Instructions to use amrtweg/Actora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amrtweg/Actora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amrtweg/Actora")# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("amrtweg/Actora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| tags: | |
| - social-media | |
| - NLP | |
| - prediction | |
| - marketing | |
| license: mit | |
|  | |
| # AcTora | |
| `Actora` is an advanced language model and Python library for predicting user interactions on social media, helping analyze content performance and optimize marketing campaigns. Developed by AmrTweg under RdivxeAI & MechaML for research in language models applied to marketing. | |
| ## Features | |
| - **User Interaction Prediction**: Estimate likelihood of engagement on posts. | |
| - **Marketing Campaign Analysis**: Evaluate campaign effectiveness via predicted engagement. | |
| - **Content Classification**: Classify content by engagement probability. | |
| ## Note | |
| Our model delivers accuracy ranging from high to reasonable, and hallucinations may occur, which is normal in early versions of models of this type and will be continuously improved in future releases. | |
| ## Installation | |
| ```bash | |
| ## Requirements | |
| !pip install actora | |
| !pip install torch | |
| !pip uninstall transformers | |
| !pip install transformers | |
| ``` | |
| #### Usage | |
| ```bash | |
| ## Example Usage | |
| from actora import ActoraPredictor, emoji_map | |
| # Initialize the predictor | |
| predictor = ActoraPredictor(model_name_or_path="amrtweg/Actora") | |
| # Example text | |
| text_example = "مرحبا بكم في تجربة حية لنموذج AcTora" | |
| # Get predictions | |
| results = predictor.predict(text_example) | |
| # Display results | |
| print(f"النص: {text_example}") | |
| for em, val in results.items(): | |
| print(f"{em}: {val}") | |
| ``` | |