Struct DatasetMetadata 

pub struct DatasetMetadata {
    pub name: &'static str,
    pub description: &'static str,
    pub download_url: &'static str,
    pub domain: &'static str,
    pub language: &'static str,
    pub entity_types: &'static [&'static str],
    pub flags: DatasetFlags,
    pub citation: Option<&'static str>,
    pub license: Option<&'static str>,
    pub year: Option<u16>,
    pub paper_url: Option<&'static str>,
}

Complete metadata for a single dataset.

All fields are static references for zero-cost access.

Fields

name: &'static str

Human-readable name

description: &'static str

Short description (1-2 sentences)

download_url: &'static str

Download URL (HuggingFace, GitHub, etc.)

domain: &'static str

Primary domain (news, biomedical, social-media, etc.)

language: &'static str

ISO 639-1/3 language code (en, de, zh, multilingual, etc.)

entity_types: &'static [&'static str]

Entity types in this dataset

flags: DatasetFlags

Capability flags (replaces all is_* methods)

citation: Option<&'static str>

Academic citation (if available)

license: Option<&'static str>

License (MIT, CC-BY, etc.)

year: Option<u16>

Publication year

paper_url: Option<&'static str>

Paper URL (arXiv, ACL Anthology, etc.)

Implementations

impl DatasetMetadata

pub const fn new( name: &'static str, description: &'static str, download_url: &'static str, ) -> Self

Create metadata with required fields only.

pub const fn domain(self, domain: &'static str) -> Self

Builder: set domain.

pub const fn language(self, language: &'static str) -> Self

Builder: set language.

pub const fn entity_types(self, types: &'static [&'static str]) -> Self

Builder: set entity types.

pub const fn flags(self, flags: DatasetFlags) -> Self

Builder: set flags.

pub const fn citation(self, citation: &'static str) -> Self

Builder: set citation.

pub const fn license(self, license: &'static str) -> Self

Builder: set license.

pub const fn year(self, year: u16) -> Self

Builder: set year.

pub const fn paper_url(self, url: &'static str) -> Self

Builder: set paper URL.

pub const fn is_ner(&self) -> bool

Returns true if this dataset supports named entity recognition.

NER datasets provide entity span annotations with type labels.

pub const fn is_coreference(&self) -> bool

Returns true if this dataset contains coreference annotations.

Coreference datasets provide mention clusters or entity chains indicating which mentions refer to the same entity.

pub const fn is_intra_doc_coref(&self) -> bool

Returns true if this dataset contains within-document coreference.

Intra-document coreference datasets link mentions within a single document, forming coreference chains or clusters.

pub const fn is_inter_doc_coref(&self) -> bool

Returns true if this dataset contains cross-document coreference.

Inter-document coreference datasets link entities across multiple documents, enabling cross-document entity resolution.

pub const fn is_temporal_ner(&self) -> bool

Returns true if this dataset contains temporal entity annotations.

Temporal NER datasets include time expressions, events with temporal anchors, or entities with temporal attributes (e.g., historical entities).

pub const fn is_biomedical(&self) -> bool

Returns true if this dataset is from the biomedical domain.

Biomedical datasets include medical, clinical, or life science text with specialized entity types (diseases, genes, proteins, chemicals).

pub const fn is_social_media(&self) -> bool

Returns true if this dataset contains social media text.

Social media datasets include Twitter, Reddit, or other informal text with non-standard capitalization, abbreviations, and informal language.

pub const fn is_specialized_domain(&self) -> bool

Returns true if this dataset is from a specialized domain.

Specialized domain datasets include technical, legal, financial, or other domain-specific text requiring specialized entity recognition.

pub const fn is_relation_extraction(&self) -> bool

Returns true if this dataset supports relation extraction.

Relation extraction datasets provide entity-relation-entity triples, enabling evaluation of models that extract structured relationships.

pub const fn is_historical(&self) -> bool

Returns true if this dataset contains historical text.

Historical datasets include ancient texts, historical documents, or diachronic corpora that test model robustness to language evolution.

pub const fn is_bias_evaluation(&self) -> bool

Returns true if this dataset is designed for bias evaluation.

Bias evaluation datasets test for gender, demographic, or other biases in entity recognition and coreference resolution.

pub const fn is_dialogue_coref(&self) -> bool

Returns true if this dataset contains dialogue coreference annotations.

Dialogue coreference datasets include multi-party conversations, meetings, or interviews where coreference resolution must handle speaker turns and dialogue-specific phenomena (e.g., prosody, gestures).

pub const fn is_joint_ner_re(&self) -> bool

Returns true if this dataset supports joint NER and relation extraction.

Joint datasets provide both entity annotations and relation triples, enabling evaluation of models that perform both tasks simultaneously.

pub const fn is_discontinuous_ner(&self) -> bool

Returns true if this dataset contains discontinuous entity annotations.

Discontinuous entities span non-contiguous tokens (e.g., “left and right ventricle” where “ventricle” is split). Requires specialized evaluation metrics beyond standard span-based NER.

pub const fn is_few_shot(&self) -> bool

Returns true if this dataset is designed for few-shot learning evaluation.

Few-shot datasets typically have small training sets or are used to test zero-shot transfer from related domains.

pub const fn is_multilingual(&self) -> bool

Returns true if this dataset covers multiple languages.

Multilingual datasets enable cross-lingual evaluation and testing of zero-shot transfer between languages.

pub const fn is_constructed_language(&self) -> bool

Returns true if this dataset contains constructed/artificial languages.

Constructed languages (e.g., Esperanto, Klingon) test model generalization to languages with different structural properties than natural languages.

pub const fn is_code_switching(&self) -> bool

Returns true if this dataset contains code-switched text.

Code-switching datasets include text where speakers mix multiple languages within the same utterance, requiring models to handle language boundaries.

pub const fn is_african_language(&self) -> bool

Returns true if this dataset contains African languages.

African language datasets are important for evaluating model performance on under-resourced languages and diverse linguistic structures.

Trait Implementations

impl Clone for DatasetMetadata

fn clone(&self) -> DatasetMetadata

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Copy for DatasetMetadata

impl Debug for DatasetMetadata

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.