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README.md

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 ---
-license: cc-by-4.0
+library_name: transformers
+tags: [fake-news-detection, NLP, classification, transformers, DistilBERT]
 ---
+
+# Model Card for Fake News Detection Model
+
+## Model Summary
+
+This is a fine-tuned DistilBERT model for **fake news detection**. It classifies news articles as either **real** or **fake** based on textual content. The model has been trained on a labeled dataset consisting of true and false news articles collected from various sources.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Dhruv Pal
+- **Finetuned from:** `distilbert-base-uncased`
+- **Language:** English
+- **Model type:** Transformer-based text classification model
+- **License:** MIT
+- **Intended Use:** Fake news detection on social media and news websites
+
+### Model Sources
+
+- **Repository:** [Hugging Face Model Hub](https://huggingface.co/your-model-id)
+- **Paper (if applicable):** N/A
+- **Demo (if applicable):** N/A
+
+## Uses
+
+### Direct Use
+
+- This model can be used to detect whether a given news article is **real or fake**.
+- It can be integrated into fact-checking platforms, misinformation detection systems, and social media moderation tools.
+
+### Downstream Use
+
+- Can be further fine-tuned on domain-specific fake news datasets.
+- Useful for media companies, journalists, and researchers studying misinformation.
+
+### Out-of-Scope Use
+
+- This model is **not designed for generating news content**.
+- It may not work well for languages other than English.
+- Not suitable for fact-checking complex claims requiring external knowledge.
+
+## Bias, Risks, and Limitations
+
+### Risks
+
+- The model may be biased towards certain topics, sources, or writing styles based on the dataset used for training.
+- There is a possibility of **false positives (real news misclassified as fake)** or **false negatives (fake news classified as real)**.
+- Model performance can degrade on out-of-distribution samples.
+
+### Recommendations
+
+- Users should **not rely solely** on this model for determining truthfulness.
+- It is recommended to **use human verification** and **cross-check information** from multiple sources.
+
+## How to Use the Model
+
+You can load the model using `transformers` and use it for inference as shown below:
+
+```python
+from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
+import torch
+
+tokenizer = DistilBertTokenizerFast.from_pretrained("your-model-id")
+model = DistilBertForSequenceClassification.from_pretrained("your-model-id")
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    return "Fake News" if torch.argmax(probs) == 1 else "Real News"
+
+text = "Breaking: Scientists discover a new element!"
+print(predict(text))
+```
+
+## Training Details
+
+### Training Data
+
+The model was trained on a dataset consisting of **news articles labeled as real or fake**. The dataset includes information from reputable sources and misinformation websites.
+
+### Training Procedure
+
+- **Preprocessing:**
+  - Tokenization using `DistilBertTokenizerFast`
+  - Removal of stop words and punctuation
+  - Converting text to lowercase
+
+- **Training Configuration:**
+  - **Model:** `distilbert-base-uncased`
+  - **Optimizer:** AdamW
+  - **Batch size:** 16
+  - **Epochs:** 3
+  - **Learning rate:** 2e-5
+
+### Compute Resources
+
+- **Hardware:** NVIDIA Tesla T4 (Google Colab)
+- **Training Time:** ~2 hours
+
+## Evaluation
+
+### Testing Data
+
+- The model was evaluated on a held-out test set of **10,000 news articles**.
+
+### Metrics
+
+- **Accuracy:** 92%
+- **F1 Score:** 90%
+- **Precision:** 91%
+- **Recall:** 89%
+
+### Results
+
+| Metric   | Score |
+|----------|-------|
+| Accuracy | 92%   |
+| F1 Score | 90%   |
+| Precision | 91%  |
+| Recall   | 89%   |
+
+## Environmental Impact
+
+- **Hardware Used:** NVIDIA Tesla T4
+- **Total Compute Time:** ~2 hours
+- **Carbon Emissions:** Estimated using the [ML Impact Calculator](https://mlco2.github.io/impact#compute)
+
+## Technical Specifications
+
+### Model Architecture
+
+- The model is based on **DistilBERT**, a lightweight transformer architecture that reduces computation while retaining accuracy.
+
+### Dependencies
+
+- `transformers`
+- `torch`
+- `datasets`
+- `scikit-learn`
+
+## Citation
+
+If you use this model, please cite it as:
+
+```bibtex
+@misc{DhruvPal2025FakeNewsDetection,
+  title={Fake News Detection with DistilBERT},
+  author={Dhruv Pal},
+  year={2025},
+  howpublished={\url{https://huggingface.co/your-model-id}}
+}
+```
+
+## Contact
+
+For any queries, feel free to reach out:
+- **Author:** Dhruv Pal
+- **Email:** dhruv416pal@gmail.com
+- **GitHub:** [dhruvpal05](https://github.com/dhruvpal05)
+- **LinkedIn:** [idhruvpal](https://linkedin.com/in/idhruvpal)
+

config.json

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+{
+  "_name_or_path": "./fakenews_bert_model",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "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,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3",
+  "vocab_size": 30522
+}

model.safetensors

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+size 267832560

special_tokens_map.json

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+    "normalized": false,
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+    "single_word": false
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+    "lstrip": false,
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+  },
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+    "lstrip": false,
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+    "single_word": false
+  }
+}

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
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+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}