pipelineVerified releasev1
tag_titles
Assign 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.
Interface
Verified contract
Inputs
| Name | Type |
|---|---|
| snapshot | dict |
Outputs
| Name | Type |
|---|---|
| model | unspecified |
| result | unspecified |
Exact release
Pinned local call
from spl import SPLClient
result = SPLClient.embedded().call(
"splime://@splime.project/case4-hn/tag_titles@1",
kwargs={'snapshot': {}},
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
- Tag titles
- No caller model file
- Hn topics v1 predictions
- Assign hn titles topics
- Immutable trained model
- Model embedded required
- Required native graph
- Automatic names use
History
Public versions
- v12 downloads
Discover
Similar Objects
- 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.
- hn_topic_modelReturn the immutable fitted JSON topic model included as a native Scalar dependency in this signed release. No model file, caller model input or retraining is required.
- train_topic_modelFit a simple TF-IDF and nonnegative matrix factorization topic model on supplied training titles only. Returns portable JSON vocabulary, IDF weights, learned components, automatic top-term names and a content identity; no rule-generated labels.
- 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.
- predict_topicsApply a supplied fitted HN topic model without fitting vocabulary or components. Returns topic assignments, automatic keyword tags, unknown-topic diagnostics and unchanged observed popularity values.