refine datasets name usage in readme

README.md

@@ -108,14 +108,14 @@ bow = load_dataset(
 )
 ```
 
-From the parent of a downloaded repository:
+From the root of the downloaded repository:
 
 ```python
 noow = load_dataset(
-    "csv", data_files={"corpus": "RuWiki-Good-2026-V2/data/noow.csv"}, split="corpus"
+    "csv", data_files={"corpus": "data/noow.csv"}, split="corpus"
 )
 bow = load_dataset(
-    "csv", data_files={"corpus": "RuWiki-Good-2026-V2/data/bow.csv"}, split="corpus"
+    "csv", data_files={"corpus": "data/bow.csv"}, split="corpus"
 )
 ```
 
@@ -133,7 +133,7 @@ from pathlib import Path
 
 csv.field_size_limit(2**31 - 1)
 with Path("data/noow.csv").open(encoding="utf-8", newline="") as source:
-    with Path("../RuWiki-Good-2026-V2-noow.vw").open("w", encoding="utf-8", newline="") as output:
+    with Path("../noow.vw").open("w", encoding="utf-8", newline="") as output:
         for row in csv.DictReader(source):
             output.write(row["vw_text"] + "\n")
 ```
@@ -293,24 +293,40 @@ without information from held-out documents. For text-only experiments, use only
 
 ### Rebuild NLP CSVs from ready article snapshots
 
-Run from the parent directory containing the downloaded `RuWiki-Good-2026-V2`
-and `RuWiki-Good-2026-V2-Selected` repositories. Working files go under a separate
-`ruwiki-rebuild` directory. Choose fresh output and scratch paths on a disk with
-enough space; corpus output and scratch inside a published clone are rejected.
-Python 3.11.15 is the pinned
+Run the preparation commands from `preprocessing/` in the downloaded Full
+collection. Local repository directories may have any names. Replace the paths
+below with your own absolute paths: `work_dir` is a separate working directory,
+and `selected_data` points to the Selected collection's `data/` directory.
+Choose fresh output and scratch paths on a disk with enough space; corpus output
+and scratch inside a published clone are rejected. Python 3.11.15 is the pinned
 runtime for both build and verify: VW escaping depends on its Unicode database.
 On Linux x86-64, use the supplied locks:
 
 ```bash
-conda create --name ruwiki-nlp --file RuWiki-Good-2026-V2/preprocessing/conda-linux-64.lock.txt
+cd "/absolute/path/to/full/preprocessing"
+work_dir="/absolute/path/to/ruwiki-rebuild"
+selected_data="/absolute/path/to/selected/data"
+
+conda create --name ruwiki-nlp --file conda-linux-64.lock.txt
 conda activate ruwiki-nlp
-python -m pip install -r RuWiki-Good-2026-V2/preprocessing/pip-linux-x86_64.lock.txt
-python RuWiki-Good-2026-V2/preprocessing/prepare.py --help
-python RuWiki-Good-2026-V2/preprocessing/prepare.py --stage nlp --action download --dataset both --sources-dir ./ruwiki-rebuild/nlp-sources
-python RuWiki-Good-2026-V2/preprocessing/prepare.py --stage nlp --action build --dataset both --sources-dir ./ruwiki-rebuild/nlp-sources --output-dir ./ruwiki-rebuild/nlp-output --scratch-dir ./ruwiki-rebuild/nlp-scratch
-python RuWiki-Good-2026-V2/preprocessing/prepare.py --stage nlp --action verify --dataset both --sources-dir ./ruwiki-rebuild/nlp-sources --output-dir ./ruwiki-rebuild/nlp-output
+python -m pip install -r pip-linux-x86_64.lock.txt
+python prepare.py --help
+python prepare.py --stage nlp --action download --dataset both \
+  --sources-dir "$work_dir/nlp-sources"
+python prepare.py --stage nlp --action build --dataset both \
+  --sources-dir "$work_dir/nlp-sources" \
+  --output-dir "$work_dir/nlp-output" \
+  --scratch-dir "$work_dir/nlp-scratch"
+python prepare.py --stage nlp --action verify --dataset both \
+  --sources-dir "$work_dir/nlp-sources" \
+  --output-dir "$work_dir/nlp-output"
 ```
 
+Local paths above are independent of Hugging Face repository names. Online
+loading examples and cross-repository links use the published Hugging Face IDs.
+If those IDs change, the links and the downloader's pinned source addresses need
+to be reviewed separately; renaming a local directory does not change them.
+
 `--stage nlp` is the default, so earlier commands without `--stage` continue to
 work. Choose `--dataset full`, `selected` or `both`. The
 [pinned NLP contract](preprocessing/wikipedia-quality-nlp-20261001.json) records
@@ -324,10 +340,10 @@ requirements in a separate environment, retain Python 3.11.15.
 
 Download checks existing source files against their pins, fetches missing files,
 and acquires the pinned NLTK archive at
-`./ruwiki-rebuild/nlp-sources/nltk_data/corpora/stopwords.zip`. To reuse downloaded JSONL files,
-place them at `./ruwiki-rebuild/nlp-sources/full/articles.jsonl.gz`,
-`./ruwiki-rebuild/nlp-sources/selected/articles.jsonl.gz` and
-`./ruwiki-rebuild/nlp-sources/selected/topics.jsonl.gz` before running download. This step creates
+`$work_dir/nlp-sources/nltk_data/corpora/stopwords.zip`. To reuse downloaded JSONL files,
+place them at `$work_dir/nlp-sources/full/articles.jsonl.gz`,
+`$work_dir/nlp-sources/selected/articles.jsonl.gz` and
+`$work_dir/nlp-sources/selected/topics.jsonl.gz` before running download. This step creates
 the local NLTK resource directory; no pre-existing `nltk_data` is required.
 Use `--nltk-data` to select a different local NLTK resource directory.
 
@@ -335,7 +351,7 @@ Hugging Face downloads use anonymous Python `urllib`. For restricted repositorie
 fetch the pinned files with authenticated tools, place them at the paths above,
 and rerun download. The script does not handle authentication tokens.
 Build runs offline, checks runtime versions and Russian stopword content, and
-writes `./ruwiki-rebuild/nlp-output/full` and `./ruwiki-rebuild/nlp-output/selected`. Keep outputs and scratch
+writes `$work_dir/nlp-output/full` and `$work_dir/nlp-output/selected`. Keep outputs and scratch
 separate from published data and use a fresh scratch directory for a new build.
 Progress messages identify phases and document counts. Each build manifest
 records counts and ordered IDs of documents with an empty `@word` namespace;
@@ -358,11 +374,16 @@ one environment, not a bound for other machines.
 Load a generated CSV directly:
 
 ```python
+from pathlib import Path
+
+# Use the same work directory as in the shell commands.
+work_dir = Path("/absolute/path/to/ruwiki-rebuild")
+output_dir = work_dir / "nlp-output/full"
 noow = load_dataset(
-    "csv", data_files={"corpus": "./ruwiki-rebuild/nlp-output/full/noow.csv"}, split="corpus"
+    "csv", data_files={"corpus": str(output_dir / "noow.csv")}, split="corpus"
 )
 bow = load_dataset(
-    "csv", data_files={"corpus": "./ruwiki-rebuild/nlp-output/full/bow.csv"}, split="corpus"
+    "csv", data_files={"corpus": str(output_dir / "bow.csv")}, split="corpus"
 )
 ```
 
@@ -375,25 +396,34 @@ retained Selected annotations. This public raw-dump replay has **not yet been ru
 end to end**. Its download time, build time and peak disk and memory requirements
 have not been measured; the NLP measurements above do not describe this stage.
 
-The example below runs from the parent of both downloaded repository directories,
-as in the NLP commands above.
+Keep the same `preprocessing/` working directory and path variables as in the
+NLP commands above.
 The Selected annotation directory must already contain `labels.json` and
 `topics.jsonl.gz`. These retained mappings reproduce the published subject
 assignments; the script does not rerun AI labeling.
 
 ```bash
-python RuWiki-Good-2026-V2/preprocessing/prepare.py --stage corpus --action download --dataset both --sources-dir ./ruwiki-rebuild/dump-sources
+python prepare.py --stage corpus --action download --dataset both \
+  --sources-dir "$work_dir/dump-sources"
 # Resume an interrupted dump download explicitly:
-python RuWiki-Good-2026-V2/preprocessing/prepare.py --stage corpus --action download --dataset both --sources-dir ./ruwiki-rebuild/dump-sources --resume
-python RuWiki-Good-2026-V2/preprocessing/prepare.py --stage corpus --action build --dataset both --sources-dir ./ruwiki-rebuild/dump-sources --annotations-dir RuWiki-Good-2026-V2-Selected/data --output-dir ./ruwiki-rebuild/corpus-output --scratch-dir ./ruwiki-rebuild/corpus-scratch
-python RuWiki-Good-2026-V2/preprocessing/prepare.py --stage corpus --action verify --dataset both --sources-dir ./ruwiki-rebuild/dump-sources --output-dir ./ruwiki-rebuild/corpus-output
+python prepare.py --stage corpus --action download --dataset both \
+  --sources-dir "$work_dir/dump-sources" \
+  --resume
+python prepare.py --stage corpus --action build --dataset both \
+  --sources-dir "$work_dir/dump-sources" \
+  --annotations-dir "$selected_data" \
+  --output-dir "$work_dir/corpus-output" \
+  --scratch-dir "$work_dir/corpus-scratch"
+python prepare.py --stage corpus --action verify --dataset both \
+  --sources-dir "$work_dir/dump-sources" \
+  --output-dir "$work_dir/corpus-output"
 ```
 
 Download stores and checks the six pinned files beneath `--sources-dir`.
 After all files are present, build is offline. It reconstructs SQL metadata,
 selects qualifying article IDs, extracts the indexed XML revisions, and applies
-the readable-text cleanup. It writes `./ruwiki-rebuild/corpus-output/full/articles.jsonl.gz` and
-`categories.jsonl.gz`, plus `./ruwiki-rebuild/corpus-output/selected/articles.jsonl.gz` and
+the readable-text cleanup. It writes `$work_dir/corpus-output/full/articles.jsonl.gz` and
+`categories.jsonl.gz`, plus `$work_dir/corpus-output/selected/articles.jsonl.gz` and
 `topics.jsonl.gz`. Use separate fresh output and scratch directories, leaving
 published `data/` untouched. Corpus verification compares outputs with the
 independently bundled expected published payloads, rather than trusting hashes
@@ -401,11 +431,19 @@ newly written by the build itself.
 
 For Full only, set `--dataset full` and omit `--annotations-dir`. Selected can
 also be reconstructed from a previously accepted Full corpus without rereading
-the dumps. From the same working directory:
+the dumps. From the same `preprocessing/` directory, resolve Full's data path:
 
 ```bash
-python RuWiki-Good-2026-V2/preprocessing/prepare.py --stage corpus --action build --dataset selected --sources-dir RuWiki-Good-2026-V2/data --full-dir RuWiki-Good-2026-V2/data --annotations-dir RuWiki-Good-2026-V2-Selected/data --output-dir ./ruwiki-rebuild/selected-output --scratch-dir ./ruwiki-rebuild/selected-scratch
-python RuWiki-Good-2026-V2/preprocessing/prepare.py --stage corpus --action verify --dataset selected --sources-dir RuWiki-Good-2026-V2/data --output-dir ./ruwiki-rebuild/selected-output
+full_data="$(cd ../data && pwd -P)"
+python prepare.py --stage corpus --action build --dataset selected \
+  --sources-dir "$full_data" \
+  --full-dir "$full_data" \
+  --annotations-dir "$selected_data" \
+  --output-dir "$work_dir/selected-output" \
+  --scratch-dir "$work_dir/selected-scratch"
+python prepare.py --stage corpus --action verify --dataset selected \
+  --sources-dir "$full_data" \
+  --output-dir "$work_dir/selected-output"
 ```
 
 This Selected-only workflow has been verified byte for byte against the published
@@ -416,9 +454,15 @@ usable as `--sources-dir`. Download then supplies the pinned NLTK archive before
 the offline NLP build:
 
 ```bash
-python RuWiki-Good-2026-V2/preprocessing/prepare.py --stage nlp --action download --dataset both --sources-dir ./ruwiki-rebuild/corpus-output
-python RuWiki-Good-2026-V2/preprocessing/prepare.py --stage nlp --action build --dataset both --sources-dir ./ruwiki-rebuild/corpus-output --output-dir ./ruwiki-rebuild/replay-nlp-output --scratch-dir ./ruwiki-rebuild/replay-nlp-scratch
-python RuWiki-Good-2026-V2/preprocessing/prepare.py --stage nlp --action verify --dataset both --sources-dir ./ruwiki-rebuild/corpus-output --output-dir ./ruwiki-rebuild/replay-nlp-output
+python prepare.py --stage nlp --action download --dataset both \
+  --sources-dir "$work_dir/corpus-output"
+python prepare.py --stage nlp --action build --dataset both \
+  --sources-dir "$work_dir/corpus-output" \
+  --output-dir "$work_dir/replay-nlp-output" \
+  --scratch-dir "$work_dir/replay-nlp-scratch"
+python prepare.py --stage nlp --action verify --dataset both \
+  --sources-dir "$work_dir/corpus-output" \
+  --output-dir "$work_dir/replay-nlp-output"
 ```
 
 The code in `preprocessing/` is available under its [MIT license](preprocessing/LICENSE).