Text to MBTI (Zero Shot - model)

😄 For fun: MBTI Translator - Infer probable MBTI from your sentence, using zero-shot NLI model.

About the project & Examples

• Please give a sentence as a input
• If possible, give a sentence which could imply your 'type' indicator

Example of input & output:

Input: "I stayed home all day"

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Output:

You are:  ISFP
Ratio {'E': 27.338588094108168, 'I': 72.66141190589182} {'N': 22.149243913056992, 'S': 77.85075608694301} {'T': 46.17274433748438, 'F': 53.82725566251562} {'P': 57.30466611213056, 'J': 42.69533388786944}

Input: "I'm making plans for my trip to Osaka. I'm so excited!"

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Output:

You are:  ESTJ
Ratio {'E': 71.53464326345417, 'I': 28.46535673654582} {'N': 35.33135528913844, 'S': 64.66864471086156} {'T': 58.70273162646018, 'F': 41.29726837353982} {'P': 46.96476087995551, 'J': 53.03523912004449}

Model and requirements

• Model: https://huggingface.co/facebook/bart-large-mnli
• Using Zero-shot text classification model
• required to setup torch & transformer library. Pleaase setup virtual environment

• 02.20 update: Highly recommend using requirement.txt to enable frontend UI using streamlit

pip install -r requirements.txt

• For torch & transformer evironment setting, please refer to: https://pytorch.org

pip install transformers

• About Zero-shot model: https://joeddav.github.io/blog/2020/05/29/ZSL.html

Module Description

• Zero-shot text classification model get texts & set of labels as input
• It outputs probability of the text is related to each of the labels