verbesserungen e-invoice

models/README.md

@@ -1,53 +0,0 @@
-# ML Models
-
-Wir verwenden bei Alexander-Global Logistics ein standard Invoice ML Modell. Dieses basiert im Grund auf unserem offizielen spacy modell 'invoice-de-0.1.0'. 
-
-
-
-## Modell Nachtrainieren
-
-Das Modell bei Alexander Logistics läst sich in der Dev Umggebung bei Bedarf nachtrainieren. Dazu geht man wie folgt vor:
-
-**1.)** Aktuelles Modell auf Tikal Cloud Server sichern:
-
-    $ ssh imixs@master-1.tikal.imixs.com
-    $ cd tikal-cloud/
-    $ ./apps/alexander-logistics.office-workflow.de/ml_model_backup.sh
-    
-    # Falls es zu einem io/error kommt, muss der spacy ml pod neu gestartet werden!
-   
-
-**2.)** Die Tikal-Cloud Pullen
-
-**3.)** Um auf dem aktuellen Modell weiter zu trainierne, den Inhalt des invoice-de-0.1.0 Folders aus dem Backup directory in die lokale Dev Umgebung kopieren / 
-   um ein neues Modell zu genereiren einfach den localen ordner 'invoice-de-0.1.0' umbenennen oder leeren.
-
-**4.)** Jetzt lokal den Trainingsserver aufrufen  
-
-	http://localhost:8081/api/openapi-ui/index.html
-
- und das training beginnen mit der datei 'training-config-prod.xml'. Man kann das training 3-4 mal durchführen.
- 
- 
-**5.)** Nun kann man die Inhalte des  invoice-de-0.1.0 Folders zurück auf den Tikal in den modell folder /invoice-de-0.1.0 einspielen 
-
-**6.)** Nun die änderungen nach Git Puschen.
-
-**7.)** auf dem Tikal Sever nun das aktuellisete Modell wieder einspielen
-
-	$ git pull
-	$ ./apps/alexander-logistics.office-workflow.de/ml_model_deploy.sh invoice-de-0.1.0/
-	
-
-
-
-## 22.05.2021 - invoice-de-0.1.0
-
-NER=2.1948
-
-
-## 17.02.2020 - invoice-de-0.1.0
-
-Modell trainiert anhand der alexander-logistics Produtiv daten
-
-

