ParaJEPA: Joint-Embedding Predictive Architecture for Text Style Transfer
This model uses a JEPA architecture to disentangle Content and Style representations from text.
Usage
To use this model, you need to download the code files along with the weights.
import torch from para_jepa_train import ParaJEPA from config import Config
1. Initialize Model Architecture
config = Config() model = ParaJEPA( model_name=config.model_name, hidden_dim=config.hidden_dim, pred_depth=config.pred_depth )
2. Load Weights
(Download para_jepa_best_model.pt manually or via hf_hub_download)
model.load_state_dict(torch.load("para_jepa_best_model.pt")) model.eval()## Architecture
- Backbone: RoBERTa-base
- Method: Predictive Bottleneck (JEPA)
- Objective: Disentangle Style (Paraphrase) from Content (Meaning)
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