widebluesky/wbs-llm-base-demo
Text Generation • 13.2k • Updated • 6
id stringclasses 8
values | document_id stringclasses 8
values | text stringclasses 8
values | split stringclasses 1
value |
|---|---|---|---|
train-0 | train-0 | the sky is blue . the grass is green . | train |
train-1 | train-1 | reset your password in account settings . | train |
train-2 | train-2 | a cat is an animal . a dog is an animal . | train |
train-3 | train-3 | water is cold . the sun is hot . | train |
train-4 | train-4 | change your email in profile settings . | train |
train-5 | train-5 | cancel your subscription in billing settings . | train |
train-6 | train-6 | France capital is Paris . Italy capital is Rome . | train |
train-7 | train-7 | one and one are two . two and two are four . | train |
widebluesky/wbs-llm-base-demo
Demo dataset v1
Small, deliberately synthetic English fixtures used to train and evaluate the
corresponding tiny demo models from advai-notebooks. The records were authored
as repository fixtures, with no external benchmark or customer data source.
This dataset verifies engineering paths and data contracts; it does not establish
domain quality or independent generalization performance.
Original source files are preserved byte-for-byte under source/. JSONL views under
data/ expose explicit subsets and splits through the Hugging Face Datasets library.
The views preserve original values and ordering within each split.
Configurations and splits
| Configuration | Split counts |
|---|---|
| base | train: 8, validation: 2, test: 2 |
Schema and split semantics
Each record contains id, document_id, text and split.
The content includes small general-fact and account-support statements.
Training supervises next tokens in document text; padding is masked.
Document groups and normalized duplicate content do not cross splits.
Loading
from datasets import load_dataset
dataset = load_dataset("widebluesky/wbs-llm-base-demo", "base", revision="demo-v1", token=True)
print(dataset)
These repositories are private. Use your existing Hugging Face login for access; do not embed credentials in code or dataset files. Pin the dataset commit SHA for reproducibility.
For project-native file loading, download the dataset snapshot and point configuration
paths at its original source/ files. The derived subsets are additional loading views.
Corresponding trained models
- widebluesky/wbs-llm-base-demo: verified training data fingerprint
26aec5cfd5b21082af1e8f772dfe41f447ca0275f5cc6b8b8b1d98334e791335.
Validation and provenance
Project validators checked IDs, schemas, references, labels, split groups and negative/candidate rules.
Dataset fingerprints match saved records from the runs that produced the published model weights.
The publication process reloads every subset locally and from a pinned Hub commit and compares all records.
Source file hashes, subset record fingerprints and model weight hashes are recorded in dataset_manifest.json.
Model weights, training state, logs and machine-specific paths are not included.
Source implementation commit: f43a44e2431bae9d0451b445cc3bde5f1e2b8493.
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Models trained or fine-tuned on widebluesky/wbs-llm-base-demo