models/TEST_PROTOKOLL.md

@@ -1,491 +0,0 @@
-# Test Protokoll Alexander Logistic invoice-de-0.1.0
-
-
-**Validierung**
-f4eaceda-f28a-4394-928e-1a05daaf2760
-a5d1e7fe-d74a-4fa2-9b06-3221865759fa
-c0290497-f613-4492-81b6-4ae1e1680029
-d8e3fce4-d11c-46a4-8ba0-369b4c6e902f
-8b766f35-828a-41f1-be04-00b4b62da4d0
-
-
-
-**********************************************************************
-** invoice-de-0.1.0-falsemodell
-**12.10.2021 15:50** (sort order created)
-**********************************************************************
-workflow.query				($workflowgroup:"Rechnungseingang" OR $workflowgroup:"Sachrechnung") AND ($taskid:5900)
-workflow.pagesize			500
-ml.training.filepattern		.pdf
-ml.training.iterations	 	10
-ml.training.dropoutrate		0,25
-multiOccurrence 			false
-ml.training.quality			LOW		
-min_losses					0
-
-RESULT
- -----------------------------------------------------------------------
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 53.33%  (200)
-imixs-ml-training_1  |   ......      quality level LOW = 31.47%  (118)
-imixs-ml-training_1  |   ......      quality level BAD = 15.2%  (57)
-imixs-ml-training_1  |   ......            average NER = 17.195895012525614
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 51.73%  (194)
-imixs-ml-training_1  |   ......      quality level LOW = 32.8%  (123)
-imixs-ml-training_1  |   ......      quality level BAD = 15.47%  (58)
-imixs-ml-training_1  |   ......            average NER = 6.50170386961344
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 53.87%  (202)
-imixs-ml-training_1  |   ......      quality level LOW = 31.73%  (119)
-imixs-ml-training_1  |   ......      quality level BAD = 14.4%  (54)
-imixs-ml-training_1  |   ......            average NER = 4.8940276912225
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 52.8%  (198)
-imixs-ml-training_1  |   ......      quality level LOW = 32%  (120)
-imixs-ml-training_1  |   ......      quality level BAD = 15.2%  (57)
-imixs-ml-training_1  |   ......            average NER = 3.9605563888416824
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 54.13%  (203)
-imixs-ml-training_1  |   ......      quality level LOW = 29.6%  (111)
-imixs-ml-training_1  |   ......      quality level BAD = 16.27%  (61)
-imixs-ml-training_1  |   ......            average NER = 3.744507591826106
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 53.07%  (199)
-imixs-ml-training_1  |   ......      quality level LOW = 31.2%  (117)
-imixs-ml-training_1  |   ......      quality level BAD = 15.73%  (59)
-imixs-ml-training_1  |   ......            average NER = 2.8395576715820736
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 53.87%  (202)
-imixs-ml-training_1  |   ......      quality level LOW = 32%  (120)
-imixs-ml-training_1  |   ......      quality level BAD = 14.13%  (53)
-imixs-ml-training_1  |   ......            average NER = 2.6799682681142363
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 53.6%  (201)
-imixs-ml-training_1  |   ......      quality level LOW = 31.47%  (118)
-imixs-ml-training_1  |   ......      quality level BAD = 14.93%  (56)
-imixs-ml-training_1  |   ......            average NER = 2.520244211218683
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 49.33%  (185)
-imixs-ml-training_1  |   ......      quality level LOW = 34.93%  (131)
-imixs-ml-training_1  |   ......      quality level BAD = 15.73%  (59)
-imixs-ml-training_1  |   ......            average NER = 2.1511980661715193
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 53.87%  (202)
-imixs-ml-training_1  |   ......      quality level LOW = 31.2%  (117)
-imixs-ml-training_1  |   ......      quality level BAD = 14.93%  (56)
-imixs-ml-training_1  |   ......            average NER = 2.2739690429824475
-
-
-
-**13.10.2021 12:00**
-workflow.query				($workflowgroup:"Rechnungseingang" OR $workflowgroup:"Sachrechnung") AND ($taskid:5900)
-workflow.pagesize			500
-ml.training.filepattern		.pdf|.PDF
-ml.training.iterations	 	10
-ml.training.dropoutrate		0,25
-multiOccurrence 			false
-ml.training.quality			LOW		
-min_losses					0
-
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 61.33%  (230)
-imixs-ml-training_1  |   ......      quality level LOW = 38.67%  (145)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 3.790466332166552
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 61.07%  (229)
-imixs-ml-training_1  |   ......      quality level LOW = 38.93%  (146)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 3.160620521438082
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 58.13%  (218)
-imixs-ml-training_1  |   ......      quality level LOW = 41.87%  (157)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 2.636420357788331
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 58.13%  (218)
-imixs-ml-training_1  |   ......      quality level LOW = 41.87%  (157)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 2.506552163520219
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 58.93%  (221)
-imixs-ml-training_1  |   ......      quality level LOW = 41.07%  (154)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 2.3495061575286793
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 59.47%  (223)
-imixs-ml-training_1  |   ......      quality level LOW = 40.53%  (152)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 1.9351332692220107
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 61.33%  (230)
-imixs-ml-training_1  |   ......      quality level LOW = 38.67%  (145)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 1.8881396005118698
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 57.87%  (217)
-imixs-ml-training_1  |   ......      quality level LOW = 42.13%  (158)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 1.9754112840739593
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 59.73%  (224)
-imixs-ml-training_1  |   ......      quality level LOW = 40.27%  (151)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 1.7839739270522237
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 59.73%  (224)
-imixs-ml-training_1  |   ......      quality level LOW = 40.27%  (151)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 1.6024293913431429
-
-
-
-
-**********************************************************************
-** invoice-de-0.1.0
-**12.10.2021 15:50** (sort order created)
-**********************************************************************
-workflow.query				($workflowgroup:"Rechnungseingang" OR $workflowgroup:"Sachrechnung") AND ($taskid:5900)
-workflow.pagesize			500
-ml.training.filepattern		.pdf|.PDF
-ml.training.iterations	 	10
-ml.training.dropoutrate		0,25
-multiOccurrence 			true
-ml.training.quality			LOW		
-min_losses					0
-
-
-
-imixs-ml-training_1  |   
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 68.27%  (256)
-imixs-ml-training_1  |   ......      quality level LOW = 31.47%  (118)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (1)
-imixs-ml-training_1  |   ......            average NER = 21.57255353509432
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 67.73%  (254)
-imixs-ml-training_1  |   ......      quality level LOW = 32.27%  (121)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 8.249835149395697
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 68.53%  (257)
-imixs-ml-training_1  |   ......      quality level LOW = 31.2%  (117)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (1)
-imixs-ml-training_1  |   ......            average NER = 5.892598287488733
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 68.53%  (257)
-imixs-ml-training_1  |   ......      quality level LOW = 31.2%  (117)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (1)
-imixs-ml-training_1  |   ......            average NER = 4.964129124089013
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 68.53%  (257)
-imixs-ml-training_1  |   ......      quality level LOW = 31.2%  (117)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (1)
-imixs-ml-training_1  |   ......            average NER = 3.5603186705177947
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 67.2%  (252)
-imixs-ml-training_1  |   ......      quality level LOW = 32.53%  (122)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (1)
-imixs-ml-training_1  |   ......            average NER = 2.692053828307031
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 68%  (255)
-imixs-ml-training_1  |   ......      quality level LOW = 31.73%  (119)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (1)
-imixs-ml-training_1  |   ......            average NER = 2.3509401762802873
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 65.87%  (247)
-imixs-ml-training_1  |   ......      quality level LOW = 34.13%  (128)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 2.2695994889458104
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 68.27%  (256)
-imixs-ml-training_1  |   ......      quality level LOW = 31.47%  (118)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (1)
-imixs-ml-training_1  |   ......            average NER = 1.844410364015416
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 70.67%  (265)
-imixs-ml-training_1  |   ......      quality level LOW = 29.07%  (109)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (1)
-imixs-ml-training_1  |   ......            average NER = 1.6153951610034658
-
-
-page=1 (count=500)
---------------------------------
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 69.6%  (261)
-imixs-ml-training_1  |   ......      quality level LOW = 30.4%  (114)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 7.093659738880595
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 66.4%  (249)
-imixs-ml-training_1  |   ......      quality level LOW = 33.6%  (126)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 5.201780406598962
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 68.27%  (256)
-imixs-ml-training_1  |   ......      quality level LOW = 31.73%  (119)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 3.8116329003426204
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 66.67%  (250)
-imixs-ml-training_1  |   ......      quality level LOW = 33.33%  (125)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 3.3164488404836154
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 66.93%  (251)
-imixs-ml-training_1  |   ......      quality level LOW = 33.07%  (124)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 2.7822570929398576
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 69.07%  (259)
-imixs-ml-training_1  |   ......      quality level LOW = 30.93%  (116)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 2.31773083524846
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 68.8%  (258)
-imixs-ml-training_1  |   ......      quality level LOW = 31.2%  (117)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 2.165804551920185
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 66.4%  (249)
-imixs-ml-training_1  |   ......      quality level LOW = 33.6%  (126)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 1.9591955652007715
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 66.4%  (249)
-imixs-ml-training_1  |   ......      quality level LOW = 33.6%  (126)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 1.5473874007025803
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 375
-imixs-ml-training_1  |   ......     quality level GOOD = 65.87%  (247)
-imixs-ml-training_1  |   ......      quality level LOW = 34.13%  (128)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 1.705957084193204
-imixs-ml-training_1  | |#]
-imixs-ml-training_1  | 
-
-page=0 (count=1000)
---------------------------------
-imixs-ml-training_1  |   
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 67.47%  (506)
-imixs-ml-training_1  |   ......      quality level LOW = 32.53%  (244)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 3.2147245242951565
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 68.8%  (516)
-imixs-ml-training_1  |   ......      quality level LOW = 31.07%  (233)
-imixs-ml-training_1  |   ......      quality level BAD = 0.13%  (1)
-imixs-ml-training_1  |   ......            average NER = 2.2084385109690565
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 67.73%  (508)
-imixs-ml-training_1  |   ......      quality level LOW = 32.27%  (242)
-imixs-ml-training_1  |   ......      quality level BAD = 0%  (0)
-imixs-ml-training_1  |   ......            average NER = 2.1797281610496464
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 67.2%  (504)
-imixs-ml-training_1  |   ......      quality level LOW = 32.67%  (245)
-imixs-ml-training_1  |   ......      quality level BAD = 0.13%  (1)
-imixs-ml-training_1  |   ......            average NER = 1.8908819163368502
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 66%  (495)
-imixs-ml-training_1  |   ......      quality level LOW = 33.87%  (254)
-imixs-ml-training_1  |   ......      quality level BAD = 0.13%  (1)
-imixs-ml-training_1  |   ......            average NER = 1.7774978150912477
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 67.6%  (507)
-imixs-ml-training_1  |   ......      quality level LOW = 32.27%  (242)
-imixs-ml-training_1  |   ......      quality level BAD = 0.13%  (1)
-imixs-ml-training_1  |   ......            average NER = 1.669098427922433
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 67.33%  (505)
-imixs-ml-training_1  |   ......      quality level LOW = 32.53%  (244)
-imixs-ml-training_1  |   ......      quality level BAD = 0.13%  (1)
-imixs-ml-training_1  |   ......            average NER = 1.6048149396167861
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 67.6%  (507)
-imixs-ml-training_1  |   ......      quality level LOW = 32.27%  (242)
-imixs-ml-training_1  |   ......      quality level BAD = 0.13%  (1)
-imixs-ml-training_1  |   ......            average NER = 1.4026393956412981
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 67.87%  (509)
-imixs-ml-training_1  |   ......      quality level LOW = 32%  (240)
-imixs-ml-training_1  |   ......      quality level BAD = 0.13%  (1)
-imixs-ml-training_1  |   ......            average NER = 1.1139796046407426
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 65.87%  (494)
-imixs-ml-training_1  |   ......      quality level LOW = 34%  (255)
-imixs-ml-training_1  |   ......      quality level BAD = 0.13%  (1)
-imixs-ml-training_1  |   ......            average NER = 1.2621875029460226
-
-
-
-page=1 (count=1000)
---------------------------------
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 63.47%  (476)
-imixs-ml-training_1  |   ......      quality level LOW = 36.4%  (273)
-imixs-ml-training_1  |   ......      quality level BAD = 0.13%  (1)
-imixs-ml-training_1  |   ......            average NER = 3.7501272727903876
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 64%  (480)
-imixs-ml-training_1  |   ......      quality level LOW = 35.87%  (269)
-imixs-ml-training_1  |   ......      quality level BAD = 0.13%  (1)
-imixs-ml-training_1  |   ......            average NER = 2.7749715296165482
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 65.47%  (491)
-imixs-ml-training_1  |   ......      quality level LOW = 34.27%  (257)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (2)
-imixs-ml-training_1  |   ......            average NER = 2.191986221088383
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 63.2%  (474)
-imixs-ml-training_1  |   ......      quality level LOW = 36.53%  (274)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (2)
-imixs-ml-training_1  |   ......            average NER = 1.836000005105162
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 64.8%  (486)
-imixs-ml-training_1  |   ......      quality level LOW = 34.93%  (262)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (2)
-imixs-ml-training_1  |   ......            average NER = 1.629252182245437
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 64.27%  (482)
-imixs-ml-training_1  |   ......      quality level LOW = 35.47%  (266)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (2)
-imixs-ml-training_1  |   ......            average NER = 1.5023185259959349
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 63.6%  (477)
-imixs-ml-training_1  |   ......      quality level LOW = 36.13%  (271)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (2)
-imixs-ml-training_1  |   ......            average NER = 1.1618358977500587
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 64.93%  (487)
-imixs-ml-training_1  |   ......      quality level LOW = 34.93%  (262)
-imixs-ml-training_1  |   ......      quality level BAD = 0.13%  (1)
-imixs-ml-training_1  |   ......            average NER = 1.1362052688492958
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 64.4%  (483)
-imixs-ml-training_1  |   ......      quality level LOW = 35.33%  (265)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (2)
-imixs-ml-training_1  |   ......            average NER = 1.0497074333838767
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 65.33%  (490)
-imixs-ml-training_1  |   ......      quality level LOW = 34.4%  (258)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (2)
-imixs-ml-training_1  |   ......            average NER = 0.8865013575787167
-
-page=1 (count=1000) 
---------------------------------
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 63.33%  (475)
-imixs-ml-training_1  |   ......      quality level LOW = 36.4%  (273)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (2)
-imixs-ml-training_1  |   ......            average NER = 1.6325150460009787
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 61.73%  (463)
-imixs-ml-training_1  |   ......      quality level LOW = 38%  (285)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (2)
-imixs-ml-training_1  |   ......            average NER = 1.5385085364524884
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 63.6%  (477)
-imixs-ml-training_1  |   ......      quality level LOW = 36.13%  (271)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (2)
-imixs-ml-training_1  |   ......            average NER = 1.343965340697668
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 64.13%  (481)
-imixs-ml-training_1  |   ......      quality level LOW = 35.73%  (268)
-imixs-ml-training_1  |   ......      quality level BAD = 0.13%  (1)
-imixs-ml-training_1  |   ......            average NER = 1.1842750119930687
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 63.73%  (478)
-imixs-ml-training_1  |   ......      quality level LOW = 36%  (270)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (2)
-imixs-ml-training_1  |   ......            average NER = 1.112559219903824
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 62.8%  (471)
-imixs-ml-training_1  |   ......      quality level LOW = 36.93%  (277)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (2)
-imixs-ml-training_1  |   ......            average NER = 0.9444137586901881
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 64%  (480)
-imixs-ml-training_1  |   ......      quality level LOW = 35.73%  (268)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (2)
-imixs-ml-training_1  |   ......            average NER = 0.9175624525798468
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 63.47%  (476)
-imixs-ml-training_1  |   ......      quality level LOW = 36.27%  (272)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (2)
-imixs-ml-training_1  |   ......            average NER = 0.8877949306723629
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 62.53%  (469)
-imixs-ml-training_1  |   ......      quality level LOW = 37.2%  (279)
-imixs-ml-training_1  |   ......      quality level BAD = 0.27%  (2)
-imixs-ml-training_1  |   ......            average NER = 0.7669201996298972
-imixs-ml-training_1  | 
-imixs-ml-training_1  | ......documents trained in total = 750
-imixs-ml-training_1  |   ......     quality level GOOD = 62.8%  (471)
-imixs-ml-training_1  |   ......      quality level LOW = 37.07%  (278)
-imixs-ml-training_1  |   ......      quality level BAD = 0.13%  (1)
-imixs-ml-training_1  |   ......            average NER = 0.7161310708827515

models/invoice-de-0.1.0/config.cfg

@@ -1,128 +0,0 @@
-[paths]
-train = null
-dev = null
-vectors = null
-init_tok2vec = null
-
-[system]
-seed = 0
-gpu_allocator = null
-
-[nlp]
-lang = "de"
-pipeline = ["ner"]
-disabled = []
-before_creation = null
-after_creation = null
-after_pipeline_creation = null
-batch_size = 1000
-tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
-
-[components]
-
-[components.ner]
-factory = "ner"
-incorrect_spans_key = null
-moves = null
-scorer = {"@scorers":"spacy.ner_scorer.v1"}
-update_with_oracle_cut_size = 100
-
-[components.ner.model]
-@architectures = "spacy.TransitionBasedParser.v2"
-state_type = "ner"
-extra_state_tokens = false
-hidden_width = 64
-maxout_pieces = 2
-use_upper = true
-nO = null
-
-[components.ner.model.tok2vec]
-@architectures = "spacy.HashEmbedCNN.v2"
-pretrained_vectors = null
-width = 96
-depth = 4
-embed_size = 2000
-window_size = 1
-maxout_pieces = 3
-subword_features = true
-
-[corpora]
-
-[corpora.dev]
-@readers = "spacy.Corpus.v1"
-path = ${paths.dev}
-gold_preproc = false
-max_length = 0
-limit = 0
-augmenter = null
-
-[corpora.train]
-@readers = "spacy.Corpus.v1"
-path = ${paths.train}
-gold_preproc = false
-max_length = 0
-limit = 0
-augmenter = null
-
-[training]
-seed = ${system.seed}
-gpu_allocator = ${system.gpu_allocator}
-dropout = 0.1
-accumulate_gradient = 1
-patience = 1600
-max_epochs = 0
-max_steps = 20000
-eval_frequency = 200
-frozen_components = []
-annotating_components = []
-dev_corpus = "corpora.dev"
-train_corpus = "corpora.train"
-before_to_disk = null
-
-[training.batcher]
-@batchers = "spacy.batch_by_words.v1"
-discard_oversize = false
-tolerance = 0.2
-get_length = null
-
-[training.batcher.size]
-@schedules = "compounding.v1"
-start = 100
-stop = 1000
-compound = 1.001
-t = 0.0
-
-[training.logger]
-@loggers = "spacy.ConsoleLogger.v1"
-progress_bar = false
-
-[training.optimizer]
-@optimizers = "Adam.v1"
-beta1 = 0.9
-beta2 = 0.999
-L2_is_weight_decay = true
-L2 = 0.01
-grad_clip = 1.0
-use_averages = false
-eps = 0.00000001
-learn_rate = 0.001
-
-[training.score_weights]
-ents_f = 1.0
-ents_p = 0.0
-ents_r = 0.0
-ents_per_type = null
-
-[pretraining]
-
-[initialize]
-vectors = ${paths.vectors}
-init_tok2vec = ${paths.init_tok2vec}
-vocab_data = null
-lookups = null
-before_init = null
-after_init = null
-
-[initialize.components]
-
-[initialize.tokenizer]

models/invoice-de-0.1.0/meta.json

@@ -1,38 +0,0 @@
-{
-  "lang":"de",
-  "name":"pipeline",
-  "version":"0.0.0",
-  "spacy_version":">=3.4.1,<3.5.0",
-  "description":"",
-  "author":"",
-  "email":"",
-  "url":"",
-  "license":"",
-  "spacy_git_version":"Unknown",
-  "vectors":{
-    "width":0,
-    "vectors":0,
-    "keys":0,
-    "name":null,
-    "mode":"default"
-  },
-  "labels":{
-    "ner":[
-      "cdtr.bic",
-      "cdtr.iban",
-      "cdtr.name",
-      "invoice.date",
-      "invoice.number",
-      "invoice.total"
-    ]
-  },
-  "pipeline":[
-    "ner"
-  ],
-  "components":[
-    "ner"
-  ],
-  "disabled":[
-
-  ]
-}

models/invoice-de-0.1.0/ner/cfg

@@ -1,13 +0,0 @@
-{
-  "moves":null,
-  "update_with_oracle_cut_size":100,
-  "multitasks":[
-
-  ],
-  "min_action_freq":1,
-  "learn_tokens":false,
-  "beam_width":1,
-  "beam_density":0.0,
-  "beam_update_prob":0.0,
-  "incorrect_spans_key":null
-}

