# HerBERT

## Overview

The HerBERT model was proposed in [KLEJ: Comprehensive Benchmark for Polish Language Understanding](https://www.aclweb.org/anthology/2020.acl-main.111.pdf) by Piotr Rybak, Robert Mroczkowski, Janusz Tracz, and

Ireneusz Gawlik. It is a BERT-based Language Model trained on Polish Corpora using only MLM objective with dynamic

masking of whole words.

The abstract from the paper is the following:

*In recent years, a series of Transformer-based models unlocked major improvements in general natural language

understanding (NLU) tasks. Such a fast pace of research would not be possible without general NLU benchmarks, which

allow for a fair comparison of the proposed methods. However, such benchmarks are available only for a handful of

languages. To alleviate this issue, we introduce a comprehensive multi-task benchmark for the Polish language

understanding, accompanied by an online leaderboard. It consists of a diverse set of tasks, adopted from existing

datasets for named entity recognition, question-answering, textual entailment, and others. We also introduce a new

sentiment analysis task for the e-commerce domain, named Allegro Reviews (AR). To ensure a common evaluation scheme and

promote models that generalize to different NLU tasks, the benchmark includes datasets from varying domains and

applications. Additionally, we release HerBERT, a Transformer-based model trained specifically for the Polish language,

which has the best average performance and obtains the best results for three out of nine tasks. Finally, we provide an

extensive evaluation, including several standard baselines and recently proposed, multilingual Transformer-based

models.*

This model was contributed by [rmroczkowski](https://huggingface.co/rmroczkowski). The original code can be found

[here](https://github.com/allegro/HerBERT).

## Usage example

thon

>>> from transformers import HerbertTokenizer, RobertaModel

>>> tokenizer = HerbertTokenizer.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)

>>> # HerBERT can also be loaded using AutoTokenizer and AutoModel:

>>> import torch

>>> from transformers import AutoModel, AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")

>>> model = AutoModel.from_pretrained("allegro/herbert-klej-cased-v1")

Herbert implementation is the same as `BERT` except for the tokenization method. Refer to [BERT documentation](bert) 

for API reference and examples.  

## HerbertTokenizer

[[autodoc]] HerbertTokenizer

## HerbertTokenizerFast

[[autodoc]] HerbertTokenizerFast