| --- |
| 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 |
|
|
