datalicense

All datasets / ipipan

ipipan/silesian-wikipedia-clean-20230901

29 downloads in 30 days0 likesLast modified 24 May 2024View on Hugging Face
Permissive or share-alike

The declared licence (cc-by-sa-4.0) is classified as share-alike.

Declared licence
cc-by-sa-4.0, from the tagEvidence: README.md at 5abdec1
Licence files
No licence file at the top level of the repositoryEvidence: file list at 5abdec1
Declared sources
None listed in the card metadata
Access gated on Hugging Face
No
Each cell links to what was checked: a file, a file list or a Hub record.

Citation text from the card

@inproceedings{rybak-2024-transferring-bert,
    title = "Transferring {BERT} Capabilities from High-Resource to Low-Resource Languages Using Vocabulary Matching",
    author = "Rybak, Piotr",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1456",
    pages = "16745--16750",
    abstract = "Pre-trained language models have revolutionized the natural language understanding landscape, most notably BERT (Bidirectional Encoder Representations from Transformers). However, a significant challenge remains for low-resource languages, where limited data hinders the effective training of such models. This work presents a novel approach to bridge this gap by transferring BERT capabilities from high-resource to low-resource languages using vocabulary matching. We conduct experiments on the Silesian and Kashubian languages and demonstrate the effectiveness of our approach to improve the performance of BERT models even when the target language has minimal training data. Our results highlight the potential of the proposed technique to effectively train BERT models for 

The same facts for your agent

A free MCP server at mcp.datalicense.uk/mcp. Connect it from an MCP client such as Claude or Cursor.

dataset_licence({ "id": "ipipan/silesian-wikipedia-clean-20230901" })
signal: "permissive"
stated_licence: "cc-by-sa-4.0"
sources: 0