Initial upload of DistilBERT Clickbait Classifier

README.md

@@ -0,0 +1,68 @@
+# Clickbait Classifier 🎣
+
+This model is a fine-tuned version of `distilbert-base-uncased` trained to classify text (news headlines, article titles, video names) into two categories: **Clickbait** and **Non-Clickbait**. 
+
+It is optimized for filtering out sensationalist headlines and improving content recommendation algorithms.
+
+## Intended Use
+
+The primary goal of this model is to automatically detect clickbait titles to help users and platforms prioritize high-quality informative content over misleading or exaggerated headlines.
+
+- **Input:** Raw English text (headlines, titles, tweets).
+- **Return:** A binary classification label (`Clickbait` or `Non-Clickbait`) with a confidence score.
+
+## Training Data
+
+The model was fine-tuned using the `bhargavasthet/clickbait_dataset`, which contains a balanced collection of headlines explicitly labeled as clickbait (e.g., from Buzzfeed, Upworthy) and non-clickbait (e.g., from Reuters, The New York Times).
+
+## Performance Metrics
+
+*(Note: These metrics will be updated after training is complete!)*
+
+- **Accuracy:** `TBD`
+- **F1 Score:** `TBD`
+- **Precision:** `TBD`
+- **Recall:** `TBD`
+- **Evaluation Loss:** `TBD`
+
+## Training Constraints & Hyperparameters
+
+The model was trained under the following conditions:
+- **Base Architecture:** `distilbert-base-uncased` (chosen for speed and efficiency)
+- **Maximum Sequence Length:** 128
+- **Learning Rate:** 2e-05
+- **Batch Size:** 64
+- **Precision:** Mixed Precision (fp16)
+- **Optimizer Strategy:** Early Stopping (patience=3)
+- **Epochs:** 3
+
+## Usage 🚀
+
+You can easily integrate this model into your applications using the Hugging Face `transformers` library pipeline:
+
+```python
+from transformers import pipeline
+
+# Load the clickbait classifier
+classifier = pipeline("text-classification", model="ENTUM-AI/distilbert-clickbait-classifier")
+
+# Test with a sensational headline
+text_1 = "10 Bizarre Facts About Apples That Will BLOW YOUR MIND! 🍎🤯"
+result_1 = classifier(text_1)
+print(f"Text: '{text_1}'\nPrediction: {result_1}\n")
+
+# Test with a normal news headline
+text_2 = "Apple releases new quarterly earnings report showing 5% growth."
+result_2 = classifier(text_2)
+print(f"Text: '{text_2}'\nPrediction: {result_2}")
+```
+
+## Expected Output format:
+```json
+[{'label': 'Clickbait', 'score': 0.9921}]
+```
+
+## Potential Applications
+- 📰 **News Aggregators:** Filter out low-quality clickbait articles.
+- 📱 **Social Media Feeds:** Demote clickbait posts in recommendation algorithms.
+- ✉️ **Email Spam Filters:** Detect clickbait-style subject lines in promotional emails.

config.json

@@ -0,0 +1,36 @@
+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": null,
+  "dim": 768,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "Non-Clickbait",
+    "1": "Clickbait"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "Clickbait": 1,
+    "Non-Clickbait": 0
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.1.0",
+  "use_cache": false,
+  "vocab_size": 30522
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f2dad6976aef8704c135ed133c272c9c6bae7feed0e108fef05269e3abe577e8
+size 267832560

tokenizer_config.json

@@ -0,0 +1,14 @@
+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}