title: How can I improve my spaCy model to perfectly recognise coordinates?
body: <p>I trained a model for custom named entity recognition by using wonderful prodigy but It can not recognise coordinate in this:</p>
  
  <p>"But the sun is 7° LONG 51’ 24“ removed from opposition  to Mars "</p>
  
  <p>as you see it gives me:</p>
  
  <p>[7° LONG 51’ 24“] </p>
  
  <p>but it should be like this</p>
  
  <p>[7°  51’ 24“ LONG]
  can anyone help me why it is so?</p>
  
  <p>more detail:</p>
  
  <p>I have done an annotation by regex for labels as following: Date, Time, Coordinate, then I trained my custom named entity recognition using Prodigy </p>
  
  <p><a href="https://prodi.gy/?gclid=Cj0KCQjwyLDpBRCxARIsAEENsrLs2bbv7QT-d7lq88ZHkYaxPjI9L3aR23uRQGgWOBV1spM5WGV_LrIaAv6pEALw_wcB" rel="nofollow noreferrer">https://prodi.gy/?gclid=Cj0KCQjwyLDpBRCxARIsAEENsrLs2bbv7QT-d7lq88ZHkYaxPjI9L3aR23uRQGgWOBV1spM5WGV_LrIaAv6pEALw_wcB</a></p>
  
  <p>I followed this step  for each label first I</p>
  
  <p>1-edit annotation by this command (for instance for longitude)</p>
  
  <pre><code>python -m prodigy ner.manual an_ner_date_time_02 en_core_web_sm AN_NER_DATE_TIME_01.jsonl  --label LONG
  </code></pre>
  
  <p>to edit (by an annotator) my per-annotated data by regex.  Then I merged my dataset. the next stage was to build a model by </p>
  
  <p>2-bulid a model (for all merged data)</p>
  
  <pre><code>python -m prodigy ner.batch-train data_merged_v06 en_core_web_sm --output Model_12 --n-iter 10 --eval-split 0.2 --dropout 0.2 --no-missing
  </code></pre>
  
  <p>question: 
  how can I have better annotation regarding long,</p>
  
  <p>So now I have a model which has this result:</p>
  
  <pre><code>BEFORE     0.008
  Correct    36
  Incorrect  4438
  Entities   2802
  Unknown    0
  
  
  #          LOSS       RIGHT      WRONG      ENTS       SKIP       ACCURACY
  01         110.744    1614       163        1683       0          0.908
  02         98.563     1660       107        1719       0          0.939
  03         98.472     1668       96         1724       0          0.946
  04         96.250     1673       93         1731       0          0.947
  05         96.192     1679       80         1730       0          0.955
  06         96.108     1678       71         1719       0          0.959
  07         94.347     1681       67         1721       0          0.962
  08         96.472     1679       66         1716       0          0.962
  09         98.936     1681       57         1711       0          0.967
  10         96.175     1681       57         1711       0          0.967
  
  Correct    1681
  Incorrect  57
  Baseline   0.008
  Accuracy   0.967
  </code></pre>
  
  <p>It can annotate the data as you see here (click the link)</p>
  
  <p>![annotation text]</p>
  
  <p>(<a href="https://ibb.co/rtQm5B9" rel="nofollow noreferrer">https://ibb.co/rtQm5B9</a>)</p>
  
  <p>![annotation text]</p>
  
  <p>the problem is the model can not learn perfectly coordinate as you see 
  7° LONG 51’ 24“ it should be
  7°  51’ 24“ LONG</p>
  
  <p>is wrongly  annotated (however It annotated by regex correct in the training set but by the model in the test set no!)</p>
  
  <p>do you have any idea how to improve this?</p>
  
  <p>second question, How can I use the result of ner for the classification of sentences?</p>