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# 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

{"language": "ja", "license": "cc-by-sa-4.0", "tags": ["generated_from_trainer", "text-classification"], "metrics": ["accuracy"], "widget": [{"text": "\ud83d\udcaa(^\u03c9^ \ud83c\udf64)", "example_title": "Facemark 1"}, {"text": "(\u0a6d\u0941\u2202\u22006)\u0a6d\u0941\u207e\u207e \u0b90\u2022*\u00a8*\u2022.\u00b8\u00b8", "example_title": "Facemark 2"}, {"text": ":-P", "example_title": "Facemark 3"}, {"text": "(o.o)", "example_title": "Facemark 4"}, {"text": "\uff0810/7~\uff09", "example_title": "Non-facemark 1"}, {"text": "\uff1f\uff1f\uff1c\uff1c\u300c\uff86\uff6c\uff71(\u3057\u3083\u30fc\u306d\u3047\u306a)\u300d\uff8c\uff9f\uff72\uff6f", "example_title": "Non-facemark 2"}, {"text": "(0.01)", "example_title": "Non-facemark 3"}]}
transformers pytorch safetensors bert text-classification generated_from_trainer ja license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 




# 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 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.


Example of facemarks are:

Examples of non-facemarks are:


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.

* URL : 35591 samples (29911 facemark, 5680 non-facemark)
* URL : 3954 samples (3315 facemark, 639 non-facemark)

## Training procedure



### 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

# 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 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.


Example of facemarks are:

Examples of non-facemarks are:


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.

* URL : 35591 samples (29911 facemark, 5680 non-facemark)
* URL : 3954 samples (3315 facemark, 639 non-facemark) ## Training procedure ### 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
TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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 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.


Example of facemarks are:

Examples of non-facemarks are:


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.

* URL : 35591 samples (29911 facemark, 5680 non-facemark)
* URL : 3954 samples (3315 facemark, 639 non-facemark) ## Training procedure ### 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
TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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 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.


Example of facemarks are:

Examples of non-facemarks are:


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.

* URL : 35591 samples (29911 facemark, 5680 non-facemark)
* URL : 3954 samples (3315 facemark, 639 non-facemark)## Training procedure### 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