Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+---
+license: mit
+language: en
+tags:
+- sklearn
+- text-classification
+- psychology
+- mbti
+---
+
+# MBTI Personality Predictor
+
+This repository contains scikit-learn models for predicting MBTI personality types from text.
+
+## Model Details
+
+This system consists of a `TfidfVectorizer` and four separate `LogisticRegression` models, one for each of the MBTI dimensions:
+
+* **Mind:** Introversion (I) vs. Extraversion (E)
+* **Energy:** Intuition (N) vs. Sensing (S)
+* **Nature:** Thinking (T) vs. Feeling (F)
+* **Tactics:** Judging (J) vs. Perceiving (P)
+
+## Intended Use
+
+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.**
+
+## Training Data
+
+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.
+
+## Training Procedure
+
+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.
+
+### Evaluation Results
+
+Average F1-Scores on the test set:
+* **I/E Model:** Macro F1-Score: ~0.79
+* **N/S Model:** Macro F1-Score: [Add Your Score]
+* **F/T Model:** Macro F1-Score: [Add Your Score]
+* **J/P Model:** Macro F1-Score: [Add Your Score]
+
+## How to Use
+
+```python
+import joblib
+from huggingface_hub import hf_hub_download
+
+# Define the repo ID
+repo_id = "YOUR_USERNAME/mbti-personality-predictor"
+
+# Download all the model files
+vectorizer = joblib.load(hf_hub_download(repo_id=repo_id, filename="mbti_vectorizer.joblib"))
+model_ie = joblib.load(hf_hub_download(repo_id=repo_id, filename="mbti_model_ie.joblib"))
+model_ns = joblib.load(hf_hub_download(repo_id=repo_id, filename="mbti_model_ns.joblib"))
+model_ft = joblib.load(hf_hub_download(repo_id=repo_id, filename="mbti_model_ft.joblib"))
+model_jp = joblib.load(hf_hub_download(repo_id=repo_id, filename="mbti_model_jp.joblib"))
+
+# You can now use these objects for prediction...