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README.md

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+# DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis, AAAI 2025.
+
+### [Arxiv Paper](https://arxiv.org/abs/2412.12225)
+
+## Main Contributions
+
+Our main contributions can be summarized as follows:
+
+- **Proposed Framework:** In this study, we propose a Disentangled-Language-Focused (DLF) multimodal representation learning framework to promote MSA tasks. The framework follows a structured pipeline: feature extraction, disentanglement, enhancement, fusion, and prediction.
+- **Language-Focused Attractor (LFA):** We develop the LFA to fully harness the potential of the dominant language modality within the modality-specific space. The LFA exploits the language-guided multimodal cross-attention mechanisms to achieve a targeted feature enhancement ($X$->Language).
+- **Hierarchical Predictions:** We devise hierarchical predictions to leverage the pre-fused and post-fused features, improving the total MSA accuracy. 
+
+
+## Usage
+
+### Prerequisites
+- Python 3.9.13
+- PyTorch 1.13.0
+- CUDA 11.7
+
+### Installation
+- Create a conda environment. Please make sure you have installed conda before.
+```
+conda create -n DLF python==3.9.13
+```
+- Activate the built DLF environment.
+```
+conda activate DLF
+```
+- Install Pytorch with CUDA
+```
+pip install torch==1.13.0+cu117 torchvision==0.14.0+cu117 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu117
+```
+- Clone this repo.
+```
+git clone https://github.com/pwang322/DLF.git
+```
+- Install the necessary packages.
+```
+cd DLF
+pip install -r requirements.txt
+```
+
+### Datasets
+Data files (containing processed MOSI, MOSEI datasets) can be downloaded from [here](https://drive.google.com/drive/folders/1BBadVSptOe4h8TWchkhWZRLJw8YG_aEi?usp=sharing). 
+You can first build and then put the downloaded datasets into `./dataset` directory and revise the path in `./config/config.json`. For example, if the processed the MOSI dataset is located in `./dataset/MOSI/aligned_50.pkl`. Please make sure "dataset_root_dir": "./dataset" and "featurePath": "MOSI/aligned_50.pkl".
+Please note that the meta information and the raw data are not available due to the privacy of YouTube content creators. For more details, please follow the [official website](https://github.com/ecfm/CMU-MultimodalSDK) of these datasets.
+
+### Run the Codes
+- Training
+
+You can first set the training dataset name in `./train.py` as "mosei" or "mosi", and then run:
+```
+python3 train.py
+```
+By default, the trained model will be saved in `./pt` directory. You can change this in `train.py`.
+
+- Testing
+
+You can first set the testing dataset name in `./test.py` as "mosei" or "mosi", and then test the trained model:
+```
+python3 test.py
+```
+We also provide pre-trained models for testing. ([Google drive](https://drive.google.com/drive/folders/1GgCfC1ITAnRRw6RScGc7c2YUg5Ccbdba?usp=sharing))
+
+#### 🤗 Option 2: Load Pretrained Models from Hugging Face Hub
+
+We also release pre-trained models on Hugging Face for direct use:
+
+```
+from trains.singleTask.model.DLF import DLF
+model = DLF.from_pretrained("Peter180/DLF_mosei")  # or "Peter180/DLF_mosi"
+```
+
+### Citation
+If you find the code and our idea helpful in your research or work, please cite the following paper.
+
+```
+@article{wang2024dlf,
+  title={DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis},
+  author={Wang, Pan and Zhou, Qiang and Wu, Yawen and Chen, Tianlong and Hu, Jingtong},
+  journal={arXiv preprint arXiv:2412.12225},
+  year={2024}
+}
+```
+
+
+
+