updated readme

README copy.md

@@ -1,97 +0,0 @@
----
-title: News Source Classifier
-emoji: 📰
-colorFrom: blue
-colorTo: red
-sdk: fastapi
-sdk_version: 0.95.2
-app_file: app.py
-pinned: false
-language: en
-license: mit
-tags:
-- text-classification
-- news-classification
-- LSTM
-- tensorflow
-pipeline_tag: text-classification
-widget:
-- example_title: "Crime News Headline"
-  text: "Wife of murdered Minnesota pastor hired 3 men to kill husband after affair: police"
-- example_title: "Science News Headline"
-  text: "Scientists discover breakthrough in renewable energy research"
-- example_title: "Political News Headline"
-  text: "Presidential candidates face off in heated debate over climate policies"
-model-index:
-- name: News Source Classifier
-  results:
-  - task:
-      type: text-classification
-      name: Text Classification
-    dataset:
-      name: Custom Dataset
-      type: Custom
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 0.82
----
-
-# News Source Classifier
-
-This model classifies news headlines as either Fox News or NBC News using an LSTM neural network.
-
-## Model Description
-
-- **Model Architecture**: LSTM Neural Network
-- **Input**: News headlines (text)
-- **Output**: Binary classification (Fox News vs NBC)
-- **Training Data**: Large collection of headlines from both news sources
-- **Performance**: Achieves approximately 82% accuracy on the test set
-
-## Usage
-
-You can use this model directly with a FastAPI endpoint:
-
-```python
-import requests
-
-response = requests.post(
-    "https://huggingface.co/Jiahuita/NewsSourceClassification",
-    json={"text": "Your news headline here"}
-)
-print(response.json())
-```
-
-Or use it locally:
-
-```python
-from transformers import pipeline
-
-classifier = pipeline("text-classification", model="Jiahuita/NewsSourceClassification")
-result = classifier("Your news headline here")
-print(result)
-```
-
-Example response:
-```json
-{
-    "label": "foxnews",
-    "score": 0.875
-}
-```
-
-## Limitations and Bias
-
-This model has been trained on news headlines from specific sources and time periods, which may introduce certain biases. Users should be aware of these limitations when using the model.
-
-## Training
-
-The model was trained using:
-- TensorFlow 2.13.0
-- LSTM architecture
-- Binary cross-entropy loss
-- Adam optimizer
-
-## License
-This project is licensed under the MIT License.

README.md

@@ -1,10 +1,86 @@
 ---
 title: News Classification
-emoji: 😻
+emoji: 📰
 colorFrom: green
 colorTo: indigo
 sdk: docker
+sdk_version: 0.95.2
+app_file: app.py
 pinned: false
+language: en
+license: mit
+tags:
+- text-classification
+- news-classification
+- LSTM
+- tensorflow
+pipeline_tag: text-classification
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# News Source Classifier
+
+This model classifies news headlines as either Fox News or NBC News using a deep learning LSTM (Long Short-Term Memory) neural network architecture.
+
+## Model Description
+
+### Architecture
+- Input Layer: Embedding layer (vocab_size=74,934, embedding_dim=128)
+- LSTM Layer 1: 128 units with return sequences
+- Dropout Layer 1: For regularization
+- LSTM Layer 2: 64 units
+- Dropout Layer 2: For regularization
+- Output Layer: Dense layer with 2 units (binary classification)
+
+### Technical Details
+- Total Parameters: 9,772,676 (37.28 MB)
+- Training Parameters: 9,772,674 (37.28 MB)
+- Input Shape: (41, ) - sequences of length 41
+- Performance: Achieves binary classification of news sources
+
+## Usage
+
+You can use this model through our REST API:
+
+```python
+import requests
+
+def predict_news_source(text):
+    response = requests.post(
+        "https://jiahuita-news-classification.hf.space/predict",
+        json={"text": text},
+        headers={"Content-Type": "application/json"}
+    )
+    return response.json()
+
+# Example usage
+headline = "Scientists discover breakthrough in renewable energy research"
+result = predict_news_source(headline)
+print(result)
+```
+
+Example response:
+```json
+{
+    "label": "nbc",
+    "score": 0.789
+}
+```
+
+## Limitations and Bias
+
+This model has been trained on news headlines from specific sources and time periods, which may introduce certain biases:
+- Training data is limited to two news sources
+- Headlines represent a specific time period
+- Model may be sensitive to writing style rather than just content
+
+## Training Details
+
+The model was trained using:
+- TensorFlow 2.10.0
+- Binary cross-entropy loss
+- Embedding layer for text representation
+- Dual LSTM layers with dropout for robust feature extraction
+- Dense layer with softmax activation for final classification
+
+## License
+This project is licensed under the MIT License.