ML.Net列标签第7行的错误值

Question

我正在尝试 ML.NET 进行基本的情感分析，如链接https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/sentiment-analysis中所述。
我已按步骤进行操作，并使用了链接中提供的相同文件进行培训。还检查了另一个用户在此链接中有类似问题的所有评论和答案：
ml.net sentiment analysis warning about format errors & bad values

但是仍然出现以下错误。大部分训练数据都导致错误，如下所示（已处理860行，其中818个错误值）。理想情况下，这不应发生，因为数据和代码均由Microsoft官方站点提供（上面给出的第一个链接）。代码和错误粘贴在下面。

Microsoft站点数据是否有任何更改，尚未由他们更新？

Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
  Bad value at line 8 in column Label
  Bad value at line 112 in column Label
  Bad value at line 187 in column Label
  Bad value at line 9 in column Label
  Bad value at line 10 in column Label
  Bad value at line 11 in column Label
  Bad value at line 12 in column Label
  Bad value at line 188 in column Label
  Bad value at line 190 in column Label
  Bad value at line 113 in column Label
  Suppressing further bad value messages
Processed 1773 rows with 1731 bad values and 0 format errors
Processed 42 instances
Binning and forming Feature objects
Reserved memory for tree learner: 1188 bytes
Starting to train ...
Warning: 50 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.
Not training a calibrator because it is not needed.
  Bad value at line 7 in column Label
  Bad value at line 186 in column Label
  Bad value at line 111 in column Label
  Bad value at line 8 in column Label
  Bad value at line 9 in column Label
  Bad value at line 10 in column Label
  Bad value at line 11 in column Label
  Bad value at line 12 in column Label
  Suppressing further bad value messages
  Bad value at line 112 in column Label
  Bad value at line 187 in column Label
Processed 860 rows with 818 bad values and 0 format errors

下面是代码：

public class SentimentData
    {
        [Column(ordinal: "0", name: "Label")]
        public float Sentiment;
        [Column(ordinal: "1")]
        public string SentimentText;
    }

    public class SentimentPrediction
    {
        [ColumnName("PredictedLabel")]
        public bool Sentiment;
    }




class Program
    {
        //https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/sentiment-analysis

        static readonly string _dataPath = Path.Combine(Environment.CurrentDirectory, "Data", "wikipedia-detox-250-line-data.tsv");
        static readonly string _testDataPath = Path.Combine(Environment.CurrentDirectory, "Data", "wikipedia-detox-250-line-test.tsv");
        static readonly string _modelpath = Path.Combine(Environment.CurrentDirectory, "Data", "Model.zip");



        static async Task Main(string[] args)
        {
            //Microsoft.ML.Legacy.Transforms.SentimentAnalyzer sentimentAnalyzer = new SentimentAnalyzer();
            //sentimentAnalyzer.Data;

            Console.WriteLine("---------------Training ------------------------------------");
            var model = await Train();
            Evaluate(model);
            Console.WriteLine("---------------Training Over------------------------------------");
            Console.WriteLine("Type END to exit");
            string s = "";
            while (s.ToLower() != "end")
            {
                s = Console.ReadLine();
                Console.WriteLine("Sentiment: {0}",(Predict(model, s).Sentiment ? "Negative" : "Positive"));
            }
        }

        public static async Task<PredictionModel<SentimentData, SentimentPrediction>> Train()
        {
            var pipeline = new LearningPipeline();
            TextLoader textLoader = new TextLoader(_dataPath).CreateFrom<SentimentData>(useHeader: true, allowQuotedStrings: true, supportSparse: true, trimWhitespace: true);
            pipeline.Add(textLoader);
            pipeline.Add(new TextFeaturizer("Features", "SentimentText"));
            //pipeline.Add(new FastTreeBinaryClassifier() { NumLeaves = 50, NumTrees = 50, MinDocumentsInLeafs = 20 });
            pipeline.Add(new LogisticRegressionBinaryClassifier() { });
            PredictionModel<SentimentData, SentimentPrediction> model = pipeline.Train<SentimentData, SentimentPrediction>();
            await model.WriteAsync(_modelpath);
            return model;
        }

        public static void Evaluate(PredictionModel<SentimentData, SentimentPrediction> model)
        {
            var testData = new TextLoader(_testDataPath).CreateFrom<SentimentData>();
            var evaluator = new BinaryClassificationEvaluator();
            BinaryClassificationMetrics metrics = evaluator.Evaluate(model, testData);
            Console.WriteLine();
            Console.WriteLine("PredictionModel quality metrics evaluation");
            Console.WriteLine("------------------------------------------");
            Console.WriteLine($"Accuracy: {metrics.Accuracy:P2}");
            Console.WriteLine($"Auc: {metrics.Auc:P2}");
            Console.WriteLine($"F1Score: {metrics.F1Score:P2}");
        }

        public static SentimentPrediction Predict(PredictionModel<SentimentData, SentimentPrediction> model, string sentence)
        {
            return model.Predict(new SentimentData { SentimentText = sentence });
}