熊猫：将嵌入对象转换为浮点型

Question

我有一个movie数据表，其中有几列带有文本/类别变量的列。我使用sentenceTransformer

将