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.gitattributes

@@ -58,3 +58,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
 simplewiki-20240720.f16.index.br filter=lfs diff=lfs merge=lfs -text
 wikipedia_simple_20240720.db.br filter=lfs diff=lfs merge=lfs -text
+simplewiki-20240801.f16.index.br filter=lfs diff=lfs merge=lfs -text
+wikipedia_simple_20240801.db.br filter=lfs diff=lfs merge=lfs -text

README.md

@@ -57,7 +57,7 @@ text embeddings for wikipedia articles
       - identical recall accuracy on test queries (i8 performed poorly by contrast)
     * `multi` key support enabled (*so more than one chunk can refer to the same section id*)
 
-the **original [simple english wikipedia dump](https://dumps.wikimedia.org/simplewiki/20240720/)** that was used to generate these artifacts, dated 2024-07-20
+the **original [simple english wikipedia dump](https://dumps.wikimedia.org/simplewiki/20240801/)** that was used to generate these artifacts, dated 2024-08-01
 
 ### modifiable reproduction script `wikiembed.py`
 * *requires [msmarco distilbert tas b coreml](https://huggingface.co/ZachNagengast/coreml-msmarco-bert-base-dot-v5) model as written*

search.py

@@ -25,11 +25,11 @@ def embed(text):
     return predictions['embeddings'][0]
 
 lang = "simple"
-date = "20240720"
+date = "20240801"
 model = ct.models.CompiledMLModel('./msmarco_distilbert_base_tas_b_512_single_quantized.mlmodelc')
 tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-distilbert-base-tas-b")
 
-precision = "f32"
+precision = "f16"
 index_path = f"./{lang}wiki-{date}.{precision}.index"
 index = Index.restore(index_path, view=True)
 
@@ -37,8 +37,9 @@ db_name = f"wikipedia_{lang}_{date}.db"
 conn = sqlite3.connect(db_name)
 cursor = conn.cursor()
 
-query = "who is the current US president?"
-# query = "what is the capital of AUS?"
+query = "what is the capital of AUS?"
+# query = "who is the current US president?"
+# query = "who was the first paraplegic to fully recover spinal & motor function after severing their spinal cord?"
 print(f"🔎 testing search... '{query}'")
 qembed = embed(query)
 res: Matches = index.search(qembed, 5)

simplewiki-20240801-dump.xml.bz2

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:abff873132d5f6214984e693abd627210694480d70cb562d5376143663a7b178
+size 320782193

simplewiki-20240801.f16.index.br

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:482faf7bd9e9bd935ca72687bcb714853bf09aa2fa5171bd723040fbb852395e
+size 854632343

upload.py

@@ -18,7 +18,8 @@ def upload():
         repo_type="dataset",
         repo_id=repo_id,
         allow_patterns=["README.md", "*.py", "*.bz2", "*.br", "requirements.txt"],
-        ignore_patterns=["scraps/*"]
+        ignore_patterns=["scraps/*"],
+        delete_patterns=["*.bz2", "*.br"]
     )
 
 upload()

wikiembed.py

@@ -423,5 +423,5 @@ if __name__ == "__main__":
     model = ct.models.CompiledMLModel('./msmarco_distilbert_base_tas_b_512_single_quantized.mlmodelc')
     tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-distilbert-base-tas-b")
     lang = "simple"
-    date = "20240720"
+    date = "20240801"
     save_wiki_dump(lang, date)

wikipedia_simple_20240801.db.br

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2ac42dc65ae562017e3d4aed884f3242ebb1dfb21963057f90b3808d9e978a52
+size 78344527