Hi Martin, okay, so you will ofcourse need to translate the NER string output to a numerical format as you would do with any text data before feeding it to SPARK ML. Please read SPARK ML documentation on this. I think that they are quite clear on how to do that. But more importantly please try to answer Sean's question, explaining what you are trying to achieve and how, always helps.
Regards, Gourav Sengupta On Thu, Nov 11, 2021 at 11:03 AM Martin Wunderlich <mar...@wunderlich.com> wrote: > Hi Gourav, > > Mostly correct. The output of SparNLP here is a trained > pipeline/model/transformer. I am feeding this trained pipeline to the > MulticlassClassificationEvaluator for evaluation and this > MulticlassClassificationEvaluator only accepts floats or doubles are the > labels (instead of NER labels). > > Cheers, > > Martin > > Am 11.11.21 um 11:39 schrieb Gourav Sengupta: > > Hi Martin, > > just to confirm, you are taking the output of SPARKNLP, and then trying to > feed it to SPARK ML for running algorithms on the output of NERgenerated by > SPARKNLP right? > > > Regards, > Gourav Sengupta > > On Thu, Nov 11, 2021 at 8:00 AM <mar...@wunderlich.com> wrote: > >> Hi Sean, >> >> Apologies for the delayed reply. I've been away on vacation and then busy >> catching up afterwards. >> >> Regarding the evalution using MulticlassClassificationEvaluator: This is >> a about a sequence labeling task to identify specific non-standard named >> entities. The training and evaluation data is in CoNLL format. The training >> works all fine, using the categorical labels for the NEs. In order to use >> the MulticlassClassificationEvaluator, however, I need to convert these to >> floats. This is possible and also works fine, it is just inconvenient >> having to do the extra step. I would have expected the >> MulticlassClassificationEvaluator to be able to use the labels directly. >> >> I will try to create and propose a code change in this regard, if or when >> I find the time. >> >> Cheers, >> >> Martin >> >> >> >> Am 2021-10-25 14:31, schrieb Sean Owen: >> >> I don't think the question is representation as double. The question is >> how this output represents a label? This looks like the result of an >> annotator. What are you classifying? you need, first, ground truth and >> prediction somewhere to use any utility to assess classification metrics. >> >> On Mon, Oct 25, 2021 at 5:42 AM <mar...@wunderlich.com> wrote: >> >> Hello, >> >> I am using SparkNLP to do some NER. The result datastructure after >> training and classification is a Dataset<Row>, with one column each for >> labels and predictions. For evaluating the model, I would like to use the >> Spark ML class >> org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator. However, >> this evaluator expects labels as double numbers. In the case of an NER >> task, the results in my case are of type >> array<struct<annotatorType:string,begin:int,end:int,result:string,metadata:map<string,string>,embeddings:array<float>>>. >> >> >> It would be possible, of course, to convert this format to the required >> doubles. But is there a way to easily apply >> MulticlassClassificationEvaluator to the NER task or is there maybe a >> better evaluator? I haven't found anything yet (neither in Spark ML nor in >> SparkNLP). >> >> Thanks a lot. >> >> Cheers, >> >> Martin >> >>
