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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
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