see anbd draw the model result in python

I have used prodigy to train a model for customized entity, It is pretty good. and the result is so
Loaded model en_core_web_sm
Using 20% of accept/reject examples (1330) for evaluation
Using 100% of remaining examples (5332) for training
Dropout: 0.2 Batch size: 32 Iterations: 50

BEFORE     0.005
Correct    23
Incorrect  4442
Entities   2779
Unknown    0


#          LOSS       RIGHT      WRONG      ENTS       SKIP       ACCURACY
01         30.157     1484       435        1694       0          0.773
02         25.616     1575       306        1747       0          0.837
03         25.160     1608       262        1769       0          0.860
04         24.729     1626       237        1780       0          0.873
05         24.756     1622       220        1755       0          0.881
06         24.248     1632       204        1759       0          0.889
07         24.315     1637       178        1743       0          0.902
08         24.172     1647       162        1747       0          0.910
09         24.171     1645       150        1731       0          0.916
10         24.011     1639       161        1730       0          0.911
11         23.908     1638       156        1723       0          0.913
12         24.130     1648       155        1742       0          0.914
13         23.913     1643       157        1734       0          0.913
14         23.997     1647       163        1748       0          0.910
15         23.901     1646       160        1743       0          0.911
16         23.961     1642       158        1733       0          0.912
17         23.912     1650       154        1745       0          0.915
18         23.871     1650       147        1738       0          0.918
19         23.949     1645       159        1740       0          0.912
20         23.929     1650       153        1744       0          0.915
21         23.891     1643       160        1737       0          0.911
22         23.916     1649       152        1741       0          0.916
23         23.822     1651       157        1750       0          0.913
24         23.806     1654       144        1743       0          0.920
25         23.910     1652       151        1746       0          0.916
26         23.780     1648       151        1738       0          0.916
27         23.841     1653       147        1744       0          0.918
28         23.864     1643       152        1729       0          0.915
29         23.975     1648       154        1741       0          0.915
30         23.691     1646       147        1730       0          0.918
31         23.959     1649       143        1732       0          0.920
32         23.810     1647       143        1728       0          0.920
33         23.745     1648       143        1730       0          0.920
34         24.077     1650       143        1734       0          0.920
35         23.737     1647       150        1735       0          0.917
36         23.815     1648       146        1733       0          0.919
37         23.872     1653       138        1735       0          0.923
38         23.936     1652       146        1741       0          0.919
39         23.890     1650       150        1741       0          0.917
40         23.929     1646       151        1734       0          0.916
41         23.890     1643       149        1726       0          0.917
42         24.018     1645       152        1733       0          0.915
43         23.718     1639       155        1724       0          0.914
44         23.798     1643       150        1727       0          0.916
45         23.834     1644       152        1731       0          0.915
46         23.917     1645       158        1739       0          0.912
47         23.775     1639       159        1728       0          0.912
48         23.805     1634       164        1723       0          0.909
49         23.998     1641       159        1732       0          0.912
50         23.814     1645       155        1736       0          0.914

Correct    1653
Incorrect  138
Baseline   0.005
Accuracy   0.923

Model: C:\Users\moha\Documents\Prodigy\Last_version_data\model_merged_all_02_03
Training data: C:\Users\moha\Documents\Prodigy\Last_version_data\model_merged_all_02_03\training.jsonl
Evaluation data: C:\Users\moha\Documents\Prodigy\Last_version_data\model_merged_all_02_03\evaluation.jsonl

I am wondering how can I have access to deep learning model in order to draw los and accuracy in python?

Many many thanks