models/invoice-de-0.1.0/ner/moves

@@ -1 +0,0 @@
-‚¥movesÚ
Ä{"0":{},"1":{"invoice.total":-1,"invoice.number":-2,"cdtr.name":-3,"invoice.date":-4,"cdtr.iban":-5,"cdtr.bic":-6},"2":{"invoice.total":-1,"invoice.number":-2,"cdtr.name":-3,"invoice.date":-4,"cdtr.iban":-5,"cdtr.bic":-6},"3":{"invoice.total":-1,"invoice.number":-2,"cdtr.name":-3,"invoice.date":-4,"cdtr.iban":-5,"cdtr.bic":-6},"4":{"":1,"invoice.total":-1,"invoice.number":-2,"cdtr.name":-3,"invoice.date":-4,"cdtr.iban":-5,"cdtr.bic":-6},"5":{"":1}}£cfg<66>§neg_keyÀ

models/invoice-de-0.1.0/vocab/key2row

@@ -1 +0,0 @@
-<EFBFBD>

models/invoice-de-0.1.0/vocab/lookups.bin

@@ -1 +0,0 @@
-<EFBFBD>

models/invoice-de-0.1.0/vocab/vectors.cfg

@@ -1,3 +0,0 @@
-{
-  "mode":"default"
-}

models/invoice-de-0.1.0_2022_01_01/config.cfg

@@ -1,127 +0,0 @@
-[paths]
-train = null
-dev = null
-vectors = null
-init_tok2vec = null
-
-[system]
-seed = 0
-gpu_allocator = null
-
-[nlp]
-lang = "de"
-pipeline = ["ner"]
-disabled = []
-before_creation = null
-after_creation = null
-after_pipeline_creation = null
-batch_size = 1000
-tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
-
-[components]
-
-[components.ner]
-factory = "ner"
-incorrect_spans_key = null
-moves = null
-update_with_oracle_cut_size = 100
-
-[components.ner.model]
-@architectures = "spacy.TransitionBasedParser.v2"
-state_type = "ner"
-extra_state_tokens = false
-hidden_width = 64
-maxout_pieces = 2
-use_upper = true
-nO = null
-
-[components.ner.model.tok2vec]
-@architectures = "spacy.HashEmbedCNN.v2"
-pretrained_vectors = null
-width = 96
-depth = 4
-embed_size = 2000
-window_size = 1
-maxout_pieces = 3
-subword_features = true
-
-[corpora]
-
-[corpora.dev]
-@readers = "spacy.Corpus.v1"
-path = ${paths.dev}
-gold_preproc = false
-max_length = 0
-limit = 0
-augmenter = null
-
-[corpora.train]
-@readers = "spacy.Corpus.v1"
-path = ${paths.train}
-gold_preproc = false
-max_length = 0
-limit = 0
-augmenter = null
-
-[training]
-seed = ${system.seed}
-gpu_allocator = ${system.gpu_allocator}
-dropout = 0.1
-accumulate_gradient = 1
-patience = 1600
-max_epochs = 0
-max_steps = 20000
-eval_frequency = 200
-frozen_components = []
-annotating_components = []
-dev_corpus = "corpora.dev"
-train_corpus = "corpora.train"
-before_to_disk = null
-
-[training.batcher]
-@batchers = "spacy.batch_by_words.v1"
-discard_oversize = false
-tolerance = 0.2
-get_length = null
-
-[training.batcher.size]
-@schedules = "compounding.v1"
-start = 100
-stop = 1000
-compound = 1.001
-t = 0.0
-
-[training.logger]
-@loggers = "spacy.ConsoleLogger.v1"
-progress_bar = false
-
-[training.optimizer]
-@optimizers = "Adam.v1"
-beta1 = 0.9
-beta2 = 0.999
-L2_is_weight_decay = true
-L2 = 0.01
-grad_clip = 1.0
-use_averages = false
-eps = 0.00000001
-learn_rate = 0.001
-
-[training.score_weights]
-ents_f = 1.0
-ents_p = 0.0
-ents_r = 0.0
-ents_per_type = null
-
-[pretraining]
-
-[initialize]
-vectors = ${paths.vectors}
-init_tok2vec = ${paths.init_tok2vec}
-vocab_data = null
-lookups = null
-before_init = null
-after_init = null
-
-[initialize.components]
-
-[initialize.tokenizer]

models/invoice-de-0.1.0_2022_01_01/meta.json

@@ -1,40 +0,0 @@
-{
-  "lang":"de",
-  "name":"pipeline",
-  "version":"0.0.0",
-  "spacy_version":">=3.1.1,<3.2.0",
-  "description":"",
-  "author":"",
-  "email":"",
-  "url":"",
-  "license":"",
-  "spacy_git_version":"ffaead8fe",
-  "vectors":{
-    "width":0,
-    "vectors":0,
-    "keys":0,
-    "name":null
-  },
-  "labels":{
-    "ner":[
-      "cdtr.bic",
-      "cdtr.iban",
-      "cdtr.name",
-      "invoice.date",
-      "invoice.number",
-      "invoice.total"
-    ]
-  },
-  "pipeline":[
-    "ner"
-  ],
-  "components":[
-    "ner"
-  ],
-  "disabled":[
-
-  ],
-  "_sourced_vectors_hashes":{
-
-  }
-}

models/invoice-de-0.1.0_2022_01_01/ner/cfg

@@ -1,13 +0,0 @@
-{
-  "moves":null,
-  "update_with_oracle_cut_size":100,
-  "multitasks":[
-
-  ],
-  "min_action_freq":1,
-  "learn_tokens":false,
-  "beam_width":1,
-  "beam_density":0.0,
-  "beam_update_prob":0.0,
-  "incorrect_spans_key":null
-}

