Update README.md

README.md

@@ -1,167 +1,144 @@
----
-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)
-
+---
+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
+
+- **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`