Add DAPR-ConditionalQA chunk-level retrieval eval (native passages + coref)

README.md

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+---
+language:
+- en
+task_categories:
+- text-retrieval
+size_categories:
+- 1K<n<10K
+dataset_info:
+- config_name: documents
+  features:
+  - name: chunk_id
+    dtype: string
+  - name: chunk
+    dtype: string
+  - name: source_url
+    dtype: string
+  - name: title
+    dtype: string
+  - name: chunk_idx
+    dtype: int64
+  - name: chunk_start_char
+    dtype: int64
+  - name: chunk_end_char
+    dtype: int64
+  splits:
+  - name: test
+    num_bytes: 1761506
+    num_examples: 4936
+  download_size: 600220
+  dataset_size: 1761506
+- config_name: queries
+  features:
+  - name: original_query
+    dtype: string
+  - name: query
+    dtype: string
+  - name: answer
+    list: string
+  - name: score
+    list: int64
+  - name: source_url
+    list: string
+  - name: frag_start_char
+    list: int64
+  - name: frag_end_char
+    list: int64
+  - name: n_gold
+    dtype: int64
+  - name: roles
+    list: string
+  - name: fact
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 26959
+    num_examples: 38
+  download_size: 14279
+  dataset_size: 26959
+configs:
+- config_name: documents
+  data_files:
+  - split: test
+    path: documents/test-*
+- config_name: queries
+  data_files:
+  - split: test
+    path: queries/test-*
+---
+
+# EDGAR 8-K — Context Chunk Retrieval (coreference + near-duplicate)
+
+A contextualized sentence-level (query2chunk) retrieval eval built from **SEC 8-K
+restructuring filings** (EDGAR), for the
+[Chunk-level Retrieval Eval](https://huggingface.co/collections/bowang0911/chunk-level-retrieval-eval)
+collection.
+
+**Why this benchmark exists.** Standard passage benchmarks (e.g. DAPR) don't discriminate
+contextual embedding models, because their answer passages already contain the
+distinguishing entity and have no near-duplicate distractors. This dataset is built to
+stress **document-context disambiguation**:
+
+- **Sentence chunks are coreference-dependent** — the answer sentence says *"the Company"*,
+  never the company name, so resolving it requires the surrounding document.
+- **Near-identical distractors across companies** — 8-Ks are templated, so the corpus holds
+  dozens of near-identical sentences like *"the Company estimates that it will incur charges
+  of approximately $X million"* differing only by amount/company. In isolation these are
+  indistinguishable; only the document context resolves which "Company" is meant.
+
+Example — query *"What restructuring charges does Elastic expect to incur?"* → answer
+*"The Company expects to incur total non-recurring cash charges of approximately $22 million
+to $25 million…"* competes against 58 near-identical charge sentences from other filings.
+
+## Stats
+
+| | count |
+| --- | --- |
+| documents (filings) | 246 |
+| corpus (sentence chunks) | 4,936 |
+| queries | 38 (28 with a supporting-evidence fragment) |
+| near-duplicate charge sentences | 58 |
+| near-duplicate workforce sentences | 53 |
+
+## Graded gold: answer vs supporting evidence
+
+Gold is **graded** via the standard `score` field (no schema change vs the rest of the
+collection):
+
+| `score` | role | meaning |
+| --- | --- | --- |
+| **2** | `answer` | the sentence that directly answers the query (uses coreference) |
+| **1** | `evidence` | a supporting fact that disambiguates the answer (e.g. the sentence binding the company name to *"the Company"*) |
+
+Derive the two metrics from one qrels:
+```python
+standard_recall@k = |topk ∩ {score >= 2}| / |{score >= 2}|   # answer only
+evidence_recall@k = |topk ∩ {score >= 1}| / |{score >= 1}|   # answer + supporting evidence
+```
+`nDCG@k` can consume the graded scores directly. A human-readable `roles` field
+(`answer` / `evidence`) mirrors the score. Backward compatible: datasets that use
+`score = 1` uniformly are answer-only (no evidence tier).
+
+## Configs
+
+### `documents`
+`chunk_id`, `chunk` (one sentence), `source_url` (filing accession — **group chunks by this
+to give a contextual model its document view**), `title` (company + 8-K date),
+`chunk_idx` (sentence position in the filing), `chunk_start_char` / `chunk_end_char`.
+
+### `queries`
+`original_query` / `query`, `answer[]` (gold sentence texts), `score[]` (2=answer, 1=evidence),
+`source_url[]` (filing per gold), `frag_start_char[]` / `frag_end_char[]`, `n_gold`,
+`roles[]` (`answer`/`evidence`), `fact` (`workforce` / `charges`).
+
+Gold matching: a chunk is gold for a query if it shares `source_url` and its
+`[chunk_start_char, chunk_end_char)` overlaps a gold fragment's span (each gold maps to
+exactly one sentence chunk).
+
+## Construction & limitations
+
+Built from EDGAR full-text search for restructuring 8-Ks (2022–2026), one primary filing per
+company. HTML cleaned (inline-XBRL/hidden/exhibit boilerplate stripped, sliced to the Item
+narrative), sentence-split with **NLTK Punkt**, junk/heading/fragment chunks removed.
+
+- **Prototype scale** — 38 queries; the query set skews toward `charges` (workforce answers
+  more often repeat the company name and are filtered out to preserve the coreference axis).
+- **Templated queries** — 2 regex-generated templates (`workforce`, `charges`); phrasing
+  overlaps the gold, so the difficulty is the coreference/near-duplicate disambiguation, not
+  the wording. A natural/LLM-generated query pass is future work.
+- **Evidence coverage** — 28/38 queries have a supporting-evidence fragment; the rest are
+  answer-only.
+
+Source: public SEC EDGAR filings (U.S. government works / public domain).

documents/test-00000-of-00001.parquet

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queries/test-00000-of-00001.parquet

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