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README.md

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----
-license: mit
----
+---
+language:
+  - id
+license: mit
+tags:
+  - text-classification
+  - bert
+  - spam-detection
+  - indonesian
+  - twitter
+  - retrained
+datasets:
+  - nahiar/spam_detection_v2
+pipeline_tag: text-classification
+inference: true
+base_model: nahiar/spam-detection-bert-v1
+model_type: bert
+library_name: transformers
+widget:
+  - text: "lacak hp hilang by no hp / imei lacak penipu/scammer/tabrak lari/terror/revengeporn sadap / hack / pulihkan akun"
+    example_title: "Spam Example"
+  - text: "Senin, 21 Juli 2025, Samapta Polsek Ngaglik melaksanakan patroli stasioner balong jalan palagan donoharjo"
+    example_title: "Ham Example"
+  - text: "Mari berkontribusi terhadap gerakan rakyat dengan membeli baju ini seharga Rp 160.000. Hubungi kami melalui WA 08977472296"
+    example_title: "Obvious Spam"
+model-index:
+  - name: spam-detection-bert
+    results:
+      - task:
+          type: text-classification
+          name: Text Classification
+        dataset:
+          name: Indonesian Spam Detection Dataset v2
+          type: nahiar/spam_detection_v2
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: 0.99
+          - name: F1 Score (Weighted)
+            type: f1
+            value: 0.99
+          - name: Precision (HAM)
+            type: precision
+            value: 0.99
+          - name: Recall (HAM)
+            type: recall
+            value: 1.00
+          - name: Precision (SPAM)
+            type: precision
+            value: 1.00
+          - name: Recall (SPAM)
+            type: recall
+            value: 0.83
+---
+
+# Indonesian Spam Detection BERT
+
+Model BERT untuk deteksi spam dalam bahasa Indonesia dengan akurasi **99%**. Model ini telah di-retrain dengan dataset yang telah diperbarui dan dilabeli ulang untuk performa yang optimal pada konten Indonesia.
+
+## Quick Start
+
+```python
+from transformers import pipeline
+
+# Cara termudah menggunakan model
+classifier = pipeline("text-classification",
+                     model="nahiar/spam-detection-bert",
+                     tokenizer="nahiar/spam-detection-bert")
+
+# Test dengan teks
+texts = [
+    "lacak hp hilang by no hp / imei lacak penipu/scammer/tabrak lari/terror/revengeporn sadap / hack / pulihkan akun",
+    "Senin, 21 Juli 2025, Samapta Polsek Ngaglik melaksanakan patroli stasioner balong jalan palagan donoharjo",
+    "Mari berkontribusi terhadap gerakan rakyat dengan membeli baju ini seharga Rp 160.000. Hubungi kami melalui WA 08977472296"
+]
+
+results = classifier(texts)
+for text, result in zip(texts, results):
+    print(f"Text: {text}")
+    print(f"Result: {result['label']} (confidence: {result['score']:.4f})")
+    print("---")
+```
+
+## Model Details
+
+- **Base Model**: nahiar/spam-detection-bert-v1 (fine-tuned from cahya/bert-base-indonesian-1.5G)
+- **Task**: Binary Text Classification (Spam vs Ham)
+- **Language**: Indonesian (Bahasa Indonesia)
+- **Model Size**: ~110M parameters
+- **Max Sequence Length**: 512 tokens
+- **Training Epochs**: 3
+- **Batch Size**: 16
+- **Learning Rate**: 2e-5
+
+## Performance
+
+| Metric               | HAM  | SPAM | Overall |
+| -------------------- | ---- | ---- | ------- |
+| Precision            | 99%  | 100% | 99%     |
+| Recall               | 100% | 83%  | 99%     |
+| F1-Score             | 99%  | 91%  | 99%     |
+| **Overall Accuracy** | -    | -    | **99%** |
+
+### Confusion Matrix
+
+- True HAM correctly predicted: 430/430 (100%)
+- True SPAM correctly predicted: 25/30 (83%)
+- False Positives (HAM predicted as SPAM): 0
+- False Negatives (SPAM predicted as HAM): 5
+
+## Dataset
+
+Model v2 ini dilatih ulang menggunakan dataset yang telah diperbarui dan dilabeli ulang secara manual:
+
+- **Dataset**: spam_re_labelled_vNew.csv
+- **Total Samples**: 460 pesan
+- **Distribution**: 430 HAM, 30 SPAM
+- **Encoding**: Latin-1
+- **Quality**: Manual re-labeling untuk akurasi yang lebih tinggi
+
+**Updated**: Januari 2025
+
+## Key Features
+
+✅ **Re-trained** dengan dataset yang telah dilabeli ulang secara manual
+✅ **High accuracy** (99%) pada deteksi spam dengan konteks Indonesia
+✅ **Better handling** untuk pesan dengan format yang kompleks
+✅ **Enhanced performance** pada teks dengan campuran formal dan informal
+✅ **Optimized** untuk konten media sosial Indonesia
+
+## Label Mapping
+
+```
+0: "HAM" (tidak spam)
+1: "SPAM" (spam)
+```
+
+## Training Process
+
+Model ini di-retrain menggunakan:
+
+- **Optimizer**: AdamW
+- **Learning Rate**: 2e-5
+- **Epochs**: 3
+- **Batch Size**: 16
+- **Max Length**: 128 tokens
+- **Train/Validation Split**: 80/20
+
+## Usage Example
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load model dan tokenizer
+tokenizer = AutoTokenizer.from_pretrained("nahiar/spam-detection-bert")
+model = AutoModelForSequenceClassification.from_pretrained("nahiar/spam-detection-bert")
+
+def predict_spam(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    outputs = model(**inputs)
+    probs = torch.softmax(outputs.logits, dim=1)
+    predicted_label = torch.argmax(probs, dim=1).item()
+    confidence = probs[0][predicted_label].item()
+    label_map = {0: "HAM", 1: "SPAM"}
+    return label_map[predicted_label], confidence
+
+# Test
+text = "Dapatkan uang dengan mudah! Klik link ini sekarang!"
+result, confidence = predict_spam(text)
+print(f"Prediksi: {result} (Confidence: {confidence:.4f})")
+```
+
+## Citation
+
+```bibtex
+@misc{nahiar_spam_detection_bert,
+  title={Indonesian Spam Detection BERT},
+  author={Raihan Hidayatullah Djunaedi},
+  year={2025},
+  url={https://huggingface.co/nahiar/spam-detection-bert}
+}
+```
+
+## Changelog
+
+### Current Version (January 2025)
+
+- Re-trained model dengan dataset yang telah dilabeli ulang secara manual
+- Enhanced handling untuk konten Indonesia yang kompleks
+- Better performance pada deteksi spam dengan konteks lokal Indonesia
+- Optimized untuk konten media sosial (Twitter, Instagram, dll)
+- Improved accuracy dengan distribusi dataset yang lebih balanced

config.json

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+{
+  "architectures": ["BertForSequenceClassification"],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "finetuning_task": "text-classification",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "ham",
+    "1": "spam"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "ham": 0,
+    "spam": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pipeline_tag": "text-classification",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "task_specific_params": {
+    "text-classification": {
+      "num_labels": 2,
+      "problem_type": "single_label_classification"
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32000
+}

model.safetensors

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+size 442499064

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "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,
+  "extra_special_tokens": {},
+  "full_tokenizer_file": null,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "pipeline_tag": "text-classification",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}