| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: distilbert-base-uncased |
| language: |
| - en |
| tags: |
| - text-classification |
| - sequence-classification |
| - youtube |
| - music-genres |
| - 7-class |
| - distilbert |
| - generated_from_trainer |
| datasets: |
| - custom-youtube-music-genres |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: text |
| results: |
| - task: |
| type: text-classification |
| name: Text Classification |
| dataset: |
| name: YouTube Music Genre Comments (custom) |
| type: custom |
| split: validation |
| metrics: |
| - type: accuracy |
| value: 1.0 |
| - type: f1 |
| value: 1.0 |
| - type: precision |
| value: 1.0 |
| - type: recall |
| value: 1.0 |
| --- |
| |
| # text |
|
|
| A DistilBERT-based **7-class text classifier** fine-tuned to predict the **music genre** associated with a YouTube comment. |
| Inputs are raw comment strings; outputs are one of seven genre labels. |
|
|
| > Base model: [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) |
|
|
| ## Results (evaluation set) |
|
|
| - **Loss:** 0.0675 |
| - **Accuracy:** 1.0 |
| - **F1:** 1.0 |
| - **Precision:** 1.0 |
| - **Recall:** 1.0 |
|
|
| ### Training curves (from `Trainer` logs) |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 1.2677 | 1.0 | 84 | 1.0653 | 0.9107 | 0.9097 | 0.9147 | 0.9107 | |
| | 0.4341 | 2.0 | 168 | 0.3179 | 0.9821 | 0.9820 | 0.9829 | 0.9821 | |
| | 0.0963 | 3.0 | 252 | 0.0865 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | 0.0568 | 4.0 | 336 | 0.0427 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | 0.0414 | 5.0 | 420 | 0.0356 | 1.0 | 1.0 | 1.0 | 1.0 | |
|
|
| > **Note:** Perfect scores may indicate an easy task, strong regularization, or possible data leakage. Validate on a held-out set and/or external data. |
|
|
| ## Model description |
|
|
| - **Architecture:** DistilBERT encoder with a linear classification head |
| - **Task:** Multi-class text classification (7 genres) |
| - **Input:** A single YouTube comment (`str`) |
| - **Output:** Predicted genre label + scores |
|
|
| ### Labels |
|
|
| Classical |
| rock |
| metal |
| electronic |
| R&B |
| pop |
| jazz |
|
|
|
|
| ## Intended uses & limitations |
|
|
| **Intended uses** |
| - Exploratory analysis of audience/genre engagement on music videos |
| - Routing comments to genre-specific moderation or analytics queues |
| - Downstream features (e.g., per-genre dashboards) |
|
|
| **Limitations** |
| - Trained on YouTube comments; may not generalize to other platforms/domains |
| - Genre labels reflect the training taxonomy; ambiguous or mixed-genre comments can be misclassified |
| - Not designed for toxicity, sentiment, or demographic inference |
|
|
| **Ethical considerations** |
| - Comments can contain personal data; ensure collection complies with platform ToS and privacy laws |
| - Avoid using predictions to profile individuals |
|
|
| ## How to use |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline |
| |
| repo_id = "scottymcgee/text-classifier" # update if different |
| tokenizer = AutoTokenizer.from_pretrained(repo_id) |
| model = AutoModelForSequenceClassification.from_pretrained(repo_id) |
| |
| pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=False) |
| pipe("this chorus is so catchy, reminds me of late 90s production") |
| |
