Hugging Face's logo

HueyNemud
/
icdar23-entrydetector_plaintext_breaks_indents_left_ref

Token Classification
Transformers
PyTorch
camembert
Generated from Trainer
Inference Endpoints
Model card Files Community
HueyNemud commited on
Commit
d1422cc
•
1 Parent(s): aa3d45c

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +31 -23
README.md CHANGED
@@ -13,12 +13,12 @@ should probably proofread and complete it, then remove this comment. -->
13
 
14
  This model is a fine-tuned version of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on the None dataset.
15
  It achieves the following results on the evaluation set:
16
- - Loss: 0.0060
17
- - Ebegin: {'precision': 0.9928166351606805, 'recall': 0.9875893192929672, 'f1': 0.9901960784313725, 'number': 2659}
18
- - Eend: {'precision': 0.9984750285932139, 'recall': 0.9786995515695067, 'f1': 0.9884883940366107, 'number': 2676}
19
- - Overall Precision: 0.9956
20
- - Overall Recall: 0.9831
21
- - Overall F1: 0.9893
22
  - Overall Accuracy: 0.9982
23
 
24
  ## Model description
@@ -50,23 +50,31 @@ The following hyperparameters were used during training:
50
 
51
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
52
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
53
- | No log | 0.07 | 300 | 0.0401 | 0.9408 | 0.9725 | 0.9564 | 0.9936 |
54
- | 0.1602 | 0.14 | 600 | 0.0195 | 0.9896 | 0.9741 | 0.9818 | 0.9972 |
55
- | 0.1602 | 0.21 | 900 | 0.0101 | 0.9949 | 0.9875 | 0.9911 | 0.9985 |
56
- | 0.0205 | 0.29 | 1200 | 0.0117 | 0.9860 | 0.9894 | 0.9877 | 0.9979 |
57
- | 0.0104 | 0.36 | 1500 | 0.0091 | 0.9819 | 0.9948 | 0.9883 | 0.9979 |
58
- | 0.0104 | 0.43 | 1800 | 0.0058 | 0.9886 | 0.9933 | 0.9909 | 0.9984 |
59
- | 0.0081 | 0.5 | 2100 | 0.0067 | 0.9892 | 0.9931 | 0.9911 | 0.9984 |
60
- | 0.0081 | 0.57 | 2400 | 0.0049 | 0.9928 | 0.9939 | 0.9934 | 0.9988 |
61
- | 0.0069 | 0.64 | 2700 | 0.0048 | 0.9895 | 0.9931 | 0.9913 | 0.9985 |
62
- | 0.0066 | 0.72 | 3000 | 0.0061 | 0.9971 | 0.9865 | 0.9918 | 0.9985 |
63
- | 0.0066 | 0.79 | 3300 | 0.0042 | 0.9954 | 0.9927 | 0.9940 | 0.9990 |
64
- | 0.0046 | 0.86 | 3600 | 0.0039 | 0.9958 | 0.9923 | 0.9941 | 0.9990 |
65
- | 0.0046 | 0.93 | 3900 | 0.0058 | 0.9835 | 0.9959 | 0.9896 | 0.9981 |
66
- | 0.0052 | 1.0 | 4200 | 0.0055 | 0.9963 | 0.9892 | 0.9927 | 0.9987 |
67
- | 0.003 | 1.07 | 4500 | 0.0051 | 0.9939 | 0.9929 | 0.9934 | 0.9988 |
68
- | 0.003 | 1.14 | 4800 | 0.0075 | 0.9977 | 0.9871 | 0.9924 | 0.9987 |
69
- | 0.0039 | 1.22 | 5100 | 0.0051 | 0.9952 | 0.9922 | 0.9937 | 0.9989 |
 
 
 
 
 
 
 
 
70
 
71
 
72
  ### Framework versions
 
13
 
14
  This model is a fine-tuned version of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on the None dataset.
15
  It achieves the following results on the evaluation set:
16
+ - Loss: 0.0062
17
+ - Ebegin: {'precision': 0.997709049255441, 'recall': 0.9827002632568634, 'f1': 0.9901477832512315, 'number': 2659}
18
+ - Eend: {'precision': 0.9973363774733638, 'recall': 0.9794469357249627, 'f1': 0.9883107088989442, 'number': 2676}
19
+ - Overall Precision: 0.9975
20
+ - Overall Recall: 0.9811
21
+ - Overall F1: 0.9892
22
  - Overall Accuracy: 0.9982
23
 