models/invoice-de-0.1.0_2022_01_01/ner/moves

@@ -1 +0,0 @@
-‚¥movesÚ
Ä{"0":{},"1":{"cdtr.name":-1,"cdtr.iban":-2,"cdtr.bic":-3,"invoice.total":-4,"invoice.date":-5,"invoice.number":-6},"2":{"cdtr.name":-1,"cdtr.iban":-2,"cdtr.bic":-3,"invoice.total":-4,"invoice.date":-5,"invoice.number":-6},"3":{"cdtr.name":-1,"cdtr.iban":-2,"cdtr.bic":-3,"invoice.total":-4,"invoice.date":-5,"invoice.number":-6},"4":{"":1,"cdtr.name":-1,"cdtr.iban":-2,"cdtr.bic":-3,"invoice.total":-4,"invoice.date":-5,"invoice.number":-6},"5":{"":1}}£cfg<66>§neg_keyÀ

models/invoice-de-0.1.0_2022_01_01/vocab/key2row

@@ -1 +0,0 @@
-<EFBFBD>

models/invoice-de-0.1.0_2022_01_01/vocab/lookups.bin

@@ -1 +0,0 @@
-<EFBFBD>

models/invoice-de-0.2.0/config.cfg

@@ -1,128 +0,0 @@
-[paths]
-train = null
-dev = null
-vectors = null
-init_tok2vec = null
-
-[system]
-seed = 0
-gpu_allocator = null
-
-[nlp]
-lang = "de"
-pipeline = ["ner"]
-disabled = []
-before_creation = null
-after_creation = null
-after_pipeline_creation = null
-batch_size = 1000
-tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
-
-[components]
-
-[components.ner]
-factory = "ner"
-incorrect_spans_key = null
-moves = null
-scorer = {"@scorers":"spacy.ner_scorer.v1"}
-update_with_oracle_cut_size = 100
-
-[components.ner.model]
-@architectures = "spacy.TransitionBasedParser.v2"
-state_type = "ner"
-extra_state_tokens = false
-hidden_width = 64
-maxout_pieces = 2
-use_upper = true
-nO = null
-
-[components.ner.model.tok2vec]
-@architectures = "spacy.HashEmbedCNN.v2"
-pretrained_vectors = null
-width = 96
-depth = 4
-embed_size = 2000
-window_size = 1
-maxout_pieces = 3
-subword_features = true
-
-[corpora]
-
-[corpora.dev]
-@readers = "spacy.Corpus.v1"
-path = ${paths.dev}
-gold_preproc = false
-max_length = 0
-limit = 0
-augmenter = null
-
-[corpora.train]
-@readers = "spacy.Corpus.v1"
-path = ${paths.train}
-gold_preproc = false
-max_length = 0
-limit = 0
-augmenter = null
-
-[training]
-seed = ${system.seed}
-gpu_allocator = ${system.gpu_allocator}
-dropout = 0.1
-accumulate_gradient = 1
-patience = 1600
-max_epochs = 0
-max_steps = 20000
-eval_frequency = 200
-frozen_components = []
-annotating_components = []
-dev_corpus = "corpora.dev"
-train_corpus = "corpora.train"
-before_to_disk = null
-
-[training.batcher]
-@batchers = "spacy.batch_by_words.v1"
-discard_oversize = false
-tolerance = 0.2
-get_length = null
-
-[training.batcher.size]
-@schedules = "compounding.v1"
-start = 100
-stop = 1000
-compound = 1.001
-t = 0.0
-
-[training.logger]
-@loggers = "spacy.ConsoleLogger.v1"
-progress_bar = false
-
-[training.optimizer]
-@optimizers = "Adam.v1"
-beta1 = 0.9
-beta2 = 0.999
-L2_is_weight_decay = true
-L2 = 0.01
-grad_clip = 1.0
-use_averages = false
-eps = 0.00000001
-learn_rate = 0.001
-
-[training.score_weights]
-ents_f = 1.0
-ents_p = 0.0
-ents_r = 0.0
-ents_per_type = null
-
-[pretraining]
-
-[initialize]
-vectors = ${paths.vectors}
-init_tok2vec = ${paths.init_tok2vec}
-vocab_data = null
-lookups = null
-before_init = null
-after_init = null
-
-[initialize.components]
-
-[initialize.tokenizer]

models/invoice-de-0.2.0/meta.json

@@ -1,38 +0,0 @@
-{
-  "lang":"de",
-  "name":"pipeline",
-  "version":"0.0.0",
-  "spacy_version":">=3.4.1,<3.5.0",
-  "description":"",
-  "author":"",
-  "email":"",
-  "url":"",
-  "license":"",
-  "spacy_git_version":"Unknown",
-  "vectors":{
-    "width":0,
-    "vectors":0,
-    "keys":0,
-    "name":null,
-    "mode":"default"
-  },
-  "labels":{
-    "ner":[
-      "cdtr.bic",
-      "cdtr.iban",
-      "cdtr.name",
-      "invoice.date",
-      "invoice.number",
-      "invoice.total"
-    ]
-  },
-  "pipeline":[
-    "ner"
-  ],
-  "components":[
-    "ner"
-  ],
-  "disabled":[
-
-  ]
-}

models/invoice-de-0.2.0/ner/cfg

@@ -1,13 +0,0 @@
-{
-  "moves":null,
-  "update_with_oracle_cut_size":100,
-  "multitasks":[
-
-  ],
-  "min_action_freq":1,
-  "learn_tokens":false,
-  "beam_width":1,
-  "beam_density":0.0,
-  "beam_update_prob":0.0,
-  "incorrect_spans_key":null
-}

