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nlp-mbti's Introduction

MBTI Personality Classification based on users' social media posts


steps

Table of Contents

  1. About The Project
  2. Getting Started
  3. Usage

About The Project

Myers Briggs Type Indicator personality Classification based on users' from social media posts.

Built With

Getting Started

To get a local copy up and running follow these simple steps.

Dependencies


  • pip
     pip install torch pandas transformers tokenizers datasets numpy

Usage


Command line Arguments

Here the user has to input the required command line arguments to run the model.

It is recommended for an intial user to run "python main.py --help" , for information on each argument.

"-m" -> Model name.

"-d" -> Dataset directory path.

"-dl" -> Next delimiter.

"-w" -> Minimum words per post.

"-tb" -> Training batch size.

"-eb" -> Eval batch size.

"-r" -> Test split ratio.

"-l" -> Learning rate.

"-wd" -> Weight decay.

"-n" -> Num classes.

"-ml"-> Multilabel classification.

"-f" -> Number of epochs.

"-mt"-> Metrics.

"--optimizer" -> Optimizer.

"--loss" -> Loss criterion.

"--train"->To train model.

"-o"-> Output directory .

"--save_interval" 

Example Cli Command


python main.py -m vinai/bertweet-base -d ./mbti_1.csv -dl "|||" -w 5 -tb 256 -eb 256 -r 0.75 -l 176e-06 -wd 1e-05 -n 4 -f 5 --optimizer ADAM --loss BCE --train -o ./ops --save_interval 2

nlp-mbti's People

Contributors

deepaknayani22 avatar mahidhar58 avatar pranshu-bahadur avatar

Stargazers

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Watchers

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nlp-mbti's Issues

V 0.1.0

Rules:

  1. test case for every module/function (unless trivial)
  2. Only widely used libraries are allowed.

Design: (Tentative)

  • controller (model-based RL?)
    - [ ] constructor
    - [ ] action space, state space (search space of hyperparams, models)
    - [ ] back tracking (worst case reset? maybe...)
    - [ ] state tracking (s, a, r)
    - [ ] optimization based on, validation acc
    - [ ] policy based on prev states
    - [ ] (Run agent for 5e?)
    - [ ] preprocess/ agent env gen strat
    - [ ] Tweet to token format scraping
    - [ ] exploding
    - [ ] tokenizer config
    - [ ] Multi-label/Single label set up
  • agent
    - [ ] constructor - (splitter)
    - [ ] state tracking/loading
    - [ ] action/behavior (train, eval)
    - [ ] Handle Imbalance
    - [ ] subset clustering
    - [ ] weighted sampling
    - [ ] weighted loss
    - [ ] Use of text generation model? ..
  • main/run
  • test (for-each module, for-each function)

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