Update dataset card

README.md

@@ -23,72 +23,6 @@ configs:
   data_files:
   - split: test
     path: data/test-*
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: question
-    dtype: string
-  - name: answer
-    dtype: string
-  - name: reasoning_steps
-    list: string
-  - name: question_type
-    dtype: string
-  - name: hop_count
-    dtype: int32
-  - name: topology
-    dtype: string
-  - name: motif
-    dtype: string
-  - name: pattern
-    dtype: string
-  - name: document_relationship
-    dtype: string
-  - name: sector
-    dtype: string
-  - name: sub_industry
-    dtype: string
-  - name: difficulty
-    dtype: string
-  - name: idf_score
-    dtype: float64
-  - name: quality_score
-    dtype: int32
-  - name: nodes
-    list:
-    - name: role
-      dtype: string
-    - name: id
-      dtype: string
-    - name: name
-      dtype: string
-    - name: type
-      dtype: string
-    - name: year
-      dtype: int32
-    - name: idf_score
-      dtype: float64
-  - name: hops
-    list:
-    - name: hop
-      dtype: int32
-    - name: relationship
-      dtype: string
-    - name: source_file
-      dtype: string
-    - name: page_id
-      dtype: string
-    - name: chunk_id
-      dtype: string
-    - name: chunk_text
-      dtype: string
-  splits:
-  - name: test
-    num_bytes: 166250550
-    num_examples: 15600
-  download_size: 63399776
-  dataset_size: 166250550
 ---
 
 # Dataset Card for FinMultiHopQA
@@ -136,10 +70,10 @@ from datasets import load_dataset
 
 ds = load_dataset("domyn/FinMultiHopQA", split="test")  # 15,600 questions
 
-# Filter by question type, hop count, topology, or partition
+# Filter by question type, hop count, topology, or motif
 quant = ds.filter(lambda r: r["question_type"] == "quantitative")
 three_hop = ds.filter(lambda r: r["hop_count"] == 3)
-part = ds.filter(lambda r: r["partition"] == "quantitative_3hop_cross_year_type1")
+inter_year = ds.filter(lambda r: r["topology"] == "cross_year" and r["motif"] == "chain")
 
 row = ds[0]
 print(row["question"])
@@ -151,8 +85,8 @@ for hop in row["hops"]:
 
 ### Files and splits
 
-The full benchmark is one file, `data/test.parquet`, with a single `test` split (15,600 rows). Use the
-`question_type`, `hop_count`, `topology`, `motif`, and `partition` columns to select subsets.
+The full benchmark is a single `test` split (15,600 rows). Use the `question_type`, `hop_count`,
+`topology`, and `motif` columns to select subsets.
 
 ### Statistics
 
@@ -178,7 +112,7 @@ Energy 853 · Utilities 853 · Real Estate 371 · Materials 168
 
 ### Partitions
 
-Each partition keeps the top-N candidates by `quality_score` (descending), then `question_id` (ascending).
+Each partition keeps the top-N candidates by `quality_score` (descending), then source question ID (ascending).
 
 | Partition | Questions | Sampling | Quality score (min / max / mean) |
 |---|---:|---|---|
@@ -206,7 +140,6 @@ Each partition keeps the top-N candidates by `quality_score` (descending), then
 | Field | Type | Description |
 |---|---|---|
 | `id` | string | Unique key: `<partition>-<row index>` |
-| `question_id` | int | Generator ID; **not unique** (repeats across generation runs) |
 | `question` | string | The multi-hop question |
 | `answer` | string | Reference answer grounded in the evidence chunks |
 | `reasoning_steps` | list[string] | Step-by-step reasoning chain behind the reference answer |
@@ -214,22 +147,18 @@ Each partition keeps the top-N candidates by `quality_score` (descending), then
 | `hop_count` | int | `2` or `3` |
 | `topology` | string | `intra_doc`, `cross_year`, or `cross_company` (from the partition; use this for topology analysis) |
 | `motif` | string | `chain` (2-hop and 3-hop `type1`) or `branch` (3-hop `type2`) |
-| `partition` | string | Source partition (one of the 18 listed above) |
 | `pattern` | string | Typed KG path, e.g. `ORG -[Invests_In]-> GPE <-[Operates_In]- ORG` |
-| `document_relationship` | string | Original label (see Known Issues) |
+| `document_relationship` | string | `intra_document`, `inter_document_same_company`, or `inter_document_cross_company` |
 | `sector` | string | GICS sector |
 | `sub_industry` | string | GICS sub-industry (null when not resolved) |
 | `difficulty` | string | IDF-based heuristic: `easy` / `medium` / `hard` |
 | `idf_score` | float | Connector IDF score (higher = more specific connector) |
 | `quality_score` | int | LLM-validator rubric total (0–50) |
-| `temporal_change` | string | Present only on 2-hop inter-year rows; always `unknown` in this release |
-| `entities` | list[struct] | Question anchors: `{role, name, type}` |
 | `nodes` | list[struct] | KG path nodes: `{role, id, name, type, year, idf_score}` |
 | `hops` | list[struct] | Evidence per hop: `{hop, relationship, source_file, page_id, chunk_id, chunk_text}` |
-| `generation_file` | string | Generation-run file the pair came from |
 
 Node roles: 2-hop paths use `start`, `intermediate`, `end`; 3-hop chains use `start`, `node_2`,
-`node_3`, `end`; 3-hop branches use `start`, `connector` (the shared node that all three anchors link to).
+`node_3`, `end`; 3-hop branches use `start`, `connector` (the shared node the anchors link to), `end`.
 For every row, `hops[i].source_file` gives the filing behind hop *i*.
 
 ### Example
@@ -250,7 +179,8 @@ For every row, `hops[i].source_file` gives the filing behind hop *i*.
   "quality_score": 48,
   "nodes": [
     {"role": "start", "id": "BA", "name": "BA", "type": "ORG", "year": 2024, "idf_score": null},
-    {"role": "connector", "id": "Asset_Allocation", "name": "Asset Allocation", "type": "ACCOUNTING_POLICY", "year": null, "idf_score": 4.01}
+    {"role": "connector", "id": "Asset_Allocation", "name": "Asset Allocation", "type": "ACCOUNTING_POLICY", "year": null, "idf_score": 4.01},
+    {"role": "end", "id": null, "name": "EMR", "type": "ORG", "year": null, "idf_score": null}
   ],
   "hops": [
     {"hop": 1, "relationship": "Monitors", "source_file": "BA_10k_2024.pdf", "page_id": "page_99", "chunk_id": "chunk_1", "chunk_text": "..."},
@@ -314,10 +244,6 @@ windows with distractors (E3), relevance-sorted descending (E4) and ascending (E
 
 ## Known Issues
 
-- **2-hop intra-document labels:** `document_relationship` is set to `inter_document_cross_company` for all
-  1,500 rows in the two `*_2hop_intra_doc` partitions. Use the `topology` column instead, which is derived
-  from the partition. 3-hop intra-document rows are correct.
-- **`question_id` is not unique.** Use `id` as the row key.
 - **`difficulty`** is an IDF heuristic (~80% `hard`), not a calibrated difficulty measure.
 
 ## Citation