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license: apache-2.0 |
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license: mit |
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language: en |
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tags: |
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- sklearn |
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- text-classification |
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- psychology |
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- mbti |
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--- |
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# MBTI Personality Predictor |
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This repository contains scikit-learn models for predicting MBTI personality types from text. |
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## Model Details |
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This system consists of a `TfidfVectorizer` and four separate `LogisticRegression` models, one for each of the MBTI dimensions: |
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* **Mind:** Introversion (I) vs. Extraversion (E) |
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* **Energy:** Intuition (N) vs. Sensing (S) |
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* **Nature:** Thinking (T) vs. Feeling (F) |
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* **Tactics:** Judging (J) vs. Perceiving (P) |
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## Intended Use |
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These models are intended for educational purposes and to demonstrate building an NLP classification system. They can be used to predict an MBTI type from a block of English text. **This is not a clinical or diagnostic tool.** |
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## Training Data |
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The models were trained on the [Myers-Briggs Personality Type Dataset](https://www.kaggle.com/datasets/datasnaek/mbti-type) from Kaggle, which contains over 8,600 entries of text from social media forums. |
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## Training Procedure |
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Text was cleaned by removing URLs and punctuation, lemmatizing, and removing stopwords. The text was then vectorized using TF-IDF (`max_features=5000`, `ngram_range=(1, 2)`). Each `LogisticRegression` model was trained with `class_weight='balanced'` to counteract the natural imbalance in the dataset. |
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### Evaluation Results |
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Average F1-Scores on the test set: |
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* **I/E Model:** Macro F1-Score: ~0.79 |
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* **N/S Model:** Macro F1-Score: [Add Your Score] |
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* **F/T Model:** Macro F1-Score: [Add Your Score] |
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* **J/P Model:** Macro F1-Score: [Add Your Score] |
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## How to Use |
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```python |
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import joblib |
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from huggingface_hub import hf_hub_download |
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# Define the repo ID |
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repo_id = "YOUR_USERNAME/mbti-personality-predictor" |
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# Download all the model files |
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vectorizer = joblib.load(hf_hub_download(repo_id=repo_id, filename="mbti_vectorizer.joblib")) |
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model_ie = joblib.load(hf_hub_download(repo_id=repo_id, filename="mbti_model_ie.joblib")) |
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model_ns = joblib.load(hf_hub_download(repo_id=repo_id, filename="mbti_model_ns.joblib")) |
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model_ft = joblib.load(hf_hub_download(repo_id=repo_id, filename="mbti_model_ft.joblib")) |
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model_jp = joblib.load(hf_hub_download(repo_id=repo_id, filename="mbti_model_jp.joblib")) |
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# You can now use these objects for prediction... |
