Java 类名：com.alibaba.alink.operator.batch.regression.BertTextRegressorPredictBatchOp
Python 类名：BertTextRegressorPredictBatchOp

功能介绍

与 BERT 文本回归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 |

| 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-test.oss-cn-beijing.aliyuncs.com/jiqi-temp/tf_ut_files/ChnSentiCorp_htl_small.csv"
  2. schema = "label double, review string"
  3. data = CsvSourceBatchOp() \
  4.     .setFilePath(url) \
  5.     .setSchemaStr(schema) \
  6.     .setIgnoreFirstLine(True)
  7. data = data.where("review is not null")
  8. data = data.firstN(300)
  9. model = CsvSourceBatchOp() \
  10.     .setFilePath("http://alink-test.oss-cn-beijing.aliyuncs.com/jiqi-temp/tf_ut_files/bert_text_regressor_model.csv") \
  11.     .setSchemaStr("model_id bigint, model_info string, label_value double")
  12. predict = BertTextRegressorPredictBatchOp() \
  13.     .setPredictionCol("pred") \
  14.     .setReservedCols(["label"]) \
  15.     .linkFrom(model, data)
  16. predict.print()

Java 代码

  1. import com.alibaba.alink.operator.batch.BatchOperator;
  2. import com.alibaba.alink.operator.batch.regression.BertTextRegressorPredictBatchOp;
  3. import com.alibaba.alink.operator.batch.source.CsvSourceBatchOp;
  4. import org.junit.Test;
  6. public class BertTextRegressorPredictBatchOpTest {
  8.     @Test
  9.     public void testBertTextRegressorPredictBatchOp() throws Exception {
  10.         String url = "http://alink-test.oss-cn-beijing.aliyuncs.com/jiqi-temp/tf_ut_files/ChnSentiCorp_htl_small.csv";
  11.         String schema = "label double, review string";
  12.         BatchOperator <?> data = new CsvSourceBatchOp()
  13.             .setFilePath(url)
  14.             .setSchemaStr(schema)
  15.             .setIgnoreFirstLine(true);
  16.         data = data.where("review is not null");
  17.         data = data.firstN(300);
  18.         BatchOperator <?> model = new CsvSourceBatchOp()
  19.             .setFilePath("http://alink-test.oss-cn-beijing.aliyuncs.com/jiqi-temp/tf_ut_files/bert_text_regressor_model.csv")
  20.             .setSchemaStr("model_id bigint, model_info string, label_value double");
  21.         BertTextRegressorPredictBatchOp predict = new BertTextRegressorPredictBatchOp()
  22.             .setPredictionCol("pred")
  23.             .setReservedCols("label")
  24.             .linkFrom(model, data);
  25.         predict.print();
  26.     }
  27. }

运行结果

| label | pred | | —- | —- |

| 1.0 | 5.004022 |

| 1.0 | 5.004022 |

| 1.0 | 5.004022 |

| 1.0 | 5.004022 |

| 1.0 | 5.004022 |

| … | … |

| 0.0 | 5.004022 |

| 0.0 | 5.004022 |

| 0.0 | 5.004022 |

| 0.0 | 5.004022 |

| 0.0 | 5.004022 |