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

.gitattributes

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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.

config.json

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+{
+  "_num_labels": 1,
+  "activation_function": "gelu_new",
+  "add_cross_attention": false,
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "dtype": "float32",
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 6,
+  "n_positions": 1024,
+  "pad_token_id": null,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.10.2",
+  "use_cache": false,
+  "vocab_size": 50257
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "transformers_version": "5.10.2"
+}

model.safetensors

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}

training_args.bin

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