lm2445/FinTagging1000_Grounding

FinTagging Context-Aware Concept Tagging Test Set

This dataset combines the table-context and text-context test splits derived from FinTagging_800_200_HF. It is intended for the second tagging step: given an extracted numeric entity, datatype, local context, and original context, predict the valid XBRL concept tag or tags.

Identical inputs are grouped. If the same input maps to multiple valid concepts, the target is a JSON list containing all ground-truth concepts.

Splits

Split	Rows	Source occurrences	Table rows	Text rows	Multi-tag inputs
test	2,509	2,520	2,341	168	11

Columns

• source_sample_idx: original source row index.
• context_id: original context identifier.
• split: always test.
• input_type: table or text.
• input: JSON string containing the model input fields.
• input_fields: structured version of input.
• output: JSON list of ground-truth XBRL concept tags.
• ground_truth_concepts: structured version of output.
• ground_truth_count: number of unique valid concepts for the input.
• source_entity_indices: original numeric_entities indices grouped into this row.
• source_match_statuses: first-stage parser match statuses for audit only.
• source_occurrence_count: number of source entity occurrences grouped into this row.

Input Format

For table inputs, input_fields contains:

{"numeric_entity": "...", "datatype": "...", "row_context": "...", "column_context": "...", "original_context": "..."}

For text inputs, input_fields contains:

{"numeric_entity": "...", "datatype": "...", "sentence_context": "...", "original_context": "..."}