24
  ## Model description
 
50
 
51
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
52
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
53
+ | No log | 0.07 | 300 | 0.0389 | 0.9556 | 0.9663 | 0.9609 | 0.9949 |
54
+ | 0.1775 | 0.14 | 600 | 0.0162 | 0.9854 | 0.9893 | 0.9873 | 0.9977 |
55
+ | 0.1775 | 0.21 | 900 | 0.0114 | 0.9928 | 0.9889 | 0.9909 | 0.9984 |
56
+ | 0.0229 | 0.29 | 1200 | 0.0172 | 0.9793 | 0.9851 | 0.9822 | 0.9975 |
57
+ | 0.016 | 0.36 | 1500 | 0.0087 | 0.9906 | 0.9907 | 0.9907 | 0.9984 |
58
+ | 0.016 | 0.43 | 1800 | 0.0079 | 0.9955 | 0.9879 | 0.9917 | 0.9985 |
59
+ | 0.0115 | 0.5 | 2100 | 0.0093 | 0.9910 | 0.9912 | 0.9911 | 0.9984 |
60
+ | 0.0115 | 0.57 | 2400 | 0.0102 | 0.9816 | 0.9942 | 0.9878 | 0.9978 |
61
+ | 0.0109 | 0.64 | 2700 | 0.0072 | 0.9895 | 0.9939 | 0.9917 | 0.9985 |
62
+ | 0.0075 | 0.72 | 3000 | 0.0055 | 0.9919 | 0.9917 | 0.9918 | 0.9985 |
63
+ | 0.0075 | 0.79 | 3300 | 0.0078 | 0.9948 | 0.9910 | 0.9929 | 0.9987 |
64
+ | 0.007 | 0.86 | 3600 | 0.0057 | 0.9937 | 0.9933 | 0.9935 | 0.9989 |
65
+ | 0.007 | 0.93 | 3900 | 0.0059 | 0.9830 | 0.9957 | 0.9893 | 0.9981 |
66
+ | 0.0055 | 1.0 | 4200 | 0.0049 | 0.9972 | 0.9899 | 0.9935 | 0.9988 |
67
+ | 0.0029 | 1.07 | 4500 | 0.0064 | 0.9944 | 0.9926 | 0.9935 | 0.9989 |
68
+ | 0.0029 | 1.14 | 4800 | 0.0057 | 0.9927 | 0.9919 | 0.9923 | 0.9987 |
69
+ | 0.0043 | 1.22 | 5100 | 0.0064 | 0.9890 | 0.9945 | 0.9917 | 0.9986 |
70
+ | 0.0043 | 1.29 | 5400 | 0.0058 | 0.9857 | 0.9957 | 0.9907 | 0.9983 |
71
+ | 0.0028 | 1.36 | 5700 | 0.0049 | 0.9961 | 0.9922 | 0.9941 | 0.9990 |
72
+ | 0.0034 | 1.43 | 6000 | 0.0048 | 0.9952 | 0.9937 | 0.9945 | 0.9990 |
73
+ | 0.0034 | 1.5 | 6300 | 0.0050 | 0.9936 | 0.9937 | 0.9937 | 0.9989 |
74
+ | 0.0022 | 1.57 | 6600 | 0.0046 | 0.9937 | 0.9934 | 0.9936 | 0.9989 |
75
+ | 0.0022 | 1.65 | 6900 | 0.0042 | 0.9954 | 0.9929 | 0.9941 | 0.9990 |
76
+ | 0.0039 | 1.72 | 7200 | 0.0042 | 0.9959 | 0.9931 | 0.9945 | 0.9990 |
77
+ | 0.003 | 1.79 | 7500 | 0.0039 | 0.9968 | 0.9927 | 0.9947 | 0.9991 |
78
 
79
 
80
  ### Framework versions