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)
