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Favoris ; Entreprise AND Classification

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SetFit (SBERT fine-tuning)

41 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

P-tuning for sequence classification

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2023-08-28 A propos

Unlocking the Power of Cross-Lingual Classification in NLP

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2023-07-20 A propos

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

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2023-07-20 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

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

Few-Shot Text Classification (Cloudera 2020)

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2022-11-24 A propos

Text classification by labeling words | Proceedings of the 19th national conference on Artifical intelligence (2004)

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2022-11-08 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

Active Learning for BERT: An Empirical Study - ACL Anthology

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2022-09-02 A propos

Tu Vu sur Twitter : "Enormous LMs like GPT-3 exhibit impressive few-shot performance, but w/ self-training a BERT base sized model can achieve much better results!

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2022-04-13 A propos

[2203.10581] Cluster & Tune: Boost Cold Start Performance in Text Classification

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[Leshem Choshen sur Twitter : "Labelled data is scarce, what can we do?..."](doc:2022/04/leshem_choshen_sur_twitter_l)

> **One-sentence Summary**: we suggest adding an unsupervised intermediate classification step, before finetunning and after pretraining BERT, and show it improves performance for data-constrained cases.

> for text classification cold start (when labeled
data is scarce), **add an intermediate unsupervised
classification task**, between the pretraining
and fine-tuning phases:
> perform clustering and
train the pre-trained model on predicting the
cluster labels.

> this additional
classification phase can significantly improve
performance, mainly for **topical classification**
tasks

> we use an efficient clustering technique,
that relies on simple Bag Of Words (BOW)
representations, to partition the unlabeled training
data into relatively homogeneous clusters of text
instances.
>
> Next, we treat these clusters as labeled
data for an intermediate text classification task, and
train the pre-trained model – with or without additional
MLM pretraining – with respect to this
multi-class problem, prior to the final fine-tuning
over the actual target-task labels

> The underlying
intuition is that inter-training the model
over a related text classification task would be more
beneficial compared to MLM inter-training, which
focuses on different textual entities, namely predicting
the identity of a single token.

2022-04-06 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

New pipeline for zero-shot text classification - 🤗Transformers - Hugging Face Forums

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2021-03-15 A propos

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

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2021-02-23 A propos

Zero-shot classifier distillation at master · huggingface/transformers

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2021-02-23 A propos

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

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2021-02-23 A propos

raphaelsty/ckb: Contextual knowledge bases

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2020-11-09 A propos

Sylvain Gugger sur Twitter : "Training a transformer model for text classification..."

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2020-10-19 A propos

Hugging Face sur Twitter : "No labeled data? No problem. The 🤗 Transformers master branch now includes a built-in pipeline for zero-shot text classification...

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2020-08-12 A propos

The Extreme Classification Repository

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2020-08-12 A propos

Everything you always wanted to know about extreme classification (but were afraid to ask) - Microsoft Research - 2019

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2020-08-12 A propos

SLEEC: Sparse Local Embeddings for Extreme Multi-label Classification (2015)

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2020-08-11 A propos

FastHugs | ntentional

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2020-02-19 A propos

"I made a bet that a Naive Bayes classifier would work as well on humor recognition as a neural net with fine-tuned Bert embeddings. I won"

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2019-06-06 A propos

Combining Knowledge with Deep Convolutional Neural Networks for Short Text Classification (2017)

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2019-05-20 A propos

[1904.08398] DocBERT: BERT for Document Classification

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2019-04-18 A propos

Tutorial on Text Classification (NLP) using ULMFiT and fastai Library in Python - Analytics Vidhya

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2018-12-01 A propos

Practical guide to text classification | Google Developers

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2018-07-23 A propos

Text Classification with TensorFlow Estimators

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2018-04-17 A propos

Extreme Multi-label Learning with Label Features for Warm-start Tagging, Ranking & Recommendation (2018)

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2018-03-04 A propos

[1712.09405] Advances in Pre-Training Distributed Word Representations

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2017-12-29 A propos

Implementing a CNN for Text Classification in TensorFlow – WildML

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2017-11-06 A propos

A Ranking Approach to Keyphrase Extraction - Microsoft Research (2009)

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2017-06-27 A propos

Text Classification With Word2Vec - DS lore (2016)

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2017-05-18 A propos

Challenges of the email domain for text classification

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2015-10-20 A propos