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Model: corruptedzayn/distilgpt2-spam-detector
Source: Original Platform

README.md

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+---
+language: en
+license: mit
+tags:
+- text-generation
+- spam-detection
+- distilgpt2
+datasets:
+- sms_spam
+---
+
+# DistilGPT2 Spam Detector
+
+This model fine-tunes [distilgpt2](https://huggingface.co/distilgpt2) on the
+[SMS Spam Collection](https://huggingface.co/datasets/sms_spam) dataset to classify
+text messages as **spam** or **ham**, framed as a text-generation task.
+
+## How it works
+
+The model is trained on examples formatted as:
+
+```
+Message: <text>
+Label: <ham or spam>
+```
+
+At inference time, feed it `"Message: <your text>
+Label:"` and let it generate a
+few tokens — it will produce "spam" or "ham".
+
+## Example usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("corruptedzayn/distilgpt2-spam-detector")
+model = AutoModelForCausalLM.from_pretrained("corruptedzayn/distilgpt2-spam-detector")
+
+prompt = "Message: Win a free iPhone now, click here!
+Label:"
+inputs = tokenizer(prompt, return_tensors="pt")
+output = model.generate(**inputs, max_new_tokens=5, pad_token_id=tokenizer.eos_token_id)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+## Training details
+
+- Base model: distilgpt2
+- Dataset: sms_spam (~5,500 messages, 80/20 train/test split)
+- Epochs: 3
+- Framed as causal language modeling, not a classification head
+
+## Limitations
+
+This is a small demo model trained on a small, English-only, slightly dated dataset.
+It is not intended for production spam filtering.