Java 类名：com.alibaba.alink.operator.batch.classification.BertTextPairClassifierPredictBatchOp
Python 类名：BertTextPairClassifierPredictBatchOp

功能介绍

与 BERT 文本对分类训练组件对应的预测组件。

参数说明

| 名称 | 中文名称 | 描述 | 类型 | 是否必须？ | 取值范围 | 默认值 | | —- | —- | —- | —- | —- | —- | —- |

| predictionCol | 预测结果列名 | 预测结果列名 | String | ✓ | | |

| inferBatchSize | 推理数据批大小 | 推理数据批大小 | Integer | | | 256 |

| predictionDetailCol | 预测详细信息列名 | 预测详细信息列名 | String | | | |

| reservedCols | 算法保留列名 | 算法保留列 | String[] | | | null |

代码示例

以下代码仅用于示意，可能需要修改部分代码或者配置环境后才能正常运行！

Python 代码

  1. url = "http://alink-algo-packages.oss-cn-hangzhou-zmf.aliyuncs.com/data/MRPC/train.tsv"
  2. schemaStr = "f_quality bigint, f_id_1 string, f_id_2 string, f_string_1 string, f_string_2 string"
  3. data = CsvSourceBatchOp() \
  4.     .setFilePath(url) \
  5.     .setSchemaStr(schemaStr) \
  6.     .setFieldDelimiter("\t") \
  7.     .setIgnoreFirstLine(True) \
  8.     .setQuoteChar(None)
  9. data = data.firstN(300)
  10. model = CsvSourceBatchOp() \
  11.     .setFilePath("http://alink-test.oss-cn-beijing.aliyuncs.com/jiqi-temp/tf_ut_files/bert_text_pair_classifier_model.csv") \
  12.     .setSchemaStr("model_id bigint, model_info string, label_value bigint")
  13. predict = BertTextPairClassifierPredictBatchOp() \
  14.     .setPredictionCol("pred") \
  15.     .setPredictionDetailCol("pred_detail") \
  16.     .setReservedCols(["f_quality"]) \
  17.     .linkFrom(model, data)
  18. predict.print()

Java 代码

  1. import com.alibaba.alink.operator.batch.BatchOperator;
  2. import com.alibaba.alink.operator.batch.classification.BertTextPairClassifierPredictBatchOp;
  3. import com.alibaba.alink.operator.batch.source.CsvSourceBatchOp;
  4. import org.junit.Test;
  6. public class BertTextPairClassifierPredictBatchOpTest {
  8.     @Test
  9.     public void testBertTextPairClassifierPredictBatchOpTest() throws Exception {
  10.         String url = "http://alink-algo-packages.oss-cn-hangzhou-zmf.aliyuncs.com/data/MRPC/train.tsv";
  11.         String schemaStr = "f_quality bigint, f_id_1 string, f_id_2 string, f_string_1 string, f_string_2 string";
  12.         BatchOperator <?> data = new CsvSourceBatchOp()
  13.             .setFilePath(url)
  14.             .setSchemaStr(schemaStr)
  15.             .setFieldDelimiter("\t")
  16.             .setIgnoreFirstLine(true)
  17.             .setQuoteChar(null);
  18.         data = data.firstN(300);
  19.         BatchOperator <?> model = new CsvSourceBatchOp()
  20.             .setFilePath("http://alink-test.oss-cn-beijing.aliyuncs.com/jiqi-temp/tf_ut_files/bert_text_pair_classifier_model.csv")
  21.             .setSchemaStr("model_id bigint, model_info string, label_value bigint");
  22.         BertTextPairClassifierPredictBatchOp predict = new BertTextPairClassifierPredictBatchOp()
  23.             .setPredictionCol("pred")
  24.             .setPredictionDetailCol("pred_detail")
  25.             .setReservedCols("f_quality")
  26.             .linkFrom(model, data);
  27.         predict.print();
  28.     }
  29. }

运行结果

| f_quality | pred | pred_detail | | —- | —- | —- |

| 1 | 1 | {“0”:0.07034707069396973,”1”:0.9296529293060303} |

| 0 | 1 | {“0”:0.07034707069396973,”1”:0.9296529293060303} |

| 1 | 1 | {“0”:0.07034707069396973,”1”:0.9296529293060303} |

| 0 | 1 | {“0”:0.07034707069396973,”1”:0.9296529293060303} |

| 1 | 1 | {“0”:0.07034707069396973,”1”:0.9296529293060303} |

| … | … | … |

| 1 | 1 | {“0”:0.0704156756401062,”1”:0.9295843243598938} |

| 1 | 1 | {“0”:0.0704156756401062,”1”:0.9295843243598938} |

| 1 | 1 | {“0”:0.0704156756401062,”1”:0.9295843243598938} |

| 1 | 1 | {“0”:0.0704156756401062,”1”:0.9295843243598938} |

| 0 | 1 | {“0”:0.0704156756401062,”1”:0.9295843243598938} |