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custom-classifier-demo

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metadata
license: apache-2.0
tags:
  - summarization
  - custom-model
  - pegasus
  - seq2seq
  - huggingface
  - transformers
library_name: transformers
inference: false
model-index:
  - name: Custom Pegasus Summarizer
    results: []

🦅 Custom Pegasus Summarizer

This model is a custom-wrapped version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) built for summarization tasks. It's implemented using Hugging Face's `transformers` library and wrapped with a custom model class for educational and experimental flexibility.

✅ It supports:

  • Easy fine-tuning and extension (e.g., adapters, prompt tuning)
  • Drop-in replacement for the original model
  • Hugging Face Hub compatibility
  • Works with `AutoTokenizer` and `CustomSeq2SeqModel`

🧠 Model Architecture

  • Base: google/pegasus-xsum
  • Wrapper: CustomSeq2SeqModel (inherits from PreTrainedModel)
  • Tokenizer: AutoTokenizer from the same repo
  • Configuration: CustomSeq2SeqConfig (inherits from PretrainedConfig)

🧪 Training Details

  • Dataset: xsum (500-sample subset)
  • Task: Abstractive Summarization
  • Epochs: 1
  • Batch Size: 4
  • Learning Rate: 2e-5
  • Training Framework: Hugging Face Trainer

💡 Usage Example

```python from transformers import AutoTokenizer from model import CustomSeq2SeqModel # Your custom wrapper

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-pegasus-summarizer") model = CustomSeq2SeqModel.from_pretrained("your-username/custom-pegasus-summarizer")

text = "summarize: The Apollo program was a major milestone in space exploration..." inputs = tokenizer(text, return_tensors="pt", truncation=True) summary_ids = model.generate(**inputs, max_length=60) print(tokenizer.decode(summary_ids[0], skip_special_tokens=True)) ```

🎛 Live Demos

You can try this model interactively on Hugging Face Spaces:

📦 Files Included

  • `config.json` – Model configuration (used by `from_pretrained`)
  • `pytorch_model.bin` – Fine-tuned model weights
  • `tokenizer_config.json` – Tokenizer settings
  • `vocab.json` / `merges.txt` – Tokenizer vocab (depends on tokenizer type)
  • `special_tokens_map.json` – Special tokens for summarization
  • `README.md` – This model card
  • `model.py` – (if included) Your `CustomSeq2SeqModel` class

📜 License

Apache 2.0 — same license as the original `pegasus-xsum`.

🙏 Acknowledgments

  • Hugging Face for `transformers`, `datasets`, and `hub`
  • Authors of PEGASUS
  • Educational/Research communities building custom NLP models