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Favoris ; Classification AND Few-shot text classification

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Few-shot text classification

22 Documents

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

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2023-12-09 About

Nils Reimers sur Twitter : "Cross-Lingual Text-Classification just from English Data"

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2023-07-20 About

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 About

Andrej Karpathy sur Twitter : "Base LLMs (non-finetuned) make very strong few-shot classifiers. Describe task in English, give few examples, read off the label probabilities on test example. No gradient-based optimization necessary. It brings a cannon to a knife fight but is fast, convenient, strong baseline." / Twitter

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2023-03-19 About

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 About

Few-Shot Text Classification (Cloudera 2020)

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2022-11-24 About

[2104.11882] Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System

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2022-10-25 About

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 About

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

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2022-10-12 About

[2209.11055] Efficient Few-Shot Learning Without Prompts

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2022-09-23 About

What Makes a Good Classification Example?

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2022-08-16 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

[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 About

[1712.05972] Train Once, Test Anywhere: Zero-Shot Learning for Text Classification

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2021-10-16 About

[2010.07245] Text Classification Using Label Names Only: A Language Model Self-Training Approach

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2021-10-16 About

Distill our @huggingface zero-shot classifier with your specified class names

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2021-02-23 About

Zero-shot classifier distillation at master · huggingface/transformers

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2021-02-23 About

Zero-Shot Learning in Modern NLP | Joe Davison Blog (2020-05)

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2021-02-23 About

Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings - (ACL 2019)

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2020-10-05 About

Unsupervised text classification with word embeddings - Max Halford

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2020-10-05 About

Knowledge-Based Short Text Categorization Using Entity and Category Embedding | Springer for Research & Development (2019)

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2019-05-30 About