stanfordnlp/CoreNLP · error · RuntimeIOException

Error in LinearClassifierFactory, loading from file=

Error message

Error in LinearClassifierFactory, loading from file=${file}

What it means

Generic wrapper error from LinearClassifierFactory.loadFromFilename: any IOException or parse failure while reading a text-serialized LinearClassifier (missing file, bad number format, unexpected EOF) is rethrown with the file name attached.

Solutions

1. Confirm the file path and format (label index, feature index, weights, thresholds)
2. Ensure the file was written by saveToFilename in the same version
3. Catch and fall back to a default or retrained model

When it happens

Trigger: Thrown at src/edu/stanford/nlp/classify/LinearClassifierFactory.java:987 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/483e7cd4cd7d3d49. Report an issue: GitHub.

Appendix: source

Thrown at src/edu/stanford/nlp/classify/LinearClassifierFactory.java:987

        int label = Integer.parseInt(tuples[1]);
        double value = Double.parseDouble(tuples[2]);
        weights[feature][label] = value;
        line = in.readLine();
      }

      // First line in thresholds is the number of thresholds
      int numThresholds = Integer.parseInt(in.readLine());
      double[] thresholds = new double[numThresholds];
      int curr = 0;
      while ((line = in.readLine()) != null) {
        double tval = Double.parseDouble(line.trim());
        thresholds[curr++] = tval;
      }
      in.close();
      LinearClassifier<String, String> classifier = new LinearClassifier<>(weights, featureIndex, labelIndex);
      return classifier;
    } catch (Exception e) {
      throw new RuntimeIOException("Error in LinearClassifierFactory, loading from file=" + file, e);
    }
  }

  public void setEvaluators(int iters, Evaluator[] evaluators) {
    this.evalIters = iters;
    this.evaluators = evaluators;
  }

  public LinearClassifierCreator<L,F> getClassifierCreator(GeneralDataset<L, F> dataset) {
//    LogConditionalObjectiveFunction<L, F> objective = new LogConditionalObjectiveFunction<L, F>(dataset, logPrior);
    return new LinearClassifierCreator<>(dataset.featureIndex, dataset.labelIndex);
  }

  public static class LinearClassifierCreator<L,F> implements ClassifierCreator, ProbabilisticClassifierCreator
  {
    LogConditionalObjectiveFunction objective;
    Index<F> featureIndex;
    Index<L> labelIndex;

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