What causes "Error running testGibbs inference!" in stanfordnlp/CoreNLP?

Inspect the cause chain (e.getCause()) — the real failure is inside classifyGibbs; fix that underlying error.

stanfordnlp/CoreNLP · error · RuntimeException

Error running testGibbs inference!

Error message

Error running testGibbs inference!

What it means

classify() supports Gibbs-sampling inference when flags.doGibbs is true; any exception thrown by classifyGibbs (e.g. from the transducer model, factor graphs, or class factory configuration) is wrapped in a RuntimeException with this message and the original cause attached.

Solutions

Inspect the cause chain (e.getCause()) — the real failure is inside classifyGibbs; fix that underlying error.
If Gibbs inference is not required, set flags.doGibbs=false to use the default maxent/Viterbi path.
Verify flags.sequenceModelClass (and related factory classes) name existing, loadable classes compatible with your CoreNLP version.

Example fix

// before
props.setProperty("doGibbs", "true"); // missing sequenceModelClass
// after
props.setProperty("doGibbs", "true");
props.setProperty("sequenceModelClass", "edu.stanford.nlp.sequences.FactoredSequenceModel");

Defensive patterns

Strategy: try-catch

Validate before calling

if (flags.doGibbs) {
  Class.forName(flags.sequenceModelClass, true, getClass().getClassLoader());
}

Try / catch

try {
  return classifier.classify(document);
} catch (RuntimeException e) {
  if (e.getMessage() != null && e.getMessage().contains("testGibbs")) {
    e.getCause().printStackTrace(); // real failure inside classifyGibbs
    return classifier.classifyMaxEnt(document); // fallback
  }
  throw e;
}

Prevention

Provide a valid sequenceModelClass whenever doGibbs is enabled.
Prefer the default maxent path unless Gibbs sampling is specifically needed.
Keep CoreNLP jar version consistent with the model's serialized flags.

When it happens

Trigger: Calling classify(document) with flags.doGibbs=true when classifyGibbs throws — commonly a ClassCastException/InstantiationException creating the SeqClassifierFlags.sequenceModelClass or documentWriterClass, or a bad factorFactory.

Common situations: Setting doGibbs in properties without specifying a valid sequenceModelClass; class names that don't exist on the classpath; incompatible custom sequence model implementations after a CoreNLP upgrade.

Related errors

AI-assisted analysis of stanfordnlp/CoreNLP@1b7edd19c4 (2026-09-10). Data as JSON: /api/errors/ce68c01ccdc912bf. Report an issue: GitHub.

Appendix: source

Thrown at src/edu/stanford/nlp/ie/crf/CRFClassifier.java:1065

  @Override
  public void dumpFeatures(Collection<List<IN>> docs) {
    if (flags.exportFeatures != null) {
      Timing timer = new Timing();
      CRFFeatureExporter<IN> featureExporter = new CRFFeatureExporter<>(this);
      featureExporter.printFeatures(flags.exportFeatures, docs);
      long elapsedMs = timer.stop();
      log.info("Time to export features: " + Timing.toSecondsString(elapsedMs) + " seconds");
    }
  }

  @Override
  public List<IN> classify(List<IN> document) {
    if (flags.doGibbs) {
      try {
        return classifyGibbs(document);
      } catch (Exception e) {
        throw new RuntimeException("Error running testGibbs inference!", e);
      }
    } else if (flags.crfType.equalsIgnoreCase("maxent")) {
      return classifyMaxEnt(document);
    } else {
      throw new RuntimeException("Unsupported inference type: " + flags.crfType);
    }
  }

  private List<IN> classify(List<IN> document, Triple<int[][][], int[], double[][][]> documentDataAndLabels) {
    if (flags.doGibbs) {
      try {
        return classifyGibbs(document, documentDataAndLabels);
      } catch (Exception e) {
        throw new RuntimeException("Error running testGibbs inference!", e);
      }
    } else if (flags.crfType.equalsIgnoreCase("maxent")) {
      return classifyMaxEnt(document, documentDataAndLabels);
    } else {

View on GitHub (pinned to 1b7edd19c4)