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Readme.md

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+---
+tags:
+- spam
+- classification
+- bert
+- pytorch
+- comment-filter
+- text-classification
+- content-moderation
+- social-media
+license: mit
+language: en
+datasets: custom
+widget:
+  - text: "Click here to win a free iPhone!"
+  - text: "Great video, thanks for sharing!"
+  - text: "Follow me for daily crypto tips 💰"
+  - text: "This tutorial saved my life, thank you!"
+  - text: "🔥 Get rich quick! Limited-time offer!"
+---
+
+# 📦 Spam Detector — `vibehq/spam-detector`
+
+A BERT-based spam classifier fine-tuned to detect **spam and promotional content** in social media-style comments. Trained on real-world-like comment data including giveaways, scams, promotions, and genuine engagement.
+
+Perfect for content moderation on platforms like:
+- YouTube
+- Instagram
+- Discord
+- Reddit
+- Facebook
+- Forums or blogs
+
+---
+
+## 🚀 How to Use
+
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model = BertForSequenceClassification.from_pretrained("vibehq/spam-detector")
+tokenizer = BertTokenizer.from_pretrained("vibehq/spam-detector")
+
+def predict_spam(comment):
+    inputs = tokenizer(comment, return_tensors='pt', max_length=128, padding='max_length', truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    prediction = torch.argmax(outputs.logits, dim=-1).item()
+    return "Spam" if prediction == 1 else "Non-Spam"
+
+# Example
+print(predict_spam("Subscribe to my channel for more giveaways!"))

config.json

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+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2dd8290f439effc74bf3a741c818e884dfda04ab05caf189bdd8dc0aa02e45cb
+size 437958648

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}