Update StackOverflow vector dataset card: python

README.md

@@ -1,15 +1,18 @@
 ---
 tags:
-  - vector-database
-  - embeddings
-  - parquet
-  - qdrant
   - stackoverflow
+  - qdrant
+  - embeddings
+  - retrieval
+  - question-answering
+task_categories:
+  - feature-extraction
   - question-answering
-pretty_name: StackOverflow Vector Dataset - Python
+language:
+  - en
+pretty_name: Python StackOverflow Vector Dataset
 license: cc-by-sa-4.0
-size_categories:
-  - 1M<n<10M
+viewer: false
 ---
 
 # Python StackOverflow Vector Dataset
@@ -38,6 +41,7 @@ question_metadata/
   r0_00000.parquet
 chunk_records/
   r0_00000.parquet
+# or, for non-chunked exports:
 question_records/
   r0_00000.parquet
 ```
@@ -49,6 +53,8 @@ question_records/
 - `chunk_records/*.parquet`: chunk-level vector rows when parent-child indexing is enabled.
 - `question_records/*.parquet`: question-level vector rows when non-chunked export is used.
 
+Parent-child exports contain `question_metadata/` and `chunk_records/`. Non-chunked exports contain `question_records/` instead. A repository does not need both `chunk_records/` and `question_records/`.
+
 The Hugging Face upload is one repository per language. During upload, the local leading language directory is removed, so local files such as `python/chunk_records/r0_00000.parquet` appear in this repository as `chunk_records/r0_00000.parquet`.
 
 ## Data Model
@@ -134,4 +140,4 @@ Preserve required attribution and license notices when redistributing derived ar
 
 If you use this dataset, cite the Stack2Graph project and preserve Stack Overflow attribution requirements:
 
-- Stack2Graph: A Structured Knowledge Representation of Stack Overflow Data for Retrieval-based Question Answering
+- Kleybolte, L. A., Ventura, V., & Zarcone, A. (2026). Stack2Graph: A Structured Knowledge Representation of Stack Overflow Data for Retrieval-based Question Answering . In Proceedings of the Knowledge Graphs and Large Language Models Workshop (KG-LLM) @ LREC26 (pp. 120–133). European Language Resources Association (ELRA). https://doi.org/10.63317/583q4d3mwbdu.