What causes "Split weights cannot be negative" in stanfordnlp/CoreNLP?

Replace any negative value in -splitWeights with a zero or positive weight (use 0 to exclude a split's content rather than a negative number).

stanfordnlp/CoreNLP · error · IllegalArgumentException

Split weights cannot be negative

Error message

Split weights cannot be negative

What it means

SplitTrainingSet validates that each split weight is non-negative; after summing SPLIT_WEIGHTS in main, a weight below 0.0 triggers this IllegalArgumentException. Weights define the proportional size of each named split, so negatives are meaningless.

Solutions

Replace any negative value in -splitWeights with a zero or positive weight (use 0 to exclude a split's content rather than a negative number).
Ensure all weights together total a positive value, since the following check also requires totalWeight > 0.
Validate the properties file values before running the tool.

Example fix

// before
-splitNames train,holdout -splitWeights 0.9,-0.1
// after
-splitNames train,holdout -splitWeights 0.9,0.1

Defensive patterns

Strategy: validation

Validate before calling

double[] ws = Arrays.stream(props.getProperty("splitWeights").split(","))
                    .mapToDouble(Double::parseDouble).toArray();
for (double w : ws)
  if (w < 0.0) throw new IllegalArgumentException("Negative split weight: " + w);
if (Arrays.stream(ws).sum() <= 0.0)
  throw new IllegalArgumentException("Split weights must total positive");

Try / catch

try {
  SplitTrainingSet.main(args);
} catch (IllegalArgumentException e) {
  if (e.getMessage() != null && e.getMessage().contains("cannot be negative")) {
    log.error("Config error: splitWeights must all be >= 0 and sum > 0");
  } else throw e;
}

Prevention

Validate weight strings with a regex like ^\d+(\.\d+)?$ before passing them.
Use 0.0 (not negative values) to effectively exclude a split.
Keep weights in a reviewed config file rather than typing them inline on the command line.

When it happens

Trigger: Passing -splitWeights with a negative number (e.g. -0.1) or a value parsed as negative due to a stray minus sign/typo in the properties file.

Common situations: Hand-edited config files where a dash from surrounding text got into the numbers; experimenting with 'negative weights' to shrink a split instead of removing it; copy-paste mistakes.

Understand the failure class

Background: "value must be between 0 and 1" / "out of range" / "must not be negative" errors: fixing range-validation failures across open-source libraries — this error's family across 42 libraries.

Related errors

AI-assisted analysis of stanfordnlp/CoreNLP@1b7edd19c4 (2026-09-10). Data as JSON: /api/errors/493a36c4a19f51b6. Report an issue: GitHub.

Appendix: source

Thrown at src/edu/stanford/nlp/trees/SplitTrainingSet.java:75

    }
    return weights.size() - 1;
  }

  @SuppressWarnings("unused")
  public static void main(String[] args) throws IOException {
    // Parse the arguments
    Properties props = StringUtils.argsToProperties(args);
    ArgumentParser.fillOptions(new Class[]{ArgumentParser.class, SplitTrainingSet.class}, props);

    if (SPLIT_NAMES.length != SPLIT_WEIGHTS.length) {
      throw new IllegalArgumentException("Name and weight arrays must be of the same length");
    }

    double totalWeight = 0.0;
    for (Double weight : SPLIT_WEIGHTS) {
      totalWeight += weight;
      if (weight < 0.0) {
        throw new IllegalArgumentException("Split weights cannot be negative");
      }
    }

    if (totalWeight <= 0.0) {
      throw new IllegalArgumentException("Split weights must total to a positive weight");
    }

    List<Double> splitWeights = new ArrayList<>();
    for (Double weight : SPLIT_WEIGHTS) {
      splitWeights.add(weight / totalWeight);
    }
    logger.info("Splitting into " + splitWeights.size() + " lists with weights " + splitWeights);


    if (SEED == 0L) {
      SEED = System.nanoTime();
      logger.info("Random seed not set by options, using " + SEED);
    }

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