Hello,

I created 24908 documents with labels using EntityRuler, PhraseMatcher. Then i used ner.batch-train with below options "--n-iter 10 --eval-split 0.2 --dropout 0.2 --unsegmented --no-missing"

The accuracy is not improving much in the last iterations. Do i need to do more iterations or add more data?. I think dataset with 24000 is a very good to use batch-train. May i know why the accuracy is not improving?.

5:05:54 - MODEL: Using 24908 examples (without 'ignore')

Using 20% of accept/reject examples (4929) for evaluation

15:05:59 - RECIPE: Temporarily disabled other pipes: ['tagger', 'parser']

15:05:59 - RECIPE: Initialised EntityRecognizer with model en_core_web_sm

15:05:59 - MODEL: Merging entity spans of 4929 examples

15:05:59 - MODEL: Using 4929 examples (without 'ignore')

15:08:50 - MODEL: Evaluated 4929 examples

15:08:50 - RECIPE: Calculated baseline from evaluation examples (accuracy 0.00)

Using 100% of remaining examples (19719) for training

Dropout: 0.2 Batch size: 16 Iterations: 10

BEFORE 0.000

Correct 0

Incorrect 48658

Entities 189068

Unknown 0

# LOSS RIGHT WRONG ENTS SKIP ACCURACY

15:42:42 - MODEL: Merging entity spans of 4929 examples

15:42:42 - MODEL: Using 4929 examples (without 'ignore')

15:45:12 - MODEL: Evaluated 4929 examples

01 5095298.645 15456 44599 26853 0 0.257

16:24:57 - MODEL: Using 4929 examples (without 'ignore')

16:27:45 - MODEL: Evaluated 4929 examples

02 5158556.137 29951 29299 40757 0 0.506

17:29:12 - MODEL: Merging entity spans of 4929 examples

17:29:13 - MODEL: Using 4929 examples (without 'ignore')

17:31:11 - MODEL: Evaluated 4929 examples

03 5091354.893 34732 23599 44824 0 0.595

18:06:08 - MODEL: Merging entity spans of 4929 examples

18:06:08 - MODEL: Using 4929 examples (without 'ignore')

18:08:04 - MODEL: Evaluated 4929 examples

04 5040571.233 36622 21549 46621 0 0.630

18:40:29 - MODEL: Merging entity spans of 4929 examples

18:40:29 - MODEL: Using 4929 examples (without 'ignore')

18:42:29 - MODEL: Evaluated 4929 examples

05 4933042.997 37395 20333 46988 0 0.648

22:00:31 - MODEL: Merging entity spans of 4929 examples

22:00:32 - MODEL: Using 4929 examples (without 'ignore')

22:03:09 - MODEL: Evaluated 4929 examples

06 4978476.187 37843 19299 46856 0 0.662

22:40:34 - MODEL: Merging entity spans of 4929 examples

22:40:34 - MODEL: Using 4929 examples (without 'ignore')

22:42:39 - MODEL: Evaluated 4929 examples

07 5028080.368 38169 18570 46800 0 0.673

23:09:27 - MODEL: Merging entity spans of 4929 examples

23:09:28 - MODEL: Using 4929 examples (without 'ignore')

23:11:25 - MODEL: Evaluated 4929 examples

08 4943048.409 38547 18011 47009 0 0.682

06:59:55 - MODEL: Merging entity spans of 4929 examples

06:59:56 - MODEL: Using 4929 examples (without 'ignore')

07:01:51 - MODEL: Evaluated 4929 examples

09 4869467.426 38833 17512 47081 0 0.689

07:34:13 - MODEL: Merging entity spans of 4929 examples

07:34:14 - MODEL: Using 4929 examples (without 'ignore')

07:36:27 - MODEL: Evaluated 4929 examples

10 4931146.311 39036 17146 47122 0 0.695

Correct 39036

Incorrect 17146

Baseline 0.000

Accuracy 0.695

07:36:27 - RECIPE: Restoring disabled pipes: ['tagger', 'parser']