hn_topic_model

Inputs

Outputs

from spl import SPLClient

result = SPLClient.embedded().call(
    "splime://@splime.project/case4-hn/hn_topic_model@1",
    kwargs={},
    trust=True,
)

Review before running. Public bundles contain executable code. Verify the publisher and exact content hash; a virtual environment isolates dependencies, not the operating system.

Evidence

Evidence-backed concepts

• Hn topic model
• No model file
• Caller model
• Retraining required
• Immutable fitted JSON
• Model included native
• Native scalar dependency
• Dependency signed release

• v131 Oct 20262 downloads

• case5_first_pd1_predictApply the fitted first_pd1 rules embedded in this release. Returns application IDs, predicted classes, probabilities and segments. No caller-side model file or retraining is required.
• tag_titlesAssign HN titles to topics with the immutable trained model embedded as a required native graph dependency. Automatic names use learned top terms; no caller model file or retraining. Returns hn-topics-v1 predictions.
• case5_first_pd1_modelRetrieve the fitted first_pd1 JSON model, exact rules and input schema embedded in this immutable release.
• hn_topic_digestFetch real HN stories, normalize them, apply the required immutable embedded trained model, count observed attention and render notebook-safe HTML. Training is a separate operation. Published implementation executes locally in embedded mode.
• find_rulesSearch numeric classification rules with a dataset and target column. Skip unsupported columns and return their names and reasons in unusable_columns, alongside the model, predictions and holdout metrics.