Hugging Face's logo

xXiaobuding
/
distilbert-base-uncased_ai4privacy_en

Token Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Inference Endpoints
Model card Files Community
xXiaobuding commited on
Commit
a99ad33
•
1 Parent(s): 3e4c713

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +121 -0
README.md ADDED
@@ -0,0 +1,121 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - generated_from_trainer
5
+ model-index:
6
+ - name: distilbert-base-uncased_ai4privacy_en
7
+ results: []
8
+ ---
9
+
10
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
11
+ should probably proofread and complete it, then remove this comment. -->
12
+
13
+ # distilbert-base-uncased_ai4privacy_en
14
+
15
+ This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
16
+ It achieves the following results on the evaluation set:
17
+ - Loss: 0.0935
18
+ - Overall Precision: 0.9169
19
+ - Overall Recall: 0.9382
20
+ - Overall F1: 0.9274
21
+ - Overall Accuracy: 0.9662
22
+ - Accountname F1: 0.9924
23
+ - Accountnumber F1: 0.9878
24
+ - Age F1: 0.9283
25
+ - Amount F1: 0.9224
26
+ - Bic F1: 0.9018
27
+ - Bitcoinaddress F1: 0.8930
28
+ - Buildingnumber F1: 0.8944
29
+ - City F1: 0.9543
30
+ - Companyname F1: 0.9847
31
+ - County F1: 0.9807
32
+ - Creditcardcvv F1: 0.9191
33
+ - Creditcardissuer F1: 0.9831
34
+ - Creditcardnumber F1: 0.9029
35
+ - Currency F1: 0.7268
36
+ - Currencycode F1: 0.8590
37
+ - Currencyname F1: 0.4625
38
+ - Currencysymbol F1: 0.9503
39
+ - Date F1: 0.8227
40
+ - Dob F1: 0.6515
41
+ - Email F1: 0.9884
42
+ - Ethereumaddress F1: 0.9890
43
+ - Eyecolor F1: 0.9274
44
+ - Firstname F1: 0.9726
45
+ - Gender F1: 0.9791
46
+ - Height F1: 0.9814
47
+ - Iban F1: 0.9862
48
+ - Ip F1: 0.1964
49
+ - Ipv4 F1: 0.8063
50
+ - Ipv6 F1: 0.7958
51
+ - Jobarea F1: 0.9265
52
+ - Jobtitle F1: 0.9965
53
+ - Jobtype F1: 0.9482
54
+ - Lastname F1: 0.9469
55
+ - Litecoinaddress F1: 0.7767
56
+ - Mac F1: 0.9892
57
+ - Maskednumber F1: 0.8689
58
+ - Middlename F1: 0.9628
59
+ - Nearbygpscoordinate F1: 0.9955
60
+ - Ordinaldirection F1: 0.9784
61
+ - Password F1: 0.9503
62
+ - Phoneimei F1: 0.9944
63
+ - Phonenumber F1: 0.9799
64
+ - Pin F1: 0.9085
65
+ - Prefix F1: 0.9463
66
+ - Secondaryaddress F1: 0.9902
67
+ - Sex F1: 0.9752
68
+ - Ssn F1: 0.9759
69
+ - State F1: 0.9765
70
+ - Street F1: 0.9651
71
+ - Time F1: 0.9740
72
+ - Url F1: 0.9889
73
+ - Useragent F1: 0.9778
74
+ - Username F1: 0.9885
75
+ - Vehiclevin F1: 0.9621
76
+ - Vehiclevrm F1: 0.9840
77
+ - Zipcode F1: 0.8823
78
+
79
+ ## Model description
80
+
81
+ More information needed
82
+
83
+ ## Intended uses & limitations
