Error code:   ConfigNamesError
Exception:    ValueError
Message:      Each config must include `config_name` field with a string name of a config, but got eu_legislation. 
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 79, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1910, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1885, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1236, in get_module
                  metadata_configs = MetadataConfigs.from_dataset_card_data(dataset_card_data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/metadata.py", line 227, in from_dataset_card_data
                  raise ValueError(
              ValueError: Each config must include `config_name` field with a string name of a config, but got eu_legislation.

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YAML Metadata Error: "configs[0]" must be of type object

YAML Metadata Error: "configs[1]" must be of type object

YAML Metadata Error: "configs[2]" must be of type object

YAML Metadata Error: "configs[3]" must be of type object

YAML Metadata Error: "configs[4]" must be of type object

YAML Metadata Error: "configs[5]" must be of type object

YAML Metadata Error: "configs[6]" must be of type object

YAML Metadata Error: "configs[7]" must be of type object

YAML Metadata Error: "configs[8]" must be of type object

YAML Metadata Error: "configs[9]" must be of type object

Dataset Card for "LexFiles"

Dataset Summary

Disclaimer: This is a pre-proccessed version of the LexFiles corpus (https://huggingface.co./datasets/lexlms/lexfiles), where documents are pre-split in chunks of 512 tokens.

The LeXFiles is a new diverse English multinational legal corpus that we created including 11 distinct sub-corpora that cover legislation and case law from 6 primarily English-speaking legal systems (EU, CoE, Canada, US, UK, India). The corpus contains approx. 19 billion tokens. In comparison, the "Pile of Law" corpus released by Hendersons et al. (2022) comprises 32 billion in total, where the majority (26/30) of sub-corpora come from the United States of America (USA), hence the corpus as a whole is biased towards the US legal system in general, and the federal or state jurisdiction in particular, to a significant extent.

Dataset Specifications

Corpus	Corpus alias	Documents	Tokens	Pct.	Sampl. (a=0.5)	Sampl. (a=0.2)
EU Legislation	`eu-legislation`	93.7K	233.7M	1.2%	5.0%	8.0%
EU Court Decisions	`eu-court-cases`	29.8K	178.5M	0.9%	4.3%	7.6%
ECtHR Decisions	`ecthr-cases`	12.5K	78.5M	0.4%	2.9%	6.5%
UK Legislation	`uk-legislation`	52.5K	143.6M	0.7%	3.9%	7.3%
UK Court Decisions	`uk-court-cases`	47K	368.4M	1.9%	6.2%	8.8%
Indian Court Decisions	`indian-court-cases`	34.8K	111.6M	0.6%	3.4%	6.9%
Canadian Legislation	`canadian-legislation`	6K	33.5M	0.2%	1.9%	5.5%
Canadian Court Decisions	`canadian-court-cases`	11.3K	33.1M	0.2%	1.8%	5.4%
U.S. Court Decisions [1]	`court-listener`	4.6M	11.4B	59.2%	34.7%	17.5%
U.S. Legislation	`us-legislation`	518	1.4B	7.4%	12.3%	11.5%
U.S. Contracts	`us-contracts`	622K	5.3B	27.3%	23.6%	15.0%
Total	`lexlms/lexfiles`	5.8M	18.8B	100%	100%	100%

[1] We consider only U.S. Court Decisions from 1965 onwards (cf. post Civil Rights Act), as a hard threshold for cases relying on severely out-dated and in many cases harmful law standards. The rest of the corpora include more recent documents.

[2] Sampling (Sampl.) ratios are computed following the exponential sampling introduced by Lample et al. (2019).

Additional corpora not considered for pre-training, since they do not represent factual legal knowledge.

Corpus	Corpus alias	Documents	Tokens
Legal web pages from C4	`legal-c4`	284K	340M

Citation

Ilias Chalkidis*, Nicolas Garneau*, Catalina E.C. Goanta, Daniel Martin Katz, and Anders Søgaard. LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development. 2022. In the Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics. Toronto, Canada.

@inproceedings{chalkidis-garneau-etal-2023-lexlms,
    title = {{LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development}},
    author = "Chalkidis*, Ilias and 
              Garneau*, Nicolas and
              Goanta, Catalina and 
              Katz, Daniel Martin and 
              Søgaard, Anders",
    booktitle = "Proceedings of the 61h Annual Meeting of the Association for Computational Linguistics",
    month = june,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/xxx",
}

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