library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
- emotion-classification
- midwest-emo
- math-rock
- domain-adaptation
- hybrid-corpus
metrics:
- accuracy
datasets:
- anggars/mbti-emotion
model-index:
- name: xlm-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: anggars/mbti-emotion
type: anggars/mbti-emotion
metrics:
- type: accuracy
value: 0.8796
name: Accuracy
XLM-RoBERTa Emotion (Domain-Adapted for Midwest Emo/Math Rock)
This model is a fine-tuned version of xlm-roberta-base for Emotion Classification (28 labels based on the GoEmotions taxonomy). It has been architecturally recalibrated using a Hybrid Corpus to recognize complex emotional nuances, poetic hyperboles, and depressive metaphors specifically found in Midwest Emo and Math Rock lyrical styles.
Model Description
- Model Type: XLM-RoBERTa Base (Sequence Classification Head with 28 Nodes)
- Labels: 28 Emotion Categories (e.g., sadness, grief, admiration, anger, joy)
- Dataset:
anggars/mbti-emotion(Hybrid Corpus: 112,351 synthetic narrative rows + 129 organic scraped lyrics) - Language: English & Indonesian (Multilingual)
- Training Environment: Kaggle Compute (Dual Tesla T4 GPU, fp16 Mixed Precision)
Architectural Innovations: Overcoming Domain Shift
Initial iterations of this model were trained purely on synthetic narrative data, which caused severe Domain Shift when predicting real-world music lyrics (e.g., misclassifying depressive metaphors like "drowning" or "heal this soul" as Admiration).
To mitigate this blind spot, a Hybrid Corpus Integration was executed. The model was forced to adapt to organic lyrics scraped directly from Genius.com. The integration process successfully recalibrated the latent space, forcing the model to understand poetic contexts and lyrical structures.
Note: The slight reduction in absolute accuracy (from previous baselines) and the increased validation loss are expected phenomena known as Strategic Accuracy Drop and Softmax Calibration. The aggressive weight decay (0.05) ensures the model does not overconfidently hallucinate on ambiguous lyrics, resulting in highly generalized, real-world zero-shot capabilities.
Training Results
The following results were achieved on the evaluation set during the 3-epoch training process:
| Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|
| 1.0 | 5624 | 1.0018 | 0.8422 |
| 2.0 | 11248 | 0.8367 | 0.8660 |
| 3.0 | 16872 | 0.7716 | 0.8796 |
Intended Uses & Limitations
This model is explicitly designed for the backend NLP engine of music analytics dashboards, predicting emotions directly from raw song lyrics. Limitations: Because the model has been highly adapted to read poetic, dramatic, and emotionally dense lyrical structures, its performance may degrade if deployed on standard formal documents, legal text, or casual short-form social media chats.
Training Procedure
Training Hyperparameters
To ensure stable convergence on the complex organic lyrics and prevent overfitting on the synthetic data, the following hyperparameters were utilized:
- 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)
Framework Versions
- Transformers 4.44.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3