| --- |
| language: en |
| license: other |
| library_name: transformers |
| tags: |
| - text-classification |
| - distilbert |
| - podcast |
| - ad-detection |
| - skipr |
| datasets: |
| - custom |
| base_model: distilbert-base-uncased |
| pipeline_tag: text-classification |
| --- |
| |
| # Skipr Ad Classifier |
|
|
| Fine-tuned DistilBERT model that detects sponsor/ad segments in YouTube podcast transcript text. |
|
|
| **Also available:** [dkayaaaa/ad-classifier-quantised](https://huggingface.co/dkayaaaa/ad-classifier-quantised) — INT8 ONNX version (~64 MB) for faster, lighter inference with ONNX Runtime. |
|
|
| ## Model description |
|
|
| This model classifies a short transcript window as either an ad/sponsor segment or normal podcast content. It was trained as part of the [Skipr](https://github.com/YOUR_ORG/skippy-model-training) pipeline for skipping sponsor segments in YouTube podcasts. |
|
|
| - **Architecture:** `distilbert-base-uncased` |
| - **Task:** Binary sequence classification |
| - **Labels:** |
| - `0` — not an ad segment |
| - `1` — ad/sponsor segment |
| - **Max sequence length:** 512 tokens |
| - **Training:** 3 epochs, fine-tuned from `distilbert-base-uncased` |
|
|
| ## Intended use |
|
|
| Use this model to classify transcript windows (typically ~20 caption snippets) as ad vs non-ad content. It is designed for use in the Skipr browser extension and related inference services. |
|
|
| **Out of scope:** |
| - General sentiment or topic classification |
| - Non-English text (trained on English podcast transcripts) |
| - Full-video classification without segmentation |
|
|
| ## Training data |
|
|
| ## Training data |
|
|
| The model was trained on a mix of real and synthetic transcript windows: |
|
|
| - **Base set (~800 samples):** weak-labeled YouTube podcast segments |
| - Positive: segments matching sponsor keywords/brands |
| - Negative: normal podcast content |
| - **Augmented set (~1,200 samples):** synthetic variants generated with Llama 8B via Ollama, preserving the original label. Synthetic data generated through strategies; paraphrase, new scenario, style shift, fragment, vocabulary shift |
|
|
| Original labels are heuristic — the model learns from keyword-labeled examples. Synthetic data increases linguistic diversity but inherits the same label assumptions. |
|
|
| ## Usage |
|
|
| ### Transformers (Python) |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import torch |
| |
| repo = "dkayaaaa/ad-classifier" |
| tokenizer = AutoTokenizer.from_pretrained(repo) |
| model = AutoModelForSequenceClassification.from_pretrained(repo) |
| |
| text = "this episode is brought to you by our friends at..." |
| inputs = tokenizer( |
| text, |
| return_tensors="pt", |
| padding="max_length", |
| truncation=True, |
| max_length=512, |
| ) |
| |
| with torch.no_grad(): |
| logits = model(**inputs).logits |
| prediction = logits.argmax(dim=-1).item() |
| |
| print("ad" if prediction == 1 else "not ad") |
