language: pl
tags:
- herbert
license: cc-by-4.0
HerBERT
HerBERT is a BERT-based Language Model trained on Polish corpora using Masked Language Modelling (MLM) and Sentence Structural Objective (SSO) with dynamic masking of whole words. For more details, please refer to: HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish.
Model training and experiments were conducted with transformers in version 2.9.
Corpus
HerBERT was trained on six different corpora available for Polish language:
| Corpus | Tokens | Documents |
|---|---|---|
| CCNet Middle | 3243M | 7.9M |
| CCNet Head | 2641M | 7.0M |
| National Corpus of Polish | 1357M | 3.9M |
| Open Subtitles | 1056M | 1.1M |
| Wikipedia | 260M | 1.4M |
| Wolne Lektury | 41M | 5.5k |
Tokenizer
The training dataset was tokenized into subwords using a character level byte-pair encoding (CharBPETokenizer) with
a vocabulary size of 50k tokens. The tokenizer itself was trained with a tokenizers library.
We kindly encourage you to use the Fast version of the tokenizer, namely HerbertTokenizerFast.
Usage
Example code:
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-large-cased")
model = AutoModel.from_pretrained("allegro/herbert-large-cased")
output = model(
**tokenizer.batch_encode_plus(
[
(
"A potem szedł środkiem drogi w kurzawie, bo zamiatał nogami, ślepy dziad prowadzony przez tłustego kundla na sznurku.",
"A potem leciał od lasu chłopak z butelką, ale ten ujrzawszy księdza przy drodze okrążył go z dala i biegł na przełaj pól do karczmy."
)
],
padding='longest',
add_special_tokens=True,
return_tensors='pt'
)
)
License
CC BY 4.0
Citation
If you use this model, please cite the following paper:
@inproceedings{mroczkowski-etal-2021-herbert,
title = "{H}er{BERT}: Efficiently Pretrained Transformer-based Language Model for {P}olish",
author = "Mroczkowski, Robert and
Rybak, Piotr and
Wr{\'o}blewska, Alina and
Gawlik, Ireneusz",
booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
month = apr,
year = "2021",
address = "Kiyv, Ukraine",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bsnlp-1.1",
pages = "1--10",
}
Authors
The model was trained by Machine Learning Research Team at Allegro and Linguistic Engineering Group at Institute of Computer Science, Polish Academy of Sciences.
You can contact us at: klejbenchmark@allegro.pl