meta-learning虽然目的是learning to learn，但是其问题设定和few-shot的设定在我们看来是一种父类和子类...
使用n-gram 语言模型过滤低质量内容 This process deduplicates the data at the line level, performs language identification with a fastText linear classifier to remove non-English pages and filters low quality content with an n-gram language model. In addition, we trained a linear model to classify...
Meta-Learning是一种学习方式，本质是learn to adapt/learn; Few-Shot Learning是一种任务设置，旨在样本...
然后在准备好的数据集上Meta-Model,这里我们使用 LogisticRegression 模型。 # construct meta classifier meta_model = LogisticRegression(solver='liblinear') meta_model.fit(meta_X, data_y) 最后,使用Meta-Model对保留数据集进行预测。首先通过Base-Model,输出用于构建Meta-Model的数据集,然后使用Meta-Model进行...
将图像embedding进行上采样，MLP输出结果映射到一个动态先行分类器上（dynamic linear classifier），最后...
(MultiNonLinearClassifier, self).__init__() self.tag_size = tag_size self.linear = nn.Linear(hidden_size, int(hidden_size / 2)) self.hidden2tag = nn.Linear(int(hidden_size / 2), self.tag_size) self.dropout = nn.Dropout(dropout_rate) def forward(self, input_features): features_...
config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(pooled_logits, labels) if not return_dict: output = (pooled_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput...
global_corres(corres_pred).squeeze(-1) # global_corres的类代码 class MultiNonLinearClassifier(nn...