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Supervised machine learning AND Unsupervised machine learning

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39 Documents

[2401.12178] In-Context Learning for Extreme Multi-Label Classification

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2024-03-17 About

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

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2023-12-09 About

P-tuning for sequence classification

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2023-08-28 About

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

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2023-07-20 About

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

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2023-04-07 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

Active Learning for BERT: An Empirical Study - ACL Anthology

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2022-09-02 About

What Makes a Good Classification Example?

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2022-08-16 About

[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 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

[1901.04085] Passage Re-ranking with BERT

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2021-03-26 About

Rodrigo Nogueira sur Twitter : "Slides of our WSDM 2021 tutorial "Pretrained Transformers for Text Ranking: BERT and Beyond"

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2021-03-09 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

raphaelsty/ckb: Contextual knowledge bases

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2020-11-09 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

Paris NLP Season 4 Meetup #3 – Paris NLP (2020)

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2020-01-23 About

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

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2019-08-28 About

20% Accuracy Bump in Text Classification with ME-ULMFiT

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2019-06-23 About

"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 About

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

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2019-05-30 About

A Technique for Building NLP Classifiers Efficiently with Transfer Learning and Weak Supervision

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2019-05-22 About

[1904.08398] DocBERT: BERT for Document Classification

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2019-04-18 About

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

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2018-12-01 About

[1801.06146] Universal Language Model Fine-tuning for Text Classification

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2018-01-19 About

[1602.06797] Semi-supervised Clustering for Short Text via Deep Representation Learning

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2017-11-07 About