Create the path if it does not exist. Args: path: path to be used Returns: Existed path

Create the path if it does not exist. Args: path: path to be used Returns: Existed path

Create the path if it does not exist. Args: path: path to be used Returns: Existed path

Create the path if it does not exist. Args: path: path to be used Returns: Existed path

Get token or None if not existent.

Load SMP-CAIL2020-Argmine train file and construct TensorDataset. Args: file_path: train file with last column as label

Resize data token embeddings matrix of the model if new_num_tokens != config.vocab_size. Take care of tying weights embeddings afterwards if

Cut words for a sentence. Args: sentence: sentence Returns: words list

Train model on train set and evaluate on train and valid set. Returns: state dict of the best model with highest valid f1 score

Train model on train set and evaluate on train and valid set. Returns: state dict of the best model with highest valid f1 score

Train model on train set and evaluate on train and valid set. Returns: state dict of the best model with highest valid f1 score

the Resnet backbone network for detection module. Args: params(dict): the super parameters for network build

Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to it with indices start

Calculate accuracy and f1 score. Args: golds: answers predicts: predictions given by model Returns: accuracy, f1 sco

Calculate accuracy and f1 score. Args: golds: answers predicts: predictions given by model Returns: accuracy, f1 sco

Calculate accuracy and f1 score. Args: golds: answers predicts: predictions given by model Returns: accuracy, f1 sco

Convert tokens to ids. Args: tokens_list: word list Returns: index list

Convert tokens to ids. Args: tokens_list: word list Returns: index list

Convert tokens to ids. Args: tokens_list: word list Returns: index list

将bgr格式的numpy的图像转换为pil :param image: 图像数组 :return: Image对象

Find the most likely tag sequence using Viterbi algorithm. Args: emissions (`~torch.Tensor`): Emission score tensor of size

Evaluate model on data loader in device. Args: model: model to be evaluate data_loader: torch.utils.data.DataLoader devic

Evaluate model on data loader in device. Args: model: model to be evaluate data_loader: torch.utils.data.DataLoader devic

Get labels on the last column from file. Args: filename: file name Returns: List[str]: label list

Load SMP-CAIL2020-Argmine train file and construct TensorDataset. Args: file_path: train file with last column as label

Load SMP-CAIL2020-Argmine train file and construct TensorDataset. Args: file_path: train file with last column as label

Load SMP-CAIL2020-Argmine train file and construct TensorDataset. Args: file_path: train file with last column as label

Load SMP-CAIL2020-Argmine train file and construct TensorDataset. Args: file_path: train file with last column as label

Load SMP-CAIL2020-Argmine train file and construct TensorDataset. Args: file_path: train file with last column as label

Load SMP-CAIL2020-Argmine train file and construct TensorDataset. Args: file_path: train file with last column as label

Load SMP-CAIL2020-Argmine train file and construct TensorDataset. Args: file_path: train file with last column as label

Load all files for SMP-CAIL2020-Argmine. Args: train_file, valid_file: files for SMP-CAIL2020-Argmine Returns:

Load all files for SMP-CAIL2020-Argmine. Args: train_file, valid_file: files for SMP-CAIL2020-Argmine Returns: