README.md

metadata

library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
  - generated_from_trainer
  - mbti
  - midwest-emo
  - math-rock
  - personality-detection
  - domain-adaptation
  - hybrid-corpus
metrics:
  - accuracy
  - f1
datasets:
  - anggars/mbti-emotion
language:
  - en
  - id
  - ja
  - ko
  - es
  - fr
  - de
  - zh
  - pt
  - ru
  - it
model-index:
  - name: xlm-mbti
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: anggars/mbti-emotion
          type: anggars/mbti-emotion
        metrics:
          - type: accuracy
            value: 0.7744
            name: Accuracy
          - type: f1
            value: 0.7746
            name: F1 Macro

XLM-RoBERTa MBTI (Domain-Adapted for Midwest Emo/Math Rock)

This model is a fine-tuned version of xlm-roberta-base for MBTI Personality Classification (16 types). It has been architecturally recalibrated using a Hybrid Corpus to extract underlying cognitive functions (Thinking, Feeling, Intuition, Sensing) from poetic hyperboles and complex metaphors, specifically within the context of Midwest Emo and Math Rock lyrics.

Model Description

• Model Type: XLM-RoBERTa Base (Sequence Classification Head with 16 Nodes)
• Labels: 16 MBTI Personality Types
• Dataset: anggars/mbti-emotion (Hybrid Corpus: 120,060 total rows. Stratified split: 96,048 train / 24,012 eval)
• Language(s): English & Indonesian (Multilingual)
• License: MIT
• Training Environment: Kaggle Compute (Dual NVIDIA Tesla T4 GPU, fp16 Mixed Precision)

Architectural Innovations

Predicting 16 distinct personality classes purely from unstructured text is a highly complex NLP task (random guessing yields only a 6.25% baseline). Achieving an accuracy of ~77.44% indicates strong pattern recognition.

In this iteration, the model underwent Domain Adaptation via a Hybrid Corpus. By forcing the architecture to learn from actual organic lyrics scraped from Genius.com combined with synthetically balanced multi-language data, the model developed zero-shot capabilities for real-world musical analysis. The implementation of aggressive weight decay (0.05) prevents overconfident hallucination and yields highly organic, generalized predictions suitable for production environments.

Training Results

The following results were achieved on the evaluation set (24,012 rows) during the 3-epoch training process:

Epoch	Training Loss	Validation Loss	Accuracy	F1 Macro
1.0	0.7951	0.8099	0.7150	0.7125
2.0	0.6333	0.6736	0.7615	0.7622
3.0	0.4887	0.6578	0.7744	0.7746

Intended Uses & Limitations

This model is intended for academic research in the field of Natural Language Processing (NLP) and psychology, specifically functioning as the backend engine for music analytics dashboards. Limitations: Personality cognitive functions are highly complex. The model provides predictions based strictly on linguistic and lyrical patterns in specific musical subgenres. It operates on poetic heuristics and must not be utilized as a definitive psychological diagnostic tool for human subjects.

Training Procedure

Training Hyperparameters

• learning_rate: 1.5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• weight_decay: 0.05
• optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3
• mixed_precision_training: Native AMP (fp16)