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sts_bert_microsoft-mpnet-base_cos_dist_ORTHOGONAL_z_False_n_False_c_False_seed12_yahoo_answers_small_train.h5 (469.56 MB)
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sts_bert_microsoft-mpnet-base_cos_dist_ORTHOGONAL_z_False_n_False_c_False_seed13_yahoo_answers_small_train.h5 (469.53 MB)
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sts_bert_microsoft-mpnet-base_cos_dist_ORTHOGONAL_z_False_n_False_c_False_seed22_yahoo_answers_small_test.h5 (20.27 MB)
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sts_bert_microsoft-mpnet-base_cos_dist_ORTHOGONAL_z_False_n_False_c_False_seed22_yahoo_answers_small_train.h5 (469.59 MB)
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sts_bert_microsoft-mpnet-base_cos_dist_ORTHOGONAL_z_False_n_False_c_False_seed23_yahoo_answers_small_train.h5 (469.55 MB)
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sts_bert_microsoft-mpnet-base Yahoo Answers small embeddings

dataset
posted on 2023-05-03, 13:26 authored by Beatrix Miranda Ginn NielsenBeatrix Miranda Ginn Nielsen

Text embeddings for ten Percent of the Yahoo Answers dataset

The Yahoo Answers dataset can be fetched from https://www.kaggle.com/datasets/yacharki/yahoo-answers-10-categories-for-nlp-csv and was made by Zhang et al (https://doi.org/10.48550/arXiv.1509.01626).

 Indexes used can be found in the code repository https://github.com/bemigini/hubness-reduction-sentence-bert. 


Embeddings are made with sentence BERT models where microsoft-mpnet-base (huggingface.co/microsoft/mpnet-base) is used as the base model. 


For more details on models, see the model item: 10.11583/DTU.20708785 


 sts_bert_microsoft-mpnet-base_cos_ORTHOGONAL_z_False_n_True_c_False_seed42_yahoo_answers_small_train.h5 - currently missing

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Danish Pioneer Centre for AI, DNRF grant number P1

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