models/invoice-de-0.2.0/ner/moves

@@ -1 +0,0 @@
-‚¥movesÚ
Ä{"0":{},"1":{"invoice.total":-1,"invoice.number":-2,"invoice.date":-3,"cdtr.name":-4,"cdtr.iban":-5,"cdtr.bic":-6},"2":{"invoice.total":-1,"invoice.number":-2,"invoice.date":-3,"cdtr.name":-4,"cdtr.iban":-5,"cdtr.bic":-6},"3":{"invoice.total":-1,"invoice.number":-2,"invoice.date":-3,"cdtr.name":-4,"cdtr.iban":-5,"cdtr.bic":-6},"4":{"":1,"invoice.total":-1,"invoice.number":-2,"invoice.date":-3,"cdtr.name":-4,"cdtr.iban":-5,"cdtr.bic":-6},"5":{"":1}}£cfg<66>§neg_keyÀ

models/invoice-de-0.2.0/vocab/key2row

@@ -1 +0,0 @@
-<EFBFBD>

models/invoice-de-0.2.0/vocab/lookups.bin

@@ -1 +0,0 @@
-<EFBFBD>

models/invoice-de-0.2.0/vocab/vectors.cfg

@@ -1,3 +0,0 @@
-{
-  "mode":"default"
-}

models/model-analyze-prod.xml

@@ -1,44 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
-<document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-	xmlns:xs="http://www.w3.org/2001/XMLSchema">
-	
-	<!-- Workflow Instance -->
-	<item name="workflow.endpoint"><value xsi:type="xs:string">https://alexander-logistics.office-workflow.de/api/</value></item>
-	<item name="workflow.userid"><value xsi:type="xs:string">admin</value></item>
-	<item name="workflow.password"><value xsi:type="xs:string">imixs4.null</value></item>
-
-	<!--   -->	
-	<item name="workflow.query"><value xsi:type="xs:string">($workflowgroup:"Rechnungseingang" OR $workflowgroup:"Sachrechnung") AND ($taskid:5900)</value></item>
-
-	<item name="workflow.pagesize"><value xsi:type="xs:int">100</value></item>
-	<item name="workflow.pageindex"><value xsi:type="xs:int">30</value></item>
-	<item name="workflow.entities">
-		<value xsi:type="xs:string">cdtr.name</value>
-		<value xsi:type="xs:string">cdtr.iban</value>
-		<value xsi:type="xs:string">cdtr.bic</value>
-		<value xsi:type="xs:string">invoice.total</value>
-		<value xsi:type="xs:string">invoice.date</value>
-		<value xsi:type="xs:string">invoice.number</value>
-	</item>
-	<item name="workflow.locale">
-		<value xsi:type="xs:string">en_GB</value>
-		<value xsi:type="xs:string">en_US</value>		
-		<value xsi:type="xs:string">de_DE</value>
-	</item>
-	
-	<!-- Tika OCR Server  -->
-	<item name="tika.ocrmode"><value xsi:type="xs:string">OCR_ONLY</value></item>
-	<item name="tika.options">
-		<value xsi:type="xs:string">X-Tika-OCRLanguage=eng+deu</value>
-		<value xsi:type="xs:string">X-Tika-PDFocrStrategy=OCR_ONLY</value>		
-	</item>
-
-	<!-- ML spaCy Server -->
-	<item name="ml.validation.endpoint"><value xsi:type="xs:string">http://imixs-ml-spacy:8000/</value></item>
-	<item name="ml.validation.model"><value xsi:type="xs:string">invoice-de-0.1.0</value></item>
-	<item name="ml.validation.filepattern"><value xsi:type="xs:string">.pdf|.PDF</value></item>
-	
-</document>
-
-
-

models/model-train-prod.xml

@@ -1,53 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
-<document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-	xmlns:xs="http://www.w3.org/2001/XMLSchema">
-	
-	<!-- Workflow Instance -->
-	<item name="workflow.endpoint"><value xsi:type="xs:string">https://alexander-logistics.office-workflow.de/api/</value></item>
-	<item name="workflow.userid"><value xsi:type="xs:string">admin</value></item>
-	<item name="workflow.password"><value xsi:type="xs:string">imixs4.null</value></item>
-
-	<item name="workflow.entities">
-		<value xsi:type="xs:string">cdtr.name</value>
-		<value xsi:type="xs:string">cdtr.iban</value>
-		<value xsi:type="xs:string">cdtr.bic</value>
-		<value xsi:type="xs:string">invoice.total</value>
-		<value xsi:type="xs:string">invoice.date</value>
-		<value xsi:type="xs:string">invoice.number</value>
-	</item>
-	<item name="workflow.locale">
-		<value xsi:type="xs:string">en_GB</value>
-		<value xsi:type="xs:string">en_US</value>		
-		<value xsi:type="xs:string">de_DE</value>
-	</item>
-	
-	<!-- Tika OCR Server  -->
-	<item name="tika.ocrmode"><value xsi:type="xs:string">OCR_ONLY</value></item>
-	<item name="tika.options">
-		<value xsi:type="xs:string">X-Tika-OCRLanguage=eng+deu</value>
-		<value xsi:type="xs:string">X-Tika-PDFocrStrategy=OCR_ONLY</value>		
-	</item>
-
-	<!-- ML spaCy Server -->
-	<item name="ml.training.endpoint"><value xsi:type="xs:string">http://imixs-ml-spacy:8000/</value></item>
-	<item name="ml.training.model"><value xsi:type="xs:string">invoice-de-0.2.0</value></item>
-	<item name="ml.training.filepattern"><value xsi:type="xs:string">.pdf|.PDF</value></item>
-        <!-- LOW | GOOD -->
-	<item name="ml.training.quality"><value xsi:type="xs:string">LOW</value></item>
-
-	
-	<!-- Define the training set and taining mode -->
-	<item name="workflow.query"><value xsi:type="xs:string">($workflowgroup:"Rechnungseingang" OR $workflowgroup:"Sachrechnung") AND ($taskid:5900)</value></item>	  
-	<item name="workflow.pagesize"><value xsi:type="xs:int">1000</value></item>
-	<item name="workflow.pageindex"><value xsi:type="xs:int">0</value></item>
-	<item name="ml.training.iterations"><value xsi:type="xs:string">5</value></item>
-	<item name="ml.training.dropoutrate"><value xsi:type="xs:string">0.0</value></item>
-	<item name="ml.options">
-		<value xsi:type="xs:string">min_losses=0.0&amp;retrain_rate=100</value>
-	</item>
-
-
-</document>
-
-
-

