Library to use the text collectins present in the article: Benchmarking Text Collections for Classification and Clustering Tasks. If you use any part of this code in your research, please cite it using the following BibTex entry

@article{ref:Rossi2013,
  title={Benchmarking text collections for classification and clustering tasks},
  author={Rossi, Rafael Geraldeli and Marcacini, Ricardo Marcondes and Rezende, Solange Oliveira},
  year={2013}
}

!pip install git+https://github.com/GoloMarcos/BTCSR2/

from TextCollectionsLibrary import datasets

datasets_dictionary = datasets.load()

dataset_dictionaty[base name] return a DataFrame

• CSTR
• Classic4
• SiskillWebert
• Webkb-parsed
• Review_polarity
• Re8
• NSF
• indrustry Sector
• Dmoz-Sports
• Dmoz-Science
• Dmoz-Health
• Dmoz-Computers

• Text
• Embedding from BERT
• Embedding from DistilBERT
• Embeddings from Multilingual DistilBERT
• Embedding from RoBERTa
• Document class

• BERT model: bert-large-nli-stsb-mean-tokens
  • Devlin, Jacob, et al. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
• DistilBERT model: distilbert-base-nli-stsb-mean-tokens
  • Sanh, Victor, et al. "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter." arXiv preprint arXiv:1910.01108 (2019).
• RoBERTa model: roberta-large-nli-stsb-mean-tokens
  • Liu, Yinhan, et al. "Roberta: A robustly optimized bert pretraining approach." arXiv preprint arXiv:1907.11692 (2019).
• DistilBERT Multilingual model: distiluse-base-multilingual-cased
  • Sanh, Victor, et al. "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter." arXiv preprint arXiv:1910.01108 (2019).