Hugging Face's logo

denizspynk
/
req_mod_ner_modelv2

Token Classification
Transformers
PyTorch
Dutch
roberta
Generated from Trainer
Model card Files Community
1
denizspynk commited on
Commit
9649e7b
·
1 Parent(s): c67405b

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +33 -21
README.md CHANGED
@@ -10,6 +10,12 @@ metrics:
10
  model-index:
11
  - name: req_mod_ner_modelv2
12
  results: []
 
 
 
 
 
 
13
  ---
14
 
15
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -19,10 +25,10 @@ should probably proofread and complete it, then remove this comment. -->
19
 
20
  This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-ner](https://huggingface.co/pdelobelle/robbert-v2-dutch-ner) on the None dataset.
21
  It achieves the following results on the evaluation set:
22
- - Loss: 0.6759
23
- - Precision: 0.7112
24
- - Recall: 0.7644
25
- - F1: 0.7368
26
  - Accuracy: 0.9261
27
 
28
  ## Model description
@@ -50,27 +56,33 @@ The following hyperparameters were used during training:
50
  - lr_scheduler_type: linear
51
  - num_epochs: 16
52
 
 
 
 
 
 
 
 
53
  ### Training results
54
 
55
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
56
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
57
- | No log | 1.0 | 270 | 0.4543 | 0.6111 | 0.6322 | 0.6215 | 0.8989 |
58
- | 0.4703 | 2.0 | 540 | 0.4260 | 0.5129 | 0.6839 | 0.5862 | 0.8740 |
59
- | 0.4703 | 3.0 | 810 | 0.3841 | 0.6034 | 0.6207 | 0.6119 | 0.9020 |
60
- | 0.1808 | 4.0 | 1080 | 0.5177 | 0.6124 | 0.6264 | 0.6193 | 0.8974 |
61
- | 0.1808 | 5.0 | 1350 | 0.4433 | 0.6911 | 0.7586 | 0.7233 | 0.9168 |
62
- | 0.0933 | 6.0 | 1620 | 0.4377 | 0.7207 | 0.7414 | 0.7309 | 0.9253 |
63
- | 0.0933 | 7.0 | 1890 | 0.5752 | 0.6333 | 0.6552 | 0.6441 | 0.9114 |
64
- | 0.0551 | 8.0 | 2160 | 0.5671 | 0.6684 | 0.7529 | 0.7081 | 0.9191 |
65
- | 0.0551 | 9.0 | 2430 | 0.5356 | 0.6862 | 0.7414 | 0.7127 | 0.9253 |
66
- | 0.0394 | 10.0 | 2700 | 0.6103 | 0.6736 | 0.7471 | 0.7084 | 0.9152 |
67
- | 0.0394 | 11.0 | 2970 | 0.6029 | 0.6885 | 0.7241 | 0.7059 | 0.9246 |
68
- | 0.0286 | 12.0 | 3240 | 0.5353 | 0.6995 | 0.7356 | 0.7171 | 0.9277 |
69
- | 0.0219 | 13.0 | 3510 | 0.6057 | 0.7120 | 0.7529 | 0.7318 | 0.9261 |
70
- | 0.0219 | 14.0 | 3780 | 0.6601 | 0.7167 | 0.7414 | 0.7288 | 0.9261 |
71
- | 0.0144 | 15.0 | 4050 | 0.6678 | 0.7090 | 0.7701 | 0.7383 | 0.9261 |
72
- | 0.0144 | 16.0 | 4320 | 0.6759 | 0.7112 | 0.7644 | 0.7368 | 0.9261 |
73
-
74
 
75
  ### Framework versions
76
 
 
10
  model-index:
11
  - name: req_mod_ner_modelv2
12
  results: []
13
+ widget:
14
+ - text: "De Oplossing ondersteunt het zoeken op de metadata van zaken, documenten en objecten en op gegevens uit de basisregistraties die gekoppeld zijn aan een zaak."
15
+ - text: "De Oplossing ondersteunt parafering en het plaatsen van een gecertificeerde elektronische handtekening."
16
+ - text: "De Aangeboden oplossing stelt de medewerker in staat een zaak te registreren."
17
+ - text: "Het Financieel systeem heeft functionaliteit om een debiteurenadministratie te voeren."
18
+ - text: "Als gebruiker wil ik dat de oplossing mij naar zaken laat zoeken op basis van zaaknummer, zaaktitel, omschrijving en datum."
19
  ---
20
 
21
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
25
 
26
  This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-ner](https://huggingface.co/pdelobelle/robbert-v2-dutch-ner) on the None dataset.
27
  It achieves the following results on the evaluation set:
28
+ - Loss: 0.6678
29
+ - Precision: 0.7090
30
+ - Recall: 0.7701
31
+ - F1: 0.7383
32
  - Accuracy: 0.9261
33
 
34
  ## Model description
 
56
  - lr_scheduler_type: linear
57
  - num_epochs: 16
58
 
59
+ ### Evaluation results
60
+
61
+ | Validation Loss | Precision | Recall | F1 | Accuracy |
62
+ |:---------------:|:---------:|:------:|:------:|:--------:|
63
+ | 0.6678 | 0.7090 | 0.7701 | 0.7383 | 0.9261 |
64
+
65
+
66
  ### Training results
67
 
68
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
69
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
70
+ | No log | 1.0 | 240 | 0.4780 | 0.3456 | 0.4052 | 0.3730 | 0.8789 |
71
+ | No log | 2.0 | 480 | 0.3903 | 0.5934 | 0.4655 | 0.5217 | 0.9080 |
72
+ | 0.4168 | 3.0 | 720 | 0.5082 | 0.6782 | 0.5086 | 0.5813 | 0.9169 |
73
+ | 0.4168 | 4.0 | 960 | 0.4307 | 0.5846 | 0.6552 | 0.6179 | 0.9201 |
74
+ | 0.1633 | 5.0 | 1200 | 0.5179 | 0.6 | 0.5948 | 0.5974 | 0.9233 |
75
+ | 0.1633 | 6.0 | 1440 | 0.6073 | 0.5752 | 0.5603 | 0.5677 | 0.9185 |
76
+ | 0.0676 | 7.0 | 1680 | 0.6198 | 0.6638 | 0.6638 | 0.6638 | 0.9233 |
77
+ | 0.0676 | 8.0 | 1920 | 0.6876 | 0.6311 | 0.6638 | 0.6471 | 0.9185 |
78
+ | 0.0445 | 9.0 | 2160 | 0.7112 | 0.6522 | 0.6466 | 0.6494 | 0.9201 |
79
+ | 0.0445 | 10.0 | 2400 | 0.7232 | 0.6522 | 0.6466 | 0.6494 | 0.9193 |
80
+ | 0.0259 | 11.0 | 2640 | 0.6511 | 0.6371 | 0.6810 | 0.6583 | 0.9233 |
81
+ | 0.0259 | 12.0 | 2880 | 0.6733 | 0.6783 | 0.6724 | 0.6753 | 0.9257 |
82
+ | 0.0146 | 13.0 | 3120 | 0.6636 | 0.6695 | 0.6810 | 0.6752 | 0.9282 |
83
+ | 0.0146 | 14.0 | 3360 | 0.6943 | 0.6496 | 0.6552 | 0.6524 | 0.9257 |
84
+ | 0.0134 | 15.0 | 3600 | 0.7055 | 0.6552 | 0.6552 | 0.6552 | 0.9257 |
85
+ | 0.0134 | 16.0 | 3840 | 0.7115 | 0.6522 | 0.6466 | 0.6494 | 0.9249 |
 
86
 
87
  ### Framework versions
88