84
+
85
+ More information needed
86
+
87
+ ## Training and evaluation data
88
+
89
+ More information needed
90
+
91
+ ## Training procedure
92
+
93
+ ### Training hyperparameters
94
+
95
+ The following hyperparameters were used during training:
96
+ - learning_rate: 5e-05
97
+ - train_batch_size: 2
98
+ - eval_batch_size: 2
99
+ - seed: 42
100
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
101
+ - lr_scheduler_type: cosine_with_restarts
102
+ - lr_scheduler_warmup_ratio: 0.2
103
+ - num_epochs: 5
104
+
105
+ ### Training results
106
+
107
+ | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Accountname F1 | Accountnumber F1 | Age F1 | Amount F1 | Bic F1 | Bitcoinaddress F1 | Buildingnumber F1 | City F1 | Companyname F1 | County F1 | Creditcardcvv F1 | Creditcardissuer F1 | Creditcardnumber F1 | Currency F1 | Currencycode F1 | Currencyname F1 | Currencysymbol F1 | Date F1 | Dob F1 | Email F1 | Ethereumaddress F1 | Eyecolor F1 | Firstname F1 | Gender F1 | Height F1 | Iban F1 | Ip F1 | Ipv4 F1 | Ipv6 F1 | Jobarea F1 | Jobtitle F1 | Jobtype F1 | Lastname F1 | Litecoinaddress F1 | Mac F1 | Maskednumber F1 | Middlename F1 | Nearbygpscoordinate F1 | Ordinaldirection F1 | Password F1 | Phoneimei F1 | Phonenumber F1 | Pin F1 | Prefix F1 | Secondaryaddress F1 | Sex F1 | Ssn F1 | State F1 | Street F1 | Time F1 | Url F1 | Useragent F1 | Username F1 | Vehiclevin F1 | Vehiclevrm F1 | Zipcode F1 |
108
+ |:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:------:|:---------:|:------:|:-----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:-----------:|:---------------:|:---------------:|:-----------------:|:-------:|:------:|:--------:|:------------------:|:-----------:|:------------:|:---------:|:---------:|:-------:|:------:|:-------:|:-------:|:----------:|:-----------:|:----------:|:-----------:|:------------------:|:------:|:---------------:|:-------------:|:----------------------:|:-------------------:|:-----------:|:------------:|:--------------:|:------:|:---------:|:-------------------:|:------:|:------:|:--------:|:---------:|:-------:|:------:|:------------:|:-----------:|:-------------:|:-------------:|:----------:|
109
+ | 0.1133 | 1.0 | 17398 | 0.1049 | 0.8638 | 0.9087 | 0.8857 | 0.9619 | 0.9789 | 0.9552 | 0.8739 | 0.8043 | 0.9020 | 0.9265 | 0.8585 | 0.8866 | 0.9521 | 0.9754 | 0.8213 | 0.9718 | 0.8201 | 0.4143 | 0.7182 | 0.0921 | 0.9064 | 0.7717 | 0.4968 | 0.9811 | 0.9876 | 0.8715 | 0.9487 | 0.9602 | 0.9843 | 0.8432 | 0.0 | 0.8434 | 0.7986 | 0.8906 | 0.9808 | 0.9205 | 0.9021 | 0.8261 | 0.9741 | 0.7786 | 0.9252 | 0.9852 | 0.9458 | 0.9418 | 0.9848 | 0.9566 | 0.7740 | 0.9366 | 0.9924 | 0.9751 | 0.9822 | 0.9389 | 0.9228 | 0.9536 | 0.9772 | 0.9638 | 0.9276 | 0.9143 | 0.9043 | 0.8042 |
110
+ | 0.1019 | 2.0 | 34796 | 0.0958 | 0.9076 | 0.9315 | 0.9194 | 0.9665 | 0.9579 | 0.9564 | 0.9020 | 0.8964 | 0.8895 | 0.9511 | 0.8873 | 0.9390 | 0.9736 | 0.9761 | 0.9236 | 0.9794 | 0.8895 | 0.7610 | 0.8052 | 0.03 | 0.9429 | 0.8481 | 0.6713 | 0.9791 | 0.9850 | 0.8804 | 0.9651 | 0.9633 | 0.9753 | 0.9789 | 0.0046 | 0.8253 | 0.8038 | 0.8567 | 0.9903 | 0.9364 | 0.9394 | 0.8757 | 0.9764 | 0.8474 | 0.9321 | 0.9970 | 0.9641 | 0.9707 | 0.9944 | 0.9807 | 0.9198 | 0.8692 | 0.9913 | 0.9730 | 0.9594 | 0.9666 | 0.9568 | 0.9626 | 0.9842 | 0.9941 | 0.9780 | 0.9653 | 0.9368 | 0.8850 |
111
+ | 0.0777 | 3.0 | 52194 | 0.0935 | 0.9169 | 0.9382 | 0.9274 | 0.9662 | 0.9924 | 0.9878 | 0.9283 | 0.9224 | 0.9018 | 0.8930 | 0.8944 | 0.9543 | 0.9847 | 0.9807 | 0.9191 | 0.9831 | 0.9029 | 0.7268 | 0.8590 | 0.4625 | 0.9503 | 0.8227 | 0.6515 | 0.9884 | 0.9890 | 0.9274 | 0.9726 | 0.9791 | 0.9814 | 0.9862 | 0.1964 | 0.8063 | 0.7958 | 0.9265 | 0.9965 | 0.9482 | 0.9469 | 0.7767 | 0.9892 | 0.8689 | 0.9628 | 0.9955 | 0.9784 | 0.9503 | 0.9944 | 0.9799 | 0.9085 | 0.9463 | 0.9902 | 0.9752 | 0.9759 | 0.9765 | 0.9651 | 0.9740 | 0.9889 | 0.9778 | 0.9885 | 0.9621 | 0.9840 | 0.8823 |
112
+ | 0.0557 | 4.0 | 69592 | 0.0944 | 0.9285 | 0.9457 | 0.9370 | 0.9696 | 0.9941 | 0.9965 | 0.9190 | 0.9419 | 0.9765 | 0.9654 | 0.9188 | 0.9487 | 0.9787 | 0.9861 | 0.9424 | 0.9849 | 0.9001 | 0.7578 | 0.8875 | 0.3933 | 0.9647 | 0.8627 | 0.7004 | 0.9932 | 0.9876 | 0.9548 | 0.9736 | 0.9884 | 0.9968 | 0.9950 | 0.25 | 0.8184 | 0.7747 | 0.9206 | 0.9929 | 0.9493 | 0.9502 | 0.9075 | 0.9957 | 0.8750 | 0.9544 | 1.0 | 0.9784 | 0.9812 | 0.9944 | 0.9883 | 0.9309 | 0.9524 | 0.9935 | 0.9809 | 0.9859 | 0.9794 | 0.9624 | 0.9774 | 0.9937 | 0.9941 | 0.9852 | 0.9914 | 0.9865 | 0.9012 |
113
+ | 0.0285 | 5.0 | 86990 | 0.1285 | 0.9292 | 0.9448 | 0.9369 | 0.9693 | 0.9915 | 0.9948 | 0.9308 | 0.9352 | 0.9736 | 0.9674 | 0.9168 | 0.9561 | 0.9756 | 0.9843 | 0.9735 | 0.9849 | 0.8984 | 0.7356 | 0.8734 | 0.4161 | 0.9669 | 0.8510 | 0.7083 | 0.9945 | 0.9903 | 0.9632 | 0.9754 | 0.9926 | 0.9968 | 0.9975 | 0.3843 | 0.7983 | 0.7534 | 0.9237 | 0.9956 | 0.9510 | 0.9505 | 0.9240 | 1.0 | 0.8738 | 0.9589 | 0.9985 | 0.9784 | 0.9831 | 0.9944 | 0.9914 | 0.9480 | 0.9420 | 0.9956 | 0.9820 | 0.9860 | 0.9794 | 0.9631 | 0.9774 | 0.9937 | 0.9906 | 0.9885 | 0.9828 | 0.9946 | 0.9115 |
114
+
115
+
116
+ ### Framework versions
117
+
118
+ - Transformers 4.26.1
119
+ - Pytorch 2.0.0.post200
120
+ - Datasets 2.10.1
121
+ - Tokenizers 0.13.3