Semanlink - BERT + Sentence Embeddings ; Domain adaptation (NLP) AND Few-shot learning

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Adapter modules (LM finetuning) - Alpaca - Arxiv Doc - Dataset quality - EMNLP 2023 - Few-shot learning - Few-shot text classification - fps' post - GitHub project - Moshe Wasserblat - Multi-label Text classification - Nils Reimers - sBERT fine-tuning - Sentence-BERT - Sentence-BERT: Domain adaptation - Sentence embeddings: Domain adaptation - SetFit (SBERT fine-tuning) - Siamese networks - Tweet -

BERT + Sentence Embeddings ; Domain adaptation (NLP) AND Few-shot learning

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SetFit (SBERT fine-tuning)

9 Documents

UKP Lab sur X : "a lightweight solution for few-shot domain-specific sentence classification: AdaSent!..."

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2023-12-09 A propos

Daniel Vila Suero sur Twitter : "Data quality is key for LLMs, but we're building Open Source LLMs with data of "unknown" quality... Introducing Alpaca GarbageCollector..."

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2023-04-05 A propos

ValueError "invalid literal for int() with base 10" in trainer.evaluate (dataset created from pandas) · Issue #228 · huggingface/setfit

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2022-12-13 A propos

Lewis Tunstall sur Twitter : "The SetFit library for few-shot learning with Sentence Transformers now supports *multi-label text classification*..."

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2022-10-14 A propos

huggingface/setfit: Efficient few-shot learning with Sentence Transformers

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2022-10-12 A propos

[2209.11055] Efficient Few-Shot Learning Without Prompts

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2022-09-23 A propos

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 A propos

Nils Reimers sur Twitter : "Great post on SetFit"

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2022-03-31 A propos

[2203.14655] Few-Shot Learning with Siamese Networks and Label Tuning

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> the problem of building text classifiers with little or no training data.
>
> In recent years, an approach based on neural textual entailment models has been found to give strong results on a diverse range of tasks.

(cf. #[NLI](tag:nli), using the input text as the premise and the text representing the label as the hypothesis)

> In this work, we show that **with proper pre-training, Siamese Networks that embed texts and labels** offer a competitive alternative.
>
> We introduce **label tuning: fine-tuning the label embeddings only**. While giving lower performance than model fine-tuning (which updates all params of the model), this approach has the architectural advantage that a single encoder can be shared by many different tasks (we only fine-tune the label embeddings)
> The drop in quality can
be compensated by using a variant of **[Knowledge distillation](tag:knowledge_distillation)**

[Github](https://tinyurl.com/label-tuning), [Tweet](doc:2022/03/thomas_muller_sur_twitter_pa)

2022-03-30 A propos