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Transformer Distribution Visualizer

A Simple Flask Web-UI Research Toolkit that allows users to input a prompt, select a Hugging Face transformer model, and specify a decoding strategy to generate text. The generated text, along with its word probabilities and top 10 next best words, can be displayed in an interactive fashion on the web page.

Demo

Getting Started

These instructions will help you set up the project on your local machine for development and testing purposes.

Prerequisites

Before you begin, make sure you have the following installed:

  • Python 3.6 or higher

  • pip

Installation

  1. Clone the repository:
git clone https://github.com/aju22/Transformer-Distribution-Visualizer.git
  1. Navigate to the project directory:
cd Transformer-Distribution-Visualizer
  1. Create a virtual environment:
python -m venv env
  1. Activate the virtual environment:
source env/bin/activate
  1. Install the required packages:
pip install -r requirements.txt

File Structure

  • models.py: contains the Transformer class which is used to load and interact with the text generation model.

  • main.py: the main file that runs the Flask app.

  • templates/index.html: the template file that contains the HTML code for the app's homepage.

Usage

To run the application, the following arguments can be passed as command line parameters:

python main.py --model <model_name> --tokenizer <tokenizer_name> --max_output_tokens <int> --gen_strat <str> --num_beams <int> --num_beam_groups <int> --penalty_alpha <float>

or simply, to run with default arguments:

python main.py

Arguments

  • model [STR, DEFAULT='gpt2']: The model id of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a user or organization name, like dbmdz/bert-base-german-cased.

  • tokenizer [STR, DEFAULT='gpt2']: The model id of a predefined tokenizer hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like "bert-base-uncased", or namespaced under a user or organization name, like "bmdz/bert-base-german-cased".

  • max_output_tokens [INT, DEFAULT=100]: The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.

  • gen_strat [str, DEFAULT='multinomial-sampling'], choices=['greedy', 'contrastive-search', 'multinomial-sampling', 'beam-search','beam-search-multinomial', 'diverse-beam-search']): Specify output token sampling method.

  • num_beams [INT, DEFAULT=1]: Number of beams for beam search. 1 means no beam search.

  • num_beam_groups [INT, DEFAULT=1]: Number of groups to divide num_beams into in order to ensure diversity among different groups of beams.

  • penalty_alpha [FLOAT, DEFAULT=0]: The values balance the model confidence and the degeneration penalty in contrastive search decoding.

Note that num_beams should be divisible by num_beam_groups for group beam search.

Execution

When the app is run, it will load the specified model and tokenizer and start a local development server. Navigate to http://localhost:5000/ in your web browser to interact with the app.

Future Work

  • Support for more decoding strategies.

  • Option to load/change models and tokenzier on the webpage itself.

  • Integration with more advanced visualization techniques to analyze the probability distributions of the transformer models.

Contributing

Contributions are welcome to this project! If you have an idea for a feature or improvement, please open an issue or send a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Demo

WebUI