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，并将解压缩后的文件放置在spark 应该可以访问的位置。当我运行以下代码时：
ner = NerDLModel.pretrained('/path/to/unzipped/files')
我看到了Can not find the model to download please check the name!消息，表明它找不到文件，其后是代码中的堆栈跟踪。我也尝试过PretrainedPipeline类，结果相似。
一些值得关注的重要细节：
火花版本：2.4.4
sparknlp版本：2.3.3
Spark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。 _internal._GetResourceSize似乎返回-1，导致加载程序退出。我也收到一些有关http的警告，但是我要做的就是访问本地文件，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'
>>>
答案 0 :(得分：1)
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。预训练的模型可以加载到注释器中，然后再在管道内部使用，但是预训练的管道可以轻松加载并在以后使用。
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]|
+----------------------------------+
*/
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]|
+----------------------------------+
*/
// 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")
如果您对输入数据有任何疑问或问题，请告诉我，我会更新答案。
参考：