Spark-nlp:无法从pyspark中的磁盘加载预训练的识别实体模型

问题描述 投票:1回答:1

我设置了一个Spark集群,并希望集成spark-nlp来运行命名实体识别。我需要从磁盘访问模型,而不是在运行时从Internet下载模型。我已经从模型下载页面下载了recognize_entities_dl模型,并将解压缩的文件放置在spark should可以访问它的位置。当我运行以下代码时:

ner = NerDLModel.pretrained('/path/to/unzipped/files')

我看到Can not find the model to download please check the name!消息,指示它找不到文件,其后跟代码中的堆栈跟踪。我也尝试过PretrainedPipeline类,结果相似。

有关其价值的一些重要细节:

火花版本:2.4.4

sparknlp版本:2.3.3

Spark正在kubernetes容器内的docker容器中运行。我可以执行此容器并手动运行命令来重现该问题。看起来_internal._GetResourceSize返回-1,导致加载程序退出。我也收到一些有关http的警告,但我要尝试做的只是访问本地文件,因此不确定该怎么做:

>>> _internal._GetResourceSize('/path/in/container/recognize_entities_dl_en_2.1.0_2.4_1562946909722', 'en', remote_loc=None).apply()
19/12/02 20:29:03 WARN ApacheUtils: NoSuchMethodError was thrown when disabling normalizeUri. This indicates you are using an old version (< 4.5.8) of Apache http client. It is recommended to use http client version >= 4.5.9 to avoid the breaking change introduced in apache client 4.5.7 and the latency in exception handling. See https://github.com/aws/aws-sdk-java/issues/1919 for more information
19/12/02 20:29:03 WARN ApacheUtils: NoSuchMethodError was thrown when disabling normalizeUri. This indicates you are using an old version (< 4.5.8) of Apache http client. It is recommended to use http client version >= 4.5.9 to avoid the breaking change introduced in apache client 4.5.7 and the latency in exception handling. See https://github.com/aws/aws-sdk-java/issues/1919 for more information
'-1'
>>>
apache-spark kubernetes pyspark johnsnowlabs-spark-nlp
1个回答
0
投票

您正在尝试将预训练的管道加载到注释器中。有两种类型的预训练资源:模型和管道。可以将预训练的模型加载到注释器中,然后再在管道内部使用,但是,可以轻松地加载预训练的管道,然后再使用。

  • 预训练管道的示例(在线需要互联网):
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val testData = spark.createDataFrame(Seq(
(1, "Google has announced the release of a beta version of the popular TensorFlow machine learning library"),
(2, "Donald John Trump (born June 14, 1946) is the 45th and current president of the United States")
)).toDF("id", "text")

// Pay attention, for loading a pre-trained pipeline we use PretrainedPipeline
val pipeline = PretrainedPipeline("recognize_entities_dl", lang="en")

val annotation = pipeline.transform(testData)

annotation.show()

/*
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP
2.4.0
testData: org.apache.spark.sql.DataFrame = [id: int, text: string]
pipeline: com.johnsnowlabs.nlp.pretrained.PretrainedPipeline = PretrainedPipeline(entity_recognizer_dl,en,public/models)
annotation: org.apache.spark.sql.DataFrame = [id: int, text: string ... 6 more fields]
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|            sentence|               token|          embeddings|                 ner|       entities|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|  1|Google has announ...|[[document, 0, 10...|[[document, 0, 10...|[[token, 0, 5, Go...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 5, Go...|
|  2|Donald John Trump...|[[document, 0, 92...|[[document, 0, 92...|[[token, 0, 5, Do...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 16, D...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

annotation.select("entities.result").show(false)

/*
+----------------------------------+
|result                            |
+----------------------------------+
|[Google, TensorFlow]              |
|[Donald John Trump, United States]|
+----------------------------------+
*/

  • 预训练管道的示例(离线加载已保存的管道):
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val testData = spark.createDataFrame(Seq(
(1, "Google has announced the release of a beta version of the popular TensorFlow machine learning library"),
(2, "Donald John Trump (born June 14, 1946) is the 45th and current president of the United States")
)).toDF("id", "text")

// Here we are loading a pre-trained pipeline we already downloaded manually for offline use

val pipeline = PretrainedPipeline.load("/path/in/container/recognize_entities_dl_en_2.1.0_2.4_1562946909722")

val annotation = pipeline.transform(testData)

annotation.show()

/*
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
import com.johnsnowlabs.nlp.SparkNLP
2.4.0
testData: org.apache.spark.sql.DataFrame = [id: int, text: string]
pipeline: com.johnsnowlabs.nlp.pretrained.PretrainedPipeline = PretrainedPipeline(entity_recognizer_dl,en,public/models)
annotation: org.apache.spark.sql.DataFrame = [id: int, text: string ... 6 more fields]
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
| id|                text|            document|            sentence|               token|          embeddings|                 ner|       ner_converter|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|  1|Google has announ...|[[document, 0, 10...|[[document, 0, 10...|[[token, 0, 5, Go...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 5, Go...|
|  2|Donald John Trump...|[[document, 0, 92...|[[document, 0, 92...|[[token, 0, 5, Do...|[[word_embeddings...|[[named_entity, 0...|[[chunk, 0, 16, D...|
+---+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
*/

annotation.select("entities.result").show(false)

/*
+----------------------------------+
|result                            |
+----------------------------------+
|[Google, TensorFlow]              |
|[Donald John Trump, United States]|
+----------------------------------+
*/
  • 为NerDLModel加载预训练模型的示例
// Online
val ner = NerDLModel.pretrained(name="ner_dl", lang="en")
// Offline - manualy downloaded
val ner = NerDLModel.load("/path/ner_dl_en_2.4.0_2.4_1580251789753")

让我知道您是否对输入数据有任何疑问或问题,我将更新答案。

参考

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