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README.md

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+---
+title: ACE Classifier Doc2Vec
+emoji: 🤖
+colorFrom: blue
+colorTo: green
+sdk: custom
+app_port: 8080
+---
+
+# ACE Content Attribution Classifier (Doc2Vec)
+
+This model classifies content as either "attributed" or "unattributed" using Doc2Vec embeddings and machine learning classifiers.
+
+## Model Details
+
+- **Training Date**: 2025_09_17
+- **Architecture**: Doc2Vec + Machine Learning Classifier
+- **Task**: Binary text classification
+- **Classes**: attributed, unattributed
+
+## Usage
+
+### API Format
+
+Send POST requests to the inference endpoint:
+
+```json
+{
+  "inputs": {
+    "content": "Your content text here",
+    "meta_description": "Optional meta description"
+  }
+}
+```
+
+### Response Format
+
+```json
+[
+  {
+    "label": "attributed",
+    "score": 0.75
+  },
+  {
+    "label": "unattributed",
+    "score": 0.25
+  }
+]
+```
+
+### Python Example
+
+```python
+import requests
+
+api_url = "https://api-inference.huggingface.co/models/athenahq/ACE-classifier-doc2vec"
+headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
+
+data = {
+    "inputs": {
+        "content": "Machine learning models for content attribution analysis",
+        "meta_description": "A comprehensive guide to ML-based content classification"
+    }
+}
+
+response = requests.post(api_url, headers=headers, json=data)
+result = response.json()
+print(result)
+```
+
+### cURL Example
+
+```bash
+curl -X POST \
+  https://api-inference.huggingface.co/models/athenahq/ACE-classifier-doc2vec \
+  -H "Authorization: Bearer YOUR_HF_TOKEN" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "inputs": {
+      "content": "Your content text here",
+      "meta_description": "Optional meta description"
+    }
+  }'
+```
+
+## Model Performance
+
+The model uses the best-performing combination from extensive hyperparameter tuning across multiple Doc2Vec configurations and classifiers.
+
+## Files
+
+- `handler.py`: Custom inference handler
+- `model_summary.json`: Overview of all trained models
+- `rank_1_*_classifier.pkl`: Best performing classifier
+- `rank_1_*_doc2vec.model`: Best performing Doc2Vec model
+- `rank_1_*_metadata.json`: Model metadata and configuration
+
+## Technical Details
+
+- **Doc2Vec**: Uses both PV-DM and PV-DBOW algorithms
+- **Preprocessing**: Text cleaning, tokenization, and filtering
+- **Classifiers**: Random Forest, SVM, Logistic Regression, Neural Networks
+- **Evaluation**: Comprehensive accuracy and confidence analysis