Datasets:

classla
/

xlm-r-bertic-data

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10B<n<100B

License:

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xet

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The JWT signature verification failed. Check the signing key and the algorithm.

Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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XLM-R-BERTić dataset

Composition and usage

This dataset contains 11.5 billion words of texts written in Croatian, Bosnian, Montenegrin and Serbian.

It is an extension of the BERTić-data dataset, a 8.4 billion-words collection used to pre-train the BERTić model (paper). In this dataset there are two major additions: the MaCoCu HBS crawling collection, a collection of crawled news items, and the mC4 HBS dataset. The order of deduplication is as stated in the list of parts/splits:

macocu_hbs
hr_news
mC4
BERTić-data
- hrwac
- classla_hr
- cc100_hr
- riznica
- srwac
- classla_sr
- cc100_sr
- bswac
- classla_bs
- cnrwac

The dataset was deduplicated with onion on the basis of 5-tuples of words with duplicate threshold set to 90%.

The entire dataset can be downloaded and used as follows:

import datasets
dict_of_datasets = datasets.load_dataset("classla/xlm-r-bertic-data")
full_dataset = datasets.concatenate_datasets([d for d in dict_of_datasets.values()])

A single split can be taken as well, but note that this means all the splits will be downloaded and generated, which can take a long time:

import datasets
riznica = datasets.load_dataset("classla/xlm-r-bertic-data", split="riznica")

To circumvent this one option is using streaming:

import datasets
riznica = datasets.load_dataset("classla/xlm-r-bertic-data", split="riznica", streaming=True)
for i in riznica.take(2):
    print(i)
# Output:
# {'text': 'PRAGMATIČARI DOGMATI SANJARI'}
# {'text': 'Ivica Župan'}

Models trained or fine-tuned on classla/xlm-r-bertic-data