Bert文本对分类预测 (BertTextPairClassifierPredictBatchOp)

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

url = "http://alink-algo-packages.oss-cn-hangzhou-zmf.aliyuncs.com/data/MRPC/train.tsv"
schemaStr = "f_quality bigint, f_id_1 string, f_id_2 string, f_string_1 string, f_string_2 string"
data = CsvSourceBatchOp() \
    .setFilePath(url) \
    .setSchemaStr(schemaStr) \
    .setFieldDelimiter("\t") \
    .setIgnoreFirstLine(True) \
    .setQuoteChar(None)
data = data.firstN(300)
model = CsvSourceBatchOp() \
    .setFilePath("http://alink-test.oss-cn-beijing.aliyuncs.com/jiqi-temp/tf_ut_files/bert_text_pair_classifier_model.csv") \
    .setSchemaStr("model_id bigint, model_info string, label_value bigint")
predict = BertTextPairClassifierPredictBatchOp() \
    .setPredictionCol("pred") \
    .setPredictionDetailCol("pred_detail") \
    .setReservedCols(["f_quality"]) \
    .linkFrom(model, data)
predict.print()

Java 代码

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.classification.BertTextPairClassifierPredictBatchOp;
import com.alibaba.alink.operator.batch.source.CsvSourceBatchOp;
import org.junit.Test;

public class BertTextPairClassifierPredictBatchOpTest {

	@Test
	public void testBertTextPairClassifierPredictBatchOpTest() throws Exception {
		String url = "http://alink-algo-packages.oss-cn-hangzhou-zmf.aliyuncs.com/data/MRPC/train.tsv";
		String schemaStr = "f_quality bigint, f_id_1 string, f_id_2 string, f_string_1 string, f_string_2 string";
		BatchOperator <?> data = new CsvSourceBatchOp()
			.setFilePath(url)
			.setSchemaStr(schemaStr)
			.setFieldDelimiter("\t")
			.setIgnoreFirstLine(true)
			.setQuoteChar(null);
		data = data.firstN(300);
		BatchOperator <?> model = new CsvSourceBatchOp()
			.setFilePath("http://alink-test.oss-cn-beijing.aliyuncs.com/jiqi-temp/tf_ut_files/bert_text_pair_classifier_model.csv")
			.setSchemaStr("model_id bigint, model_info string, label_value bigint");
		BertTextPairClassifierPredictBatchOp predict = new BertTextPairClassifierPredictBatchOp()
			.setPredictionCol("pred")
			.setPredictionDetailCol("pred_detail")
			.setReservedCols("f_quality")
			.linkFrom(model, data);
		predict.print();
	}
}

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}