stanfordnlp/CoreNLP · error · IllegalArgumentException

Please only specify one of -file, -fileList or -stdin

Error message

Please only specify one of -file, -fileList or -stdin

What it means

main counts how many of the mutually exclusive input sources (-file, -fileList, -stdin) were specified and throws IllegalArgumentException if more than one is set. Exactly one input source must be chosen for the pipeline to know where to read trees from.

Solutions

1. Remove all but one of -file, -fileList, -stdin from the command line.
2. If the goal is to process multiple files, use -fileList with a file containing the list instead of multiple -file flags.
3. In scripts, make the input-source selection exclusive (if/else) rather than appending flags conditionally.
4. Note the companion error: specifying none of them throws 'Please specify either -file, -fileList or -stdin', so always supply exactly one.

Example fix

// before
java edu.stanford.nlp.sentiment.SentimentPipeline -model model.ser.gz -file in.txt -stdin
// after
java edu.stanford.nlp.sentiment.SentimentPipeline -model model.ser.gz -file in.txt

int sources = (file != null ? 1 : 0) + (fileList != null ? 1 : 0) + (stdin ? 1 : 0);
if (sources != 1) {
  throw new IllegalArgumentException("Exactly one of -file, -fileList, -stdin must be set (got " + sources + ")");
}

try {
  SentimentPipeline.main(args);
} catch (IllegalArgumentException e) {
  if (e.getMessage().contains("only specify one of")) {
    System.err.println("Pass exactly one input source: -file <path>, -fileList <list>, or -stdin");
  } else throw e;
}

When it happens

Trigger: Invoking SentimentPipeline with two or more of -file, -fileList, -stdin together, e.g. '-file a.txt -stdin' or '-file a.txt -fileList list.txt'.

Common situations: Scripts that default to -stdin but append -file when a path is provided; template commands that already contain one flag to which users add another; CI wrappers exporting both a file list and piping stdin.

Understand the failure class

Background: "mutually exclusive" flag errors: what "can't supply both nx and xx", "--raw is not compatible with -i" and "cannot be used with" mean, and how to fix them — this error's family across 29 libraries.

Related errors

• Unknown output format
• Unknown format
• Unknown argument
• You probably cannot read the serialized output, so printing…
• Unknown argument

AI-assisted analysis of stanfordnlp/CoreNLP@1b7edd19c4 (2026-09-10). Data as JSON: /api/errors/5205a00f7f4c4725. Report an issue: GitHub.

Appendix: source

Thrown at src/edu/stanford/nlp/sentiment/SentimentPipeline.java:336

    } else {
      pipelineProps.setProperty("annotators", "parse, sentiment");
      pipelineProps.setProperty("parse.binaryTrees", "true");
      pipelineProps.setProperty("parse.buildgraphs", "false");
      pipelineProps.setProperty("enforceRequirements", "false");
      tokenizerProps = new Properties();
      tokenizerProps.setProperty("annotators", "tokenize, ssplit");
    }

    if (stdin && tokenizerProps != null) {
      tokenizerProps.setProperty(StanfordCoreNLP.NEWLINE_SPLITTER_PROPERTY, "true");
    }

    int count = 0;
    if (filename != null) count++;
    if (fileList != null) count++;
    if (stdin) count++;
    if (count > 1) {
      throw new IllegalArgumentException("Please only specify one of -file, -fileList or -stdin");
    }
    if (count == 0) {
      throw new IllegalArgumentException("Please specify either -file, -fileList or -stdin");
    }

    StanfordCoreNLP tokenizer = (tokenizerProps == null) ? null : new StanfordCoreNLP(tokenizerProps);
    StanfordCoreNLP pipeline = new StanfordCoreNLP(pipelineProps);

    if (filename != null) {
      // Process a file.  The pipeline will do tokenization, which
      // means it will split it into sentences as best as possible
      // with the tokenizer.
      List<Annotation> annotations = getAnnotations(tokenizer, inputFormat, filename, filterUnknown);
      for (Annotation annotation : annotations) {
        pipeline.annotate(annotation);

        for (CoreMap sentence : annotation.get(CoreAnnotations.SentencesAnnotation.class)) {
          System.out.println(sentence);

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