11 dataset results for Amharic

The Universal Dependencies (UD) project seeks to develop cross-linguistically consistent treebank annotation of morphology and syntax for multiple languages. The first version of the dataset was released in 2015 and consisted of 10 treebanks over 10 languages. Version 2.7 released in 2020 consists of 183 treebanks over 104 languages. The annotation consists of UPOS (universal part-of-speech tags), XPOS (language-specific part-of-speech tags), Feats (universal morphological features), Lemmas, dependency heads and universal dependency labels.

505 PAPERS • 12 BENCHMARKS

CC100

This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages. This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository.

96 PAPERS • NO BENCHMARKS YET

WikiANN

WikiANN (PAN-X)

WikiANN, also known as PAN-X, is a multilingual named entity recognition dataset. It consists of Wikipedia articles that have been annotated with LOC (location), PER (person), and ORG (organization) tags in the IOB2 format¹². This dataset serves as a valuable resource for training and evaluating named entity recognition models across various languages.

57 PAPERS • 3 BENCHMARKS

OSCAR

OSCAR or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture. The dataset used for training multilingual models such as BART incorporates 138 GB of text.

55 PAPERS • NO BENCHMARKS YET

MasakhaNER

MasakhaNER is a collection of Named Entity Recognition (NER) datasets for 10 different African languages. The languages forming this dataset are: Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Luo, Nigerian-Pidgin, Swahili, Wolof, and Yorùbá.

46 PAPERS • 2 BENCHMARKS

XL-Sum

XL-Sum is a comprehensive and diverse dataset for abstractive summarization comprising 1 million professionally annotated article-summary pairs from BBC, extracted using a set of carefully designed heuristics. The dataset covers 44 languages ranging from low to high-resource, for many of which no public dataset is currently available. XL-Sum is highly abstractive, concise, and of high quality, as indicated by human and intrinsic evaluation.

41 PAPERS • NO BENCHMARKS YET

MasakhaNEWS

MasakhaNEWS is a benchmark dataset for news topic classification covering 16 languages widely spoken in Africa.

5 PAPERS • NO BENCHMARKS YET

Amharic - English Parallel Corpus for Machine Translation

Amharic - English Parallel Corpus for Machine Translation contains 33,955 sentence pairs extracted text from such news platforms as Ethiopian Press Agency1, Fana Broadcasting Corporate2, and Walta Information Center3. As the data we used is from different sources, it includes various domains such as religious (Bible and Quran), politics, economics, sports, news, among others.

1 PAPER • NO BENCHMARKS YET

Amharic Error Corpus

Amharic Error Corpus is a manually annotated spelling error corpus for Amharic, lingua franca in Ethiopia. The corpus is designed to be used for the evaluation of spelling error detection and correction. The misspellings are tagged as non-word and real-word errors. In addition, the contextual information available in the corpus makes it useful in dealing with both types of spelling errors.

1 PAPER • NO BENCHMARKS YET

An Amharic News Text classification Dataset

In NLP, text classification is one of the primary problems we try to solve and its uses in language analyses are indisputable. The lack of labeled training data made it harder to do these tasks in low resource languages like Amharic. The task of collecting, labeling, annotating, and making valuable this kind of data will encourage junior researchers, schools, and machine learning practitioners to implement existing classification models in their language. In this short paper, we aim to introduce the Amharic text classification dataset that consists of more than 50k news articles that were categorized into 6 classes. This dataset is made available with easy baseline performances to encourage studies and better performance experiments.

1 PAPER • 1 BENCHMARK

WMT 2021 Ge'ez-Amharic

WMT 2021 Ge'ez-Amharic is a Ge'ez-Amharic dataset prepared for NMT tasks of the 6th Workshop on NLP at Debre Berhan University, Ethiopia. The corpus has been collected from:

0 PAPER • NO BENCHMARKS YET

Datasets

11 dataset results for Amharic