CoreAIKit

TextClassify — zero-shot text classification on device

Sort text into labels you name at call time: an intent, a queue, a severity, a yes/no — several questions about one text in a single forward. No training for your label set, and nothing leaves the device.

The model is GLiNER2.5-Decide (fastino, Apache-2.0) exported to Core AI. The whole ML surface is one call:

let classifier = try await TextClassifier()   // the catalog's gliner2.5-decide, downloaded once
let answers = try await classifier.classify(text, tasks: [
    ClassificationTask("intent", labels: ["order_status", "refund_request", "cancel_subscription"]),
    ClassificationTask("aspects", labels: ["battery", "keyboard", "screen"], multiLabel: true, threshold: 0.4),
])
answers["intent"]?.labels          // ["refund_request"]
answers["aspects"]?.probabilities  // every label with its probability

One question has a shorter form:

let (label, probability) = try await classifier.classify(text, labels: ["spam", "ham"])

A task can also carry a prompt (the question to answer about the text) and descriptions (what each label means).

Run

swift run textclassify-cli --text "Can I get that charge refunded?" \
    --task intent=order_status,refund_request,cancel_subscription
swift run textclassify-cli --text "Battery dies before lunch, but the screen is great." \
    --task aspects=battery,keyboard,screen --multi --threshold 0.4 --task sentiment=positive,negative,mixed
swift run textclassify-cli --bundle <dir> --readme readme21.json     # the model card's 21 examples
swift run textclassify-cli --bundle <dir> --gate <oracle fixtures> --pygpu <gate_s256_gpu.json> <gate_s512_gpu.json>

Without --bundle the CLI downloads the catalog’s gliner2.5-decide on first use and caches it (TextClassifier() does the same in an app). --bundle takes a local export instead, the directory the zoo’s GLiNER2.5-Decide export writes: classifier.json, a tokenizer/ folder, and one graph per sequence length (256 and 512 tokens). --multi, --threshold, --prompt and --describe label=text apply to the --task before them.

Notes

• Progress goes to stderr, results to stdout (one JSON line, in gliner2’s classify_text(..., include_confidence=True) shape), so an agent or a script can assert on it.
• The labels of all tasks in one call share the bundle’s label slots (32). The tasks are also part of the model’s input, so asking a second question moves the first one’s probabilities a little: classify with the task set you will use.
• The classifier runs the smallest graph the text fits. Text longer than the largest one is cut at a word boundary from the end, and every result says truncated.
• --gate is what “matches gliner2” means here: for every case of the zoo’s oracle fixtures it compares the collated input (token ids, pieces, the [P]/[L] positions and the shape) with gliner2 2.0.0’s, then runs the graph and compares each decision with gliner2’s fp32 decision and the logits with the oracle’s (and, with --pygpu, with the zoo’s Python run of the same bundle).

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