# sebischair/Lbl2Vec

<https://repo.masters-masters.com/en/repos/sebischair/Lbl2Vec>

[Home](https://repo.masters-masters.com/en) › [sebischair](https://repo.masters-masters.com/en/owners/sebischair) › [Lbl2Vec](https://repo.masters-masters.com/en/repos/sebischair/Lbl2Vec)

sebischair/Lbl2Vec has 185 stars on GitHub. Its RepoMasters card: Prototype tier. Lbl2Vec learns jointly embedded label, document and word vectors to retrieve…

| Field | Value |
| --- | --- |
| Owner | [sebischair](https://repo.masters-masters.com/en/owners/sebischair) |
| Stars | 185 |
| Forks | 28 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | BSD-3-Clause |
| Created | 2021 |
| Topics | natural-language-processing, word-embeddings, document-embeddings, label-embeddings, unsupervised-classification, nlp, machine-learning, text-classification, python, unsupervised-document-retrieval |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [sebischair/Lbl2Vec](https://github.com/sebischair/Lbl2Vec) |

## Repository

Lbl2Vec learns jointly embedded label, document and word vectors to retrieve documents with predefined topics from an unlabeled document corpus.

The tier for repos with 100 to 249 ★. Published odds per card pulled: 70%. Rarity: Common.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.