DistilBERT-based Music Era Classifier
This repository contains a fine-tuned text classification model based on distilbert-base-uncased. The model is designed to classify short text descriptions of eras in classical music into one of four historical musical eras: 0, 1, 2, and 3.
Model Architecture & Training
The model was trained using the Hugging Face Trainer API. It utilizes a distilbert-base-uncased pre-trained model with a classification head on top.
-
Tokenizer: AutoTokenizer.from_pretrained("distilbert-base-uncased")
-
Model: AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
-
Training Arguments: Learning Rate: 2×10−5
-
Epochs: 5
-
Batch Size: 8
-
Evaluation Strategy: Per epoch
-
Metric: accuracy
-
Optimizer: AdamW
music-text-distilbert-predictor
This model is a fine-tuned version of
distilbert-base-uncased on the samder03/2025-24679-text-dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0495
- Accuracy: 1.0
- F1: 1.0
- Precision: 1.0
- Recall: 1.0
Limitations
This model's primary limitations are:
Numerical Labels: The model outputs a numerical label (0, 1, 2, or 3). An external lookup table is required to map these numbers to their corresponding musical era names.
Language & Casing: As the model is based on distilbert-base-uncased, it is designed for English-language text and does not differentiate between uppercase and lowercase letters. It will not work for other languages.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|
| 0.6387 | 1.0 | 80 | 0.5111 | 0.9563 | 0.9562 | 0.9574 | 0.9563 |
| 0.0833 | 2.0 | 160 | 0.1052 | 0.9812 | 0.9812 | 0.9814 | 0.9812 |
| 0.0221 | 3.0 | 240 | 0.0585 | 0.9812 | 0.9812 | 0.9814 | 0.9812 |
| 0.0122 | 4.0 | 320 | 0.0629 | 0.9812 | 0.9812 | 0.9814 | 0.9812 |
| 0.011 | 5.0 | 400 | 0.0614 | 0.9812 | 0.9812 | 0.9814 | 0.9812 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
Potential Errors
There could be a problem with dataleakage because the accuracy is at 100%
Because the model has already been trained on the augmented data, which is just a
derivative of the original data, the original dataset isn't a true holdout set.
The model is essentially being tested on data that it has already seen and, in some cases, memorized.
