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Model Card for RobeCzech

Version History

  • version 1.1: Version 1.1 was released in Jan 2024, with a change to the tokenizer described below; the model parameters were mostly kept the same, but (a) the embeddings were enlarged (by copying suitable rows) to correspond to the updated tokenizer, (b) the pooler was dropped (originally it was only randomly initialized).

    The tokenizer in the initial release (a) contained a hole (51959 did not correspond to any token), and (b) mapped several tokens (unseen during training but required by the BBPE tokenizer) to the same ID as the [UNK] token (3). That sometimes caused problems, as in https://huggingface.co/ufal/robeczech-base/discussions/4. See https://huggingface.co/ufal/robeczech-base/discussions/4#64b8f6a7f1f8e6ea5860b314 for more information.

    In version 1.1, the tokenizer was modified by (a) removing the hole, (b) mapping all tokens to a unique ID. That also required increasing the vocabulary size and embeddings weights (by replicating the embedding of the [UNK] token). Without finetuning, version 1.1 and version 1.0 gives exactly the same embeddings on any input (apart from the pooler missing in v1.1), and the tokens in version 1.0 that mapped to a different ID than the [UNK] token map to the same ID in version 1.1.

    However, the sizes of the embeddings (and LM head weights and biases) are different, so the weights of the version 1.1 are not compatible with the configuration of version 1.0 and vice versa.

  • version 1.0: Initial version released in May 2021 (with the tokenization issues described above).

    If you want to load a pretrained model, configuration, or a tokenizer of version 1.0, you can use

    from_pretrained("ufal/robeczech-base", revision="v1.0")

    to create an AutoModel, an AutoConfig, or an AutoTokenizer.

Model Details

Model Description

RobeCzech is a monolingual RoBERTa language representation model trained on Czech data.


Direct Use

Fill-Mask tasks.

Downstream Use

Morphological tagging and lemmatization, dependency parsing, named entity recognition, and semantic parsing.

Bias, Risks, and Limitations

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.


Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Training Details

Training Data

The model creators note in the associated paper:

We trained RobeCzech on a collection of the following publicly available texts:

  • SYN v4, a large corpus of contemporary written Czech, 4,188M tokens;
  • Czes, a collection of Czech newspaper and magazine articles, 432M tokens;
  • documents with at least 400 tokens from the Czech part of the web corpus.W2C , tokenized with MorphoDiTa, 16M tokens;
  • plain texts extracted from Czech Wikipedia dump 20201020 using WikiEx-tractor, tokenized with MorphoDiTa, 123M tokens

All these corpora contain whole documents, even if the SYN v4 is block-shuffled (blocks with at most 100 words respecting sentence boundaries are permuted in a document) and in total contain 4,917M tokens.

Training Procedure


The texts are tokenized into subwords with a byte-level BPE (BBPE) tokenizer, which was trained on the entire corpus and we limit its vocabulary size to 52,000 items.

Speeds, Sizes, Times

The model creators note in the associated paper:

The training batch size is 8,192 and each training batch consists of sentences sampled contiguously, even across document boundaries, such that the total length of each sample is at most 512 tokens (FULL-SENTENCES setting). We use Adam optimizer with β1 = 0.9 and β2 = 0.98 to minimize the masked language-modeling objective.

Software Used

The Fairseq implementation was used for training.


Testing Data, Factors & Metrics

Testing Data

The model creators note in the associated paper:

We evaluate RobeCzech in five NLP tasks, three of them leveraging frozen contextualized word embeddings, two approached with fine-tuning:

  • morphological analysis and lemmatization: frozen contextualized word embeddings,
  • dependency parsing: frozen contextualized word embeddings,
  • named entity recognition: frozen contextualized word embeddings,
  • semantic parsing: fine-tuned,
  • sentiment analysis: fine-tuned.


Model Morphosynt PDT3.5 (POS) (LAS) Morphosynt UD2.3 (XPOS) (LAS) NER CNEC1.1 (nested) (flat) Semant. PTG (Avg) (F1)
RobeCzech 98.50 91.42 98.31 93.77 87.82 87.47 92.36 80.13

Environmental Impact

  • Hardware Type: 8 QUADRO P5000 GPU
  • Hours used: 2190 (~3 months)


  author={Straka, Milan and N{\'a}plava, Jakub and Strakov{\'a}, Jana and Samuel, David},
  editor={Ek{\v{s}}tein, Kamil and P{\'a}rtl, Franti{\v{s}}ek and Konop{\'i}k, Miloslav},
  title={{RobeCzech: Czech RoBERTa, a Monolingual Contextualized Language Representation Model}},
  booktitle="Text, Speech, and Dialogue",
  publisher="Springer International Publishing",

How to Get Started with the Model

Use the code below to get started with the model.

Click to expand
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("ufal/robeczech-base")

model = AutoModelForMaskedLM.from_pretrained("ufal/robeczech-base")
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