models/model-valiate-prod.xml

@@ -1,48 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
-<document xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-	xmlns:xs="http://www.w3.org/2001/XMLSchema">
-	
-	<!-- Workflow Instance -->
-	<item name="workflow.endpoint"><value xsi:type="xs:string">https://alexander-logistics.office-workflow.de/api/</value></item>
-	<item name="workflow.userid"><value xsi:type="xs:string">admin</value></item>
-	<item name="workflow.password"><value xsi:type="xs:string">imixs4.null</value></item>
-
-	<item name="workflow.entities">
-		<value xsi:type="xs:string">cdtr.name</value>
-		<value xsi:type="xs:string">cdtr.iban</value>
-		<value xsi:type="xs:string">cdtr.bic</value>
-		<value xsi:type="xs:string">invoice.total</value>
-		<value xsi:type="xs:string">invoice.date</value>
-		<value xsi:type="xs:string">invoice.number</value>
-	</item>
-	<item name="workflow.locale">
-		<value xsi:type="xs:string">en_GB</value>
-		<value xsi:type="xs:string">en_US</value>		
-		<value xsi:type="xs:string">de_DE</value>
-	</item>
-	
-	<!-- Tika OCR Server  -->
-	<item name="tika.ocrmode"><value xsi:type="xs:string">OCR_ONLY</value></item>
-	<item name="tika.options">
-		<value xsi:type="xs:string">X-Tika-OCRLanguage=eng+deu</value>
-		<value xsi:type="xs:string">X-Tika-PDFocrStrategy=OCR_ONLY</value>		
-	</item>
-
-	<!-- ML spaCy Server -->
-	<item name="ml.training.endpoint"><value xsi:type="xs:string">http://imixs-ml-spacy:8000/</value></item>
-	<item name="ml.training.model"><value xsi:type="xs:string">invoice-de-0.2.0</value></item>
-	<item name="ml.training.filepattern"><value xsi:type="xs:string">.pdf|.PDF</value></item>
-        <!-- LOW | GOOD -->
-	<item name="ml.training.quality"><value xsi:type="xs:string">LOW</value></item>
-
-	
-	<!-- Define the training set and taining mode -->
-	<item name="workflow.query"><value xsi:type="xs:string">($workflowgroup:"Rechnungseingang" OR $workflowgroup:"Sachrechnung") AND ($taskid:5900)</value></item>	  
-	<item name="workflow.pagesize"><value xsi:type="xs:int">100</value></item>
-	<item name="workflow.pageindex"><value xsi:type="xs:int">40</value></item>
-
-
-</document>
-
-
-

office-alexander-logistics-app/src/main/java/com/alexanderlogistics/einvoice/EInvoiceAdapter.java

@@ -7,6 +7,7 @@ import java.io.IOException;
 import java.io.InputStream;
 import java.text.NumberFormat;
 import java.text.SimpleDateFormat;
+import java.util.Date;
 import java.util.HashMap;
 import java.util.List;
 import java.util.Locale;
@@ -22,6 +23,7 @@ import org.apache.pdfbox.pdmodel.common.filespecification.PDComplexFileSpecifica
 import org.apache.pdfbox.pdmodel.common.filespecification.PDEmbeddedFile;
 import org.imixs.einvoice.EInvoiceFormatException;
 import org.imixs.einvoice.EInvoiceModel;
+import org.imixs.einvoice.EInvoiceModelCII;
 import org.imixs.einvoice.EInvoiceModelFactory;
 import org.imixs.einvoice.TradeLineItem;
 import org.imixs.einvoice.TradeParty;
@@ -243,6 +245,12 @@ public class EInvoiceAdapter implements SignalAdapter {
             // date
             model.setIssueDateTime(workitem.getItemValueLocalDate("invoice.date"));
             model.setDueDateTime(workitem.getItemValueLocalDate("invoice.duedate"));
+            // BT-20: Payment terms description, derived from the due date
+            Date dueDate = workitem.getItemValueDate("invoice.duedate");
+            if (dueDate != null) {
+                String formattedDueDate = dateFormatter.format(dueDate);
+                ((EInvoiceModelCII) model).setPaymentTermsDescription("Payment due by " + formattedDueDate);
+            }
 
             // Update Addresses
             TradeParty billingAddress = buildAddress(workitem.getItemValueString("partner.id"), "buyer");

office-alexander-logistics-app/src/test/resources/einvoice/output/einvoice-simple.xml

@@ -7,7 +7,7 @@
                           xmlns:xs="http://www.w3.org/2001/XMLSchema">
    <rsm:ExchangedDocumentContext>
       <ram:GuidelineSpecifiedDocumentContextParameter>
-         <ram:ID>urn:cen.eu:en16931:2017</ram:ID>
+         <ram:ID>urn:cen.eu:en16931:2017#compliant#urn:xoev-de:kosit:standard:xrechnung_3.0.2</ram:ID>
       </ram:GuidelineSpecifiedDocumentContextParameter>
    </rsm:ExchangedDocumentContext>
    <rsm:ExchangedDocument>
@@ -161,7 +161,7 @@
             <ram:RateApplicablePercent>23.0</ram:RateApplicablePercent>
          </ram:ApplicableTradeTax>
          <ram:SpecifiedTradePaymentTerms>
-            <ram:Description/>
+            <ram:Description>Payment due by 10.03.2025</ram:Description>
             <ram:DueDateDateTime>
                <udt:DateTimeString format="102">20250310</udt:DateTimeString>
             </ram:DueDateDateTime>