slangID3 DL

slangID3 DL tries to identify slang phrases via Deep Learning models created with PyTorch.

You can train a selection of classifiers, and print out a test set of phrases with the DEMO button. Or you can pass a phrase and see what type it, and the individual words are identified as. All the available models are pre-trained, but you can re-train if needed.

Gallery

Prediction

Demo

How to run slangID3 DL

1. Install Python 3.10 or newer.

2. Clone the repository with

   git clone https://github.com/m4cit/slangID3_DL.git
   

   or download the latest source code.

3. Install PyTorch.

   3.1 Either with CUDA

   • Windows:

     pip3 install torch==2.2.2 --index-url https://download.pytorch.org/whl/cu121
     

   • Linux:

     pip3 install torch==2.2.2
     

   3.2 Or without CUDA

   • Windows:

     pip3 install torch==2.2.2
     

   • Linux:

     pip3 install torch==2.2.2 --index-url https://download.pytorch.org/whl/cpu
     

4. Navigate to the slangID3_DL main directory.

   cd slangID3_DL
   

5. Install dependencies.

   pip install -r requirements.txt
   

6. Run

   python slangID3_DL.py
   

Note: It might take a while to load. Be patient.

Usage

You can predict with the included pre-trained models, and re-train if needed.

Preprocessing is the last step before training a model.

If you want to use the original dataset data.csv after some changes, or the augmented dataset augmented_data.csv, use the preprocessing function before training.

Performance

There is currently two models available:

• NeuralNet_2l_lin: Neural Network with 2 linear layers
• NeuralNet_4l_relu_lin: Neural Network with 4 linear layers and 3 ReLU layers

The best F₁ score is ~71.4% with model NeuralNet_2l_lin

Note: Score on the test set with the best parameters within 100 epochs of training, with the original training data.

Issues

• The training dataset is still too small, resulting in overfitting (after augmentation).
• Reproducibility is an issue with regard to training.

Preprocessing

The preprocessing script removes the slang tags, brackets, hyphens, and converts everything to lowercase.

Augmentation

I categorized the slang words as:

• <pex> personal expressions
  • dude, one and only, bro
• <n> singular nouns
  • shit
• <npl> plural nouns
  • crybabies
• <shnpl> shortened plural nouns
  • ppl
• <mwn> multiword nouns
  • certified vaccine freak
• <mwexn> multiword nominal expressions
  • a good one
• <en> exaggerated nouns
  • guysssss
• <eex> (exaggerated) expressions
  • hahaha, aaaaaah, lmao
• <adj> adjectives
  • retarded
• <eadj> exaggerated adjectives
  • weirdddddd
• <sha> shortened adjectives
  • on
• <shmex> shortened (multiword) expressions
  • tbh, imo
• <v> infinitive verb
  • trigger

(not all tags are available due to the small dataset)

Source of the data

Most of the phrases come from archive.org's Twitter Stream of June 6th.

Recognition of Open Source use

• PyTorch
• scikit-learn
• customtkinter
• pandas
• numpy
• tqdm