Hugging Face's logo

omzn
/
facemark_detection

Text Classification
Transformers
PyTorch
Safetensors
Japanese
bert
Generated from Trainer
Model card Files Community
2
facemark_detection
File size: 3,904 Bytes 
d845a71
 
c33fd5f
d845a71
 
 
 
 
474e541
c33fd5f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d845a71
 
 
 
 
 
 
6762de3
d845a71
 
 
 
 
 
 
 
6762de3
d845a71
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
 
---
language: ja
license: cc-by-sa-4.0
tags:
- generated_from_trainer
- text-classification
metrics:
- accuracy
widget:
- text: 💪(^ω^ 🍤)
  example_title: Facemark 1
- text: (੭ु∂∀6)੭ु⁾⁾ ஐ•*¨*•.¸¸
  example_title: Facemark 2
- text: :-P
  example_title: Facemark 3
- text: (o.o)
  example_title: Facemark 4
- text: （10/7~）
  example_title: Non-facemark 1
- text: ？？＜＜「ﾆｬｱ(しゃーねぇな)」ﾌﾟｲｯ
  example_title: Non-facemark 2
- text: (0.01)
  example_title: Non-facemark 3
base_model: cl-tohoku/bert-base-japanese-whole-word-masking
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Facemark Detection

This model classifies given text into facemark(1) or not(0). 

This model is a fine-tuned version of [cl-tohoku/bert-base-japanese-whole-word-masking](https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking) on an original facemark dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1301
- Accuracy: 0.9896

## Model description

This model classifies given text into facemark(1) or not(0). 

## Intended uses & limitations

Extract a facemark-prone potion of text and apply the text to the model.
Extraction of a facemark can be done by regex but usually includes many non-facemarks.

For example, I used the following regex pattern to extract a facemark-prone text by perl.

```perl
$input_text = "facemark prne text"

my $text          = '[0-9A-Za-zぁ-ヶ一-龠]';
my $non_text      = '[^0-9A-Za-zぁ-ヶ一-龠]';
my $allow_text    = '[ovっつ゜ニノ三二]';
my $hw_kana       = '[ｦ-ﾟ]';
my $open_branket  = '[\(∩꒰（]';
my $close_branket = '[\)∩꒱）]';
my $around_face   = '(?:' . $non_text . '|' . $allow_text . ')*';
my $face          = '(?!(?:' . $text . '|' . $hw_kana . '){3,8}).{3,8}';
my $face_char     = $around_face . $open_branket . $face . $close_branket . $around_face;

my $facemark;
if ($input_text=~/($face_char)/) {
  $facemark = $1; 
}
```
Example of facemarks are:
```
  （^U^）←
  。\n\n⊂( *･ω･ )⊃
  っ(｡＞﹏＜)
  ﾀｶ( ˘ω' ) ﾔｽｩ…
  。(’↑▽↑)
  ……💰( ˘ω˘ )💰
  ーーー(*´꒳`*)！（
  …(o：∇：o)
 ！！…(；´Д｀)？
  (*´﹃ ｀*)✿
```
Examples of non-facemarks are:
```
  (3,000円)
  : (1/3)
  (@nVApO)
  （10/7~）
  ？＜＜「ﾆｬｱ(しゃーねぇな)」ﾌﾟｲｯ
  (残り 51字)
  (-0.1602)
  (25-0)
  （コーヒー飲んだ）
  （※軽トラ）
```

This model intended to use for a facemark-prone text like above.

## Training and evaluation data

Facemark data is collected manually and automatically from Twitter timeline.

* train.csv : 35591 samples (29911 facemark, 5680 non-facemark)
* test.csv : 3954 samples (3315 facemark, 639 non-facemark)

## Training procedure

```bash
python ./examples/pytorch/text-classification/run_glue.py \
   --model_name_or_path=cl-tohoku/bert-base-japanese-whole-word-masking \
   --do_train --do_eval \
   --max_seq_length=128 --per_device_train_batch_size=32 \
   --use_fast_tokenizer=False --learning_rate=2e-5 --num_train_epochs=50  \
   --output_dir=facemark_classify \
   --save_steps=1000 --save_total_limit=3 \
   --train_file=train.csv \
   --validation_file=test.csv 
```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50.0

### Training results

It achieves the following results on the evaluation set:
- Loss: 0.1301
- Accuracy: 0.9896

### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.7.1
- Tokenizers 0.13.2