update README.md

README.md

@@ -1,11 +1,86 @@
 ---
 license: cc-by-sa-4.0
 task_categories:
-- token-classification
+  - token-classification
 language:
-- en
+  - en
 size_categories:
-- 10K<n<100K
+  - 10K<n<100K
 tags:
-- not-for-all-audiences
----
+  - not-for-all-audiences
+---
+
+# E6DB
+
+This a dataset is compiled from the e621 database. It currently provides indexes
+for normalizing tags and embeddings for finding similar tags.
+
+## Utilities
+
+### `query_tags.py`
+
+A small command-line utility that finds related tags and generates 2D plots illustrating tag relationships through local PCA projection. It utilizes collaborative filtering embeddings, [computed with alternating least squares](./notebooks/AltLstSq.ipynb).
+
+When only tags are provided as arguments, it displays the top-k most similar tags (where k is set with `-k`). By using `-o plot.png` or `-o -`, it saves or displays a 2D plot showing the local projection of the query and related tags.
+
+Tag categories are represented with the e621 color scheme. Results can be filtered based on one or more categories using the `-c` flag once or multiple times. The `-f` flag sets a post count threshold.
+
+Filtering occurs in the following sequence:
+
+- Tags used fewer than twice are excluded from the dataset; tags with a post count lower than the `-f` threshold are also discarded.
+- If a category filter is specified, only matching tags are retained.
+- For each query tag, the most `-k` similar neighboring tags are selected.
+- The per-query neighbors are printed, and if no plot is being generated, filtering halts at this point.
+- Similarity scores are aggregated across queries, and the `-n` tags closest to all queries are chosen for the PCA.
+- Only the highest `-N` scoring tags are displayed in the plot.
+
+### `e6db.utils`
+
+This [module](./e6db/utils/__init__.py) contains utilities for loading the provided data and use it to normalize tag sets.
+
+- `load_tags` and `load_implications` loads the tags indexes and implications,
+- `TagNormalizer` allows to adapt the spellings of tag and alias for working with various datasets. It can normalize spellings by converting tag strings to numerical ids and back,
+- `TagSetNormalizer` uses the above class along tag implications to normalize tag sets and strip implied tags.
+
+See [this example notebook](./notebooks/Normalize%20tags%20T2I%20dataset.ipynb) that cleans T2I datasets in the sd-script format.
+
+Additionally, the `e6db.utils.numpy` and `e6db.utils.torch` modules provides
+functions to construct post-tag interaction matrices in sparse matrix format. For this
+you'll need to generate the `posts.parquet` file from CSVs.
+
+### `importdb`
+
+Reads the CSVs from [e621 db export](https://e621.net/db_export/)
+
+`python -m e6db.importdb ./data` reads tags, aliases, implications and posts CSV
+files. The following operations are performed:
+
+- Tags used at least twice are assigned numerical ids based on their rank.
+- Computes the transitive closure of implications,
+- For each post, split the tags into direct and implied tag.
+- Write parquets files for tags and posts (~500MB) and convert tags indexes using simple formats described in the next section.
+
+The CSV files must be decompressed beforehand.
+
+## Dataset content
+
+This dataset currently focus on tags alone using simple file formats that are
+easily parsed without additional dependencies.
+
+- `tag2idx.json.gz`: a dictionary mapping tag strings and aliases to numerical id (tag rank),
+- `tags.txt.gz`: list of tags sorted by rank, can be indexed by the ids given by `tag2idx.json.gz`,
+- `tags_categories.bin.gz`: a raw array of bytes representing tag categories in the same order than `tags.txt.gz`,
+- `implications.json.gz`: maps tags id to implied tag ids (including transitive implications),
+- `implications_rej.json.gz`: maps tag strings to a list of implied numerical
+  ids. Keys in implications_rej are tags that have a very little usage (less
+  than 2 posts) and don't have numerical ids associated with them.
+- `implicit_tag_factors.safetensors`: Tag embedding computed by [alternating least squares](./notebooks/AltLstSq.ipynb).
+
+No post data is currently included, since this wouldn't add any useful
+information compared to what's inside the CSVs. If you want the post parquet
+files with normalized tags, you can download the CSVs and run the
+[`e6db.importdb`](#importdb) script yourself.
+
+I plan to compile more post data in the future, such as aesthetic predictions,
+adjusted favcounts, etc. Utilities will then be added to assists with the
+selection of a subset of posts for specific ML tasks.