stanfordnlp/CoreNLP · error · RuntimeException

Serialization failed

Error message

Serialization failed: ${e.getMessage()}

What it means

RuntimeException wrapping any exception from LinearClassifier.writeClassifier when serializing the classifier to file — e.g., invalid path, no write permission, or disk full. The model could not be persisted.

Solutions

1. Check the output directory exists and is writable
2. Verify available disk space
3. Retry with a different path; treat persistence as non-fatal if training can be redone

When it happens

Trigger: Thrown at src/edu/stanford/nlp/classify/LinearClassifier.java:1346 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of stanfordnlp/CoreNLP@1b7edd19c4 (2026-09-10). Data as JSON: /api/errors/d694e86d2944d95b. Report an issue: GitHub.

Appendix: source

Thrown at src/edu/stanford/nlp/classify/LinearClassifier.java:1346

    try {
      ObjectInputStream ois = IOUtils.readStreamFromString(loadPath);
      LinearClassifier<L, F> classifier = ErasureUtils.<LinearClassifier<L, F>>uncheckedCast(ois.readObject());
      ois.close();
      return classifier;
    } catch (Exception e) {
      throw new RuntimeException("Deserialization failed: "+e.getMessage(), e);
    }
  }

  /**
   * Convenience wrapper for IOUtils.writeObjectToFile.
   */
  public static void writeClassifier(LinearClassifier<?, ?> classifier, String serializePath) {
    try {
      IOUtils.writeObjectToFile(classifier, serializePath);
      logger.info("Serializing classifier to " + serializePath + "... done.");
    } catch (Exception e) {
      throw new RuntimeException("Serialization failed: " + e.getMessage(), e);
    }
  }

  /**
   * Saves this out to a standard text file, instead of as a serialized Java object.
   * NOTE: this currently assumes feature and weights are represented as Strings.
   * @param file String filepath to write out to.
   */
  public void saveToFilename(String file) {
    try {
      File tgtFile = new File(file);
      BufferedWriter out = new BufferedWriter(new FileWriter(tgtFile));
      // output index first, blank delimiter, outline feature index, then weights
      labelIndex.saveToWriter(out);
      featureIndex.saveToWriter(out);
      int numLabels = labelIndex.size();
      int numFeatures = featureIndex.size();
      for (int featIndex=0; featIndex<numFeatures; featIndex++) {

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