### **I hope this message finds you well. I truly appreciate your time and expertise, and I want to thank you in advance for considering my question. I’ve tried my best to articulate my thoughts clearly and comprehensively, but please let me know if there’s anything I could clarify further or improve in my explanation. I deeply value your insights and guidance. Thank you so much for your time and help! **1、Our dataset is divided according to the division on the MOSEISDK, or according to the division consistent with the CelebvHq preprocessing. ** **CelebvHq preprocessing** ``` def gen_split(root: str): videos = list(filter(lambda x: x.endswith('.mp4'), os.listdir(os.path.join(root, 'cropped')))) total_num = len(videos) with open(os.path.join(root, "train.txt"), "w") as f: for i in range(int(total_num * 0.8)): f.write(videos[i][:-4] + "\n") with open(os.path.join(root, "val.txt"), "w") as f: for i in range(int(total_num * 0.8), int(total_num * 0.9)): f.write(videos[i][:-4] + "\n") with open(os.path.join(root, "test.txt"), "w") as f: for i in range(int(total_num * 0.9), total_num): f.write(videos[i][:-4] + "\n") ``` **CMU-MOSEI Official SDK** ``` standard_train_fold=['hh04W3xXa5s', 'GdFP_p4eQX0', '4iG0ffmnCOw', '81406' standard_test_fold=['7l3BNtSE0xc', 'dZFV0lyedX4', '286943', '126872' standard_valid_fold=['188343', 'VAXhC2U9-2A', 'AxNy9TeTLq8', ``` **2、In the emotion task, we still train according to multilabels, do not need to modify the evaluation code, only need to modify num _ classes = 6. and sentiment .... this is right**？ ``` def train_mosei(args, config): if task == "emotion": # multilabel num_classes = 6 elif task == "sentiment-2": # binary num_classes = 1 elif task == "sentiment-7": # multiclass num_classes = 7 model = Classifier( num_classes, config["backbone"], True, args.marlin_ckpt, "multilabel", config["learning_rate"], args.n_gpus > 1, ) dm = MoseiDataModule( data_path, finetune, task, batch_size=args.batch_size, num_workers=args.num_workers, clip_frames=backbone_config.n_frames, temporal_sample_rate=2 ) def step(self, batch: Optional[Union[Tensor, Sequence[Tensor]]]) -> Dict[str, Tensor]: x, y = batch y_hat = self(x) if self.task == "multilabel": y_hat = y_hat.flatten() y = y.flatten() prob = y_hat.sigmoid() acc = self.acc_fn(prob, y) auc = self.auc_fn(prob, y) loss = self.loss_fn(y_hat, y.float()) elif self.task == "multiclass": prob = y_hat.softmax(dim=1) print(prob) print(y) acc = self.acc_fn(prob, y) auc = self.auc_fn(prob, y) loss = self.loss_fn(y_hat, y) elif self.task == "binary": prob = y_hat.sigmoid() acc = self.acc_fn(prob.squeeze(0), y) auc = self.auc_fn(prob.squeeze(0), y.float()) loss = self.loss_fn(y_hat.squeeze(0), y.float()) return {"loss": loss, "acc": acc, "auc": auc} ``` In the sentiment task, for this task, he is a multi-category classification task multiclass. Is there a problem in writing the code as I do