Instructions to use sebasyuste/MEMES4GOOD-MODELS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use sebasyuste/MEMES4GOOD-MODELS with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://sebasyuste/MEMES4GOOD-MODELS") - Notebooks
- Google Colab
- Kaggle
Memes4Good Models
This repository contains the three versions of the Memes4Good meme classifier:
- ROBUST
- BALANCED
- POWERFUL
These models were trained on Spanish memes using a multimodal architecture combining image and text.
Feel free to reach out if you have any questions or suggestions!
Links:
GitHub - Project & Notebooks – here you can find the notebooks and the full README explaining the project in detail.
Hugging Face - Dataset – all the memes used for training and validation.
GitHub - Metadata CSV – CSV containing key columns used for training and inference:
- meme_id: Unique identifier corresponding to the meme image filename.
- extracted_text: The literal text present in the meme image.
- description_es_new: A Spanish semantic description of the visual scene and context.
- harmless: The target label where 0 indicates harmful/offensive content and 1 indicates harmless content.
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