Semanlink - fps AND NLP

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Arxiv Doc - ChatGPT - Classification: label correlation - fps: paper - fps' post - Frequently cited paper - GitHub project - Good - Hugging Face - José Moreno - k-nearest neighbors - Multi-label classification - NLP 4 Semanlink - Personal Knowledge Graph - Raphaël Sourty - Semanlink related - SetFit (SBERT fine-tuning) - Tweet -

fps AND NLP

Common descendants

8 Documents

Enrichissement des modèles de langue pré-entraînés par la distillation mutuelle des connaissances - Recherche d’Information et Synthèse d’Information

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2023-11-30 About

FP Servant sur Twitter : "Fun, chatting with #ChatGPT. Starting point was: how to recognize a dog when you see one."

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2023-01-03 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

"Text is the API for humans"

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2021-03-20 About

FP Servant sur Twitter : "constructing a personal knowledge graph as a support for learning (and a metaphor of the learning experience)..."

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2020-12-03 About

raphaelsty/ckb: Contextual knowledge bases

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2020-11-09 About

[1905.10070] Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification

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2019-06-22 About

ML-knn: A lazy learning approach to multi-label learning (2007)

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a lazy learning approach derived from the traditional [k-nearest neighbors algorithm](tag:k_nearest_neighbors_algorithm)

> for each unseen instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, [Maximum a posteriori (MAP)](tag:maximum_a_posteriori_estimation) principle is utilized to determine the label set for the unseen instance.

Implemented in [scikit-multilearn](http://scikit.ml/api/skmultilearn.adapt.mlknn.html), in [java](https://github.com/lefman/mulan-extended/blob/master/mulan/src/mulan/classifier/lazy/MLkNN.java)

> the first lazy approach proposed specifically for multi-label classification. This is also a binary relevance approach which considers each label independently as a binary classification problem. Instead of a standard k-NN method, however, MLkNN uses the maximum a-posteriori (MAP) (Kelleher et al., 2015) approach combined with k-NN. [src](https://pdfs.semanticscholar.org/af9b/33da37d290c063cd826ab5923d96892a9767.pdf)

2018-03-18 About