Work

HJ-Ky-0.1: fastText Models

Year: 2024

Type: dataset

Abstract: Here we release fastText embeddings described in the paper as follows: [...] we trained fastText embeddings on the Leipzig Corpus data. The training scheme was alsoSkip-Gram Negative Sampling, with 10 epochs, vector dimensions of 100 and 300, a window size of 5,and 10 negative samples. Character n-grams of 3 to 6 characters and 2,000,000 hashing buckets wereused for the hashing trick.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14544742

Authors: Alekseev, Anton, Kabaeva, Gulnara

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7223757752

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