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

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Dataset Card for CA-IT Parallel Corpus

Dataset Summary

The CA-IT Parallel Corpus is a Catalan-Italian dataset parallel sentences created to support Catalan in NLP tasks, specifically Machine Translation.

Supported Tasks and Leaderboards

The dataset can be used to train Bilingual Machine Translation models between Italian and Catalan in any direction, as well as Multilingual Machine Translation models.

Languages

The sentences included in the dataset are in Catalan (CA) and Italian (IT).

Dataset Structure

Data Instances

Two separate txt files are provided with the sentences sorted in the same order:

ca-it_corpus.ca
ca-it_corpus.it

The dataset is additionally provided in parquet format: ca-it_corpus.parquet.

The parquet file contains two columns of parallel text obtained from the two original text files. Each row in the file represents a pair of parallel sentences in the two languages of the dataset.

Data Fields

[N/A]

Data Splits

The dataset contains a single split: train.

Dataset Creation

Curation Rationale

This dataset is aimed at promoting the development of Machine Translation between Catalan and other languages, specifically Italian.

Source Data

Initial Data Collection and Normalization

The dataset is a combination of the following original datasets collected from Opus: NLLB, MultiCCAligned, WikiMatrix, GNOME, KDE4, OpenSubtitles, GlobalVoices.

All data was filtered according to two specific criteria:

Alignment: sentence level alignments were calculated using LaBSE and sentence pairs with a score below 0.75 were discarded.
Language identification: the probability of being the target language was calculated using Lingua.py and sentences with a language probability score below 0.5 were discarded.

The filtered datasets are then concatenated and deduplicated to form the final corpus.

Who are the source language producers?

Opus

Annotations

Annotation process

The dataset does not contain any annotations.

Who are the annotators?

[N/A]

Personal and Sensitive Information

Given that this dataset is partly derived from pre-existing datasets that may contain crawled data, and that no specific anonymisation process has been applied, personal and sensitive information may be present in the data. This needs to be considered when using the data for training models.

Considerations for Using the Data

Social Impact of Dataset

By providing this resource, we intend to promote the use of Catalan across NLP tasks, thereby improving the accessibility and visibility of the Catalan language.

Discussion of Biases

No specific bias mitigation strategies were applied to this dataset. Inherent biases may exist within the data.

Other Known Limitations

The dataset contains data of a general domain. Applications of this dataset in more specific domains such as biomedical, legal etc. would be of limited use.