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README.md

@@ -26,6 +26,37 @@ configs:
     path: data/train.jsonl
   - split: validation
     path: data/valid.jsonl
+dataset_info:
+- config_name: corpus
+  features:
+  - name: docid
+    dtype: int64
+  - name: document
+    dtype: string
+  splits:
+  - name: corpus
+    num_examples: 109650
+- config_name: pairs
+  features:
+  - name: query
+    dtype: string
+  - name: docid
+    dtype: int64
+  - name: nq_id
+    dtype: string             # IMPORTANT: string, not int – uint64 IDs overflow signed int64
+  - name: url
+    dtype: string
+  - name: title
+    dtype: string
+  - name: long_answer
+    dtype: string
+  - name: short_answer
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 307373
+  - name: validation
+    num_examples: 7830
 ---
 
 # NQ320K (NCI-style preprocessing)
@@ -42,8 +73,8 @@ Hugging Face `google-research-datasets/natural_questions` snapshot.
 | `train` pairs    | 307,373  |
 | `validation` pairs | 7,830 |
 
-* Train pairs with non-empty `long_answer`:  **152,147 / 307,373** (49.5%)
-* Train pairs with non-empty `short_answer`: **98,073 / 307,373** (31.9%)
+* Train pairs with non-empty `long_answer`:  **152,148 / 307,373** (49.5%)
+* Train pairs with non-empty `short_answer`: **106,926 / 307,373** (34.8%)
 * Date built: 2026-04-30
 
 ## Schema
@@ -117,6 +148,29 @@ These are **inherited from NCI's preprocessing** and intentional:
   Only `content` has its tags stripped. This is the canonical NCI format.
 * **Non-detokenized hyphenation**: `"post - apocalyptic"`, `"Spider - Man"`.
 
+## Caveat: `nq_id` is a string
+
+NQ's original `example_id` is a **uint64**, and roughly half of the IDs
+exceed `2^63 = 9.22 × 10^18`. They fit unsigned but overflow signed int64.
+
+`nq_id` is therefore stored as a **string**, exactly as Google publishes it.
+**Do not auto-cast it to int64** — about 50% of the values would silently
+wrap to negative numbers. If you load with pandas:
+
+```python
+import pandas as pd
+df = pd.read_json("train.jsonl", lines=True, dtype={"nq_id": str})
+```
+
+If you load with `datasets`, the typed `dataset_info` in this card already
+enforces `string`, so you don't need to do anything extra:
+
+```python
+from datasets import load_dataset
+ds = load_dataset("<your-username>/NQ320K-NCI", "pairs")
+print(ds["train"].features["nq_id"])  # Value(dtype='string', id=None)
+```
+
 ## License & attribution
 
 This dataset is a derivative of the Natural Questions dataset by Google

checksums.sha256

@@ -1,3 +1,3 @@
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-414295c365f21d88bc8aad825476a972f661ee8f186294149a5cf3692704e88b  data/valid.jsonl
+2d2a39fc9a6b5e4ad3ee30b6b4c523c15bc7a9a2ef486114da9544f375e10c20  data/train.jsonl
+ce1a1b913e4f44ddeac78cf0c3c9ff373a360eb32e4ef4085ea14b70d1c8a924  data/valid.jsonl

data/train.jsonl

@@ -1,3 +1,3 @@
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-size 316728058
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+size 317868919