Hey,
I’m having a hard time interpreting the results from ner.batch-train
.
→ prodigy ner.batch-train long_text_annotations_gold en_core_web_sm --output ./ner/models/model-3-person --eval-split 0.2 --label PERSON --n-iter 10
Loaded model en_core_web_sm
Using 20% of examples (719) for evaluation
Using 100% of remaining examples (2881) for training
Dropout: 0.2 Batch size: 32 Iterations: 10
BEFORE 0.457
Correct 86
Incorrect 102
Entities 1839
Unknown 371
# LOSS RIGHT WRONG ENTS SKIP ACCURACY
01 4.557 166 22 1554 0 0.883
02 2.779 162 26 1581 0 0.862
03 1.982 160 28 1501 0 0.851
04 1.589 159 29 1524 0 0.846
05 1.314 161 27 1523 0 0.856
06 1.639 157 31 1525 0 0.835
07 0.879 159 29 1505 0 0.846
08 0.763 160 28 1521 0 0.851
09 0.665 157 31 1486 0 0.835
10 0.568 159 29 1499 0 0.846
Correct 166
Incorrect 22
Baseline 0.457
Accuracy 0.883
The standard model report Correct 86
and Incorrect 102
. However, what do these numbers resemble? entities? I think the stats are confusing, given that I have 719 evaluation examples.
In general, how to go about interpreting these stats?
It’s been I while since I touched Prodigy, so pardon me for this rookie question. Thank you!