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README.md

@@ -71,6 +71,7 @@ Example:
   "source": {
     "dataset": "wikimedia/wikipedia",
     "article_id": "5438",
+    "source_article_hash": "...article_hash...",
     "lang": "en"
   },
   "beads": [

probes.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ebd230cd02f2c010c3114310e1d93e71a189f22b4a6346a0067dd116834315b6
-size 849544261
+oid sha256:436cb790b33b8f878210dfd312ae7154c9dfe46c9c87e4fc8f9582b7f87f47ba
+size 853994261

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "finesse-benchmark-database"
-version = "0.1.13"
+version = "0.1.14"
 description = "Data generation factory for atomic probes in Finesse benchmark. Generates probes_atomic.jsonl from Wikimedia Wikipedia."
 authors = ["winter.sci.dev <enzoescipy@gmail.com>"]
 readme = "README.md"

src/finesse_benchmark_database/chunker.py

@@ -18,7 +18,7 @@ from chunker import generate_all_strings_of_beads
 strings_of_beads = generate_all_strings_of_beads()
 # Result: List[Dict] where each inner dict contains 'source' and 'beads' keys.
 """
-
+import hashlib
 import logging
 import random
 from typing import List, Dict
@@ -79,12 +79,15 @@ def generate_all_strings_of_beads(config: ProbeConfig) -> List[Dict]:
         for example in dataset:
             if len(strings_for_lang) >= config.samples_per_language:
                 break
-            
+
             # Extract and clean text
             text = example.get("text", "").strip()
             if not text:
                 continue
-            
+
+            # Compute article hash for deduplication
+            article_hash = hashlib.sha256(example.get("text", "").encode('utf-8')).hexdigest()
+
             # Extract article ID for metadata
             article_id = example.get("id", "")
             
@@ -104,10 +107,12 @@ def generate_all_strings_of_beads(config: ProbeConfig) -> List[Dict]:
             
             # Only add the string if it has at least one bead
             if beads:
+
                 string_data = {
                     "source": {
                         "dataset": "wikimedia/wikipedia",
                         "article_id": article_id,
+                        "source_article_hash": article_hash,
                         "lang": lang
                     },
                     "beads": beads