widebluesky/wbs-embedding-demo

Independent task dataset v1

This dataset packages the synthetic English fixtures used by the trained embedding demo model. It includes its own training records and a self-contained copy of the corpus, queries and relevance judgments needed for evaluation. Loading and training do not require another dataset repository or model task. Original project-native files are preserved byte-for-byte under source/; data/ contains explicit Datasets JSONL loading views with unchanged records.

These fixtures were authored in advai-notebooks, with no customer data or external benchmark source. They verify engineering contracts, not domain quality.

Configurations and split counts

Configuration	Split counts
queries	train: 12, validation: 3, test: 3
corpus	corpus: 12
qrels	train: 144, validation: 36, test: 36
embedding_training	train: 24

The default configuration is embedding_training.

Schema and evaluation semantics

• queries: query_id, text, group_id, split.
• corpus: doc_id, text, group_id; the corpus is shared across query evaluation splits within this dataset.
• qrels: query_id, doc_id, relevance (0 to 3), assigned to the referenced query split.
• embedding_training: query_id, positive_id, negative_ids; 24 contrastive training examples.

There are 12 documents, 18 queries and 216 synthetic judgments. Training views contain only train queries; validation/test query IDs remain in the separate benchmark views. Query groups and identical queries do not cross splits. Positive document source groups are isolated by query split. Negative labels are checked against judgments.

Ranker training pairs and candidates are excluded from this dataset.

Loading

from datasets import load_dataset

samples = load_dataset(
    "widebluesky/wbs-embedding-demo", "embedding_training",
    revision="d5d422450670f840377c9f8bb3b11b7919cdadfa",
)
queries = load_dataset(
    "widebluesky/wbs-embedding-demo", "queries",
    revision="d5d422450670f840377c9f8bb3b11b7919cdadfa",
)

As of 2026-10-26, this Dataset is public and can be read without a Hugging Face login. For private or gated copies, use your existing login (token=True if needed); never embed credentials in code. Access policy can change independently of data revisions. For project-native training, download a dataset snapshot and point task configuration paths at source/corpus.jsonl, source/queries.jsonl, source/qrels.jsonl and source/embedding_samples.json.

Versions and automatic Parquet view

Training examples above pin the original demo-v1 data commit d5d422450670f840377c9f8bb3b11b7919cdadfa. Documentation on main may evolve without changing that tag, source files, splits, training fingerprints or corresponding model weights.

Hugging Face automatically maintains a Parquet browsing/query view on refs/convert/parquet. Its converter discussion is a success notification; no merge, format migration or retraining is required. Keep using the pinned JSONL Dataset configurations, or source/ files for project-native training.

The conversion ref is maintained by the Hub and can change. If reading its Parquet files directly, pin that ref's resolved commit separately and verify the config, split and record contents against the training revision. JSONL and Parquet file hashes differ by encoding; compare decoded records rather than expecting equal bytes. See the official Parquet documentation.

Trained model and provenance

Corresponding model: widebluesky/wbs-embedding-demo.

Canonical benchmark fingerprint: f96589e88e79ce684f3c8733ae1a3c98a8f028459f6e82e3477efeb2867b2a19.

Training sample fingerprint: cebc151f0e9c04eae8c92a1d79a8c3ae6771b1a81fb7484e7a13a34555ee9173.

Project validators verify formats, IDs, references, splits and training/candidate rules. Fingerprints match the saved records from the run that produced the published model. Every configuration is reloaded locally and from a pinned Hub commit and all records are compared before updating model associations. Source hashes, view fingerprints and published model weight hashes are recorded in dataset_manifest.json. No weights, optimizer state, logs, credentials or machine-specific paths are included.

Migration

This task dataset supersedes the shared repository for this model's active association. The original shared dataset was recorded at the following historical revision. On 2026-10-26 its Hub repository returned HTTP 404 with current credentials; current availability could not be verified: widebluesky/wbs-retrieval-demo. The same small benchmark records are intentionally copied into each task dataset; training records and dataset versioning are maintained independently.