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allegro/
herbert-klej-cased-tokenizer-v1

Name: herbert-klej-cased-tokenizer-v1
Availability: InStock

transformers

xlm

endpoints_compatible

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HerBERT tokenizer

HerBERT tokenizer is a character level byte-pair encoding with vocabulary size of 50k tokens. The tokenizer was trained on Wolne Lektury and a publicly available subset of National Corpus of Polish with fastBPE library. Tokenizer utilize XLMTokenizer implementation from transformers.

Tokenizer usage

Herbert tokenizer should be used together with HerBERT model:

python

from transformers import XLMTokenizer, RobertaModel

tokenizer = XLMTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
model = RobertaModel.from_pretrained("allegro/herbert-klej-cased-v1")

encoded_input = tokenizer.encode("Kto ma lepszą sztukę, ma lepszy rząd – to jasne.", return_tensors='pt')
outputs = model(encoded_input)

License

CC BY-SA 4.0

Citation

If you use this tokenizer, please cite the following paper:

@inproceedings{rybak-etal-2020-klej,
    title = "{KLEJ}: Comprehensive Benchmark for {P}olish Language Understanding",
    author = "Rybak, Piotr  and
      Mroczkowski, Robert  and
      Tracz, Janusz  and
      Gawlik, Ireneusz",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.111",
    doi = "10.18653/v1/2020.acl-main.111",
    pages = "1191--1201",
}

Authors

Tokenizer was created by Allegro Machine Learning Research team.

You can contact us at: klejbenchmark@allegro.pl