

> During training, TSDAE
encodes corrupted sentences into fixed-sized
vectors and requires the decoder to reconstruct the
original sentences from this sentence embedding.

- <https://www.sbert.net/examples/unsupervised_learning/TSDAE/README.html>
- [github](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE)
- [UKPLab/sentence-transformers: Sentence Embeddings with BERT & XLNet](doc:2020/07/ukplab_sentence_transformers_s)
- [twitter](https://twitter.com/KexinWang2049/status/1433361957579538432):

> **TSDAE can learn domain-specific sentence embeddings with unlabeled sentences**
>
> Most importantly, instead of STS (Semantic Textual Similarity), **we suggest evaluating unsupervised sentence embeddings on the domain-specific tasks&datasets, which is the real use case for them**. Actually, STS scores do not correlate with performance on specific tasks.

2021-09-01 About

Nils Reimers sur Twitter : "How to train state-of-the-art sentence embeddings?"

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2021-06-22 About

Contrastive Representation Learning

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2021-06-04 About

A Self-Training Approach for Short Text Clustering - (Hadifar 2019)

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2021-05-20 About

Nils Reimers sur Twitter : "SBERT Release v1.1.0"

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2021-04-22 About

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

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2021-04-17 About

CONCEPTUAL GROUNDING FOR TEXT REPRESENTATION LEARNING

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2019-12-03 About

A Structural Probe for Finding Syntax in Word Representations

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2019-06-10 About

[1703.02507] Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

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2019-03-25 About

nlp-notebooks/Simple Sentence Similarity.ipynb at master · nlptown/nlp-notebooks

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2019-02-07 About

Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline (Ethayarajh 2018)

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2018-10-08 About

A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors (2018)

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A La Carte Embedding

2018-09-18 About

Simple and efficient semantic embeddings for rare words, n-grams, and language features – Off the convex path

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2018-09-18 About

Deep-learning-free Text and Sentence Embedding, Part 2 – Off the convex path

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2018-06-25 About

Deep-learning-free Text and Sentence Embedding, Part 1 – Off the convex path

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2018-06-25 About

A Simple but Tough-to-Beat Baseline for Sentence Embeddings (2017)

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2018-05-10 About

Learning Concept-Driven Document Embeddings for Medical Information Search (2017)

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2018-01-03 About

[1607.01759] Bag of Tricks for Efficient Text Classification

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2017-09-10 About

[1507.07998] Document Embedding with Paragraph Vectors

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2017-08-20 About

[1405.4053] Distributed Representations of Sentences and Documents

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2017-07-10 About