Meaning of NER Training values using Spacy

Question

I am trying to train custom entities using Spacy. During the training process I am getting number of values of LOSS, score etc. What is the meaning of these values

============================= Training pipeline =============================
ℹ Pipeline: ['tok2vec', 'ner']
ℹ Initial learn rate: 0.001
E    #       LOSS TOK2VEC  LOSS NER  ENTS_F  ENTS_P  ENTS_R  SCORE 
---  ------  ------------  --------  ------  ------  ------  ------
  0       0          0.00    278.46    0.00    0.00    0.00    0.00
 20     200       3647.33  10920.67   91.75   93.68   89.90    0.92
 40     400         92.82    679.78   98.21   99.48   96.97    0.98
 60     600         66.59    274.91   98.98  100.00   97.98    0.99
 80     800         87.59    252.62   98.98   99.49   98.48    0.99

score 6 · Accepted Answer · answered Oct 12 '21 at 07:58

The values for LOSS TOK2VEC and LOSS NER are the loss values for the token-to-vector and named entity recognition steps in your pipeline. The ENTS_F, ENTS_P, and ENTS_R column indicate the values for the F-score, precision, and recall for the named entities task (see also the items under the 'Accuracy Evaluation' block on this link. The score column shows the overall score of the pipeline, which may or may not be a weighted more to specific subtasks.

Meaning of NER Training values using Spacy

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