| --- |
| 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 |
|
|
