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--- |
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license: cc-by-sa-3.0 |
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task_categories: |
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- question-answering |
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tags: |
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- wikipedia |
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- usearch |
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- distilbert |
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- msmarco |
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- text |
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size_categories: |
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- 100K<n<1M |
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--- |
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# wikiembed.py |
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text embeddings for wikipedia articles |
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## artifacts |
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**sqlite3 db** with `article_sections` table, containing cleaned wikipedia article contents by section |
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- `id` - unique section id, generated by sqlite |
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- `article_id` - wikipedia’s provided id for the article to which the section belongs |
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- `title` - the article title |
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- `url` - the article url |
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- `sequence_id` - the per-article section order number |
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- `section_name` - the header name from Wikipedia, or 'Lead' which has no heading. |
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- `text` - the cleaned text contents for the section |
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[usearch](https://unum-cloud.github.io/usearch/) **index for semantic search** of wikipedia section contents |
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- keyed on the section `id` from the sqlite `article_sections` table |
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- vector embeddings generated with [msmarco distilbert](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-tas-b) model in [coreml](https://huggingface.co/ZachNagengast/coreml-msmarco-bert-base-dot-v5) |
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* 768 dimensions |
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* f32 precision |
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* 512 token limit |
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- section contents chunked & embedded with page title and section name in a chunk header |
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- indexed with the following parameters: |
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* [inner product](https://zilliz.com/blog/similarity-metrics-for-vector-search#Inner-Product) distance metric |
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* connectivity (`M` in HSNW terms) of 16 |
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- tested 16, 32, 64, & 200; 16 was smallest disk size with identical recall accuracy on test queries |
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* <details><summary><i>index sizes with various connectivity<>precision config pairs</i></summary> |
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``` |
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❯ du -sh ./20240720/connectivity-*/* |
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979M ./20240720/connectivity-16/simplewiki-20240720.f16.index |
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1.8G ./20240720/connectivity-16/simplewiki-20240720.f32.index |
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1.8G ./20240720/connectivity-200/simplewiki-20240720.f16.index |
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1.8G ./20240720/connectivity-200/simplewiki-20240720.f32.index |
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1.0G ./20240720/connectivity-32/simplewiki-20240720.f16.index |
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1.9G ./20240720/connectivity-32/simplewiki-20240720.f32.index |
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1.2G ./20240720/connectivity-64/simplewiki-20240720.f16.index |
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2.0G ./20240720/connectivity-64/simplewiki-20240720.f32.index |
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``` |
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</details> |
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* index expansion add (`efConstruction` in HNSW terms) of 128 (usearch default) |
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* quantizing embedding vectors from f32 to f16 |
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- identical recall accuracy on test queries (i8 performed poorly by contrast) |
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* `multi` key support enabled (*so more than one chunk can refer to the same section id*) |
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the **original [simple english wikipedia dump](https://dumps.wikimedia.org/simplewiki/20240801/)** that was used to generate these artifacts, dated 2024-08-01 |
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### modifiable reproduction script `wikiembed.py` |
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* *requires [msmarco distilbert tas b coreml](https://huggingface.co/ZachNagengast/coreml-msmarco-bert-base-dot-v5) model as written* |
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* [coremltools](https://github.com/apple/coremltools) prediction only works on macOS[*](https://apple.github.io/coremltools/docs-guides/source/model-prediction.html), which was used to generate the vector embeddings for the semantic index. for cross-platform coreml prediction, check out [tvm](https://tvm.apache.org/docs/how_to/compile_models/from_coreml.html#load-pretrained-coreml-model) |
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* other dependencies described in `requirements.txt`: |
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```sh |
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pip3 install -r requirements.txt |
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``` |
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* make any desired changes to e.g. index parameters, wikipedia dump language or date, or embeddings model in the script |
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* run the script to download, clean, persist & index the dump contents |
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```sh |
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chmod +x wikiembed.py |
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./wikiembed.py |
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``` |
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--- |
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released under [creative commons license (CC BY-SA 3.0 unported)](https://en.wikipedia.org/wiki/Wikipedia:Text_of_the_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License) |
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data cleaning adapted from [olm/wikipedia](https://huggingface.co/datasets/olm/wikipedia) |
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by [britt lewis](https://bl3.dev) |
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