SADiLaR Language Resource Repository
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536 research outputs found
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NCHLT Siswati GloVe embeddings
Static word embedding model based on the Global Vectors architecture (Pennington et al., 2014). The embeddings provide real-valued vector representations for Siswati text
NCHLT Setswana word2vec-CBOW embeddings
Static word embeddings for the continuous bag of words (CBoW) flavour of the word2vec (w2v) architecture (Mikolov et al., 2013). The embedding provides real-valued vector representations for Setswana text
NCHLT Tshivenḓa fastText-Skipgram embeddings
Static word and subword embeddings for the Skipgram flavour of the fastText architecture (Bojanowski et al., 2017). The embedding provides real-valued vector representations for Tshivenḓa text
NCHLT Sepedi fastText-CBoW embeddings
Static word and subword embeddings for the continuous bag of words (CBoW) flavour of the fastText architecture (Bojanowski et al., 2017). The embedding provides real-valued vector representations for Sepedi text
NCHLT Siswati RoBERTa language model
Contextual masked language model based on the RoBERTa architecture (Liu et al., 2019). The model is trained as a masked language model and not fine-tuned for any downstream process. The model can be used both as a masked LM or as an embedding model to provide real-valued vectorised respresentations of words or string sequences for Siswati text
Autshumato Monolingual Tshivenḓa Corpus
Monolingual corpus for Tshivenḓa. The data is given as a single UTF-8 text file, with each segment on a newline
NCHLT isiNdebele fastText-Skipgram embeddings
Static word and subword embeddings for the Skipgram flavour of the fastText architecture (Bojanowski et al., 2017). The embedding provides real-valued vector representations for isiNdebele text
NCHLT isiZulu GloVe embeddings
Static word embedding model based on the Global Vectors architecture (Pennington et al., 2014). The embeddings provide real-valued vector representations for isiZulu text
NCHLT isiNdebele word2vec-Skipgram embeddings
Static word embeddings for the Skipgram flavour of the word2vec (w2v) architecture (Mikolov et al., 2013). The embedding provides real-valued vector representations for isiNdebele text
Child speech database for the South African context: Speech samples of typically developing Afrikaans and Sesotho sa Leboa-speaking children
This dataset contains child speech samples from typically developing Afrikaans and Sesotho sa Leboa-speaking children in South Africa. The recordings, totaling at least 700 minutes per language, were collected in naturalistic interactions between children and trained speech-language therapists using standardized toys and books. Data were gathered in home and clinical settings to support linguistic analysis, speech transcription, and the development of automated tools for early identification and intervention in multilingual contexts.
For more details, review the readme.txt for Afrikaans and Sesotho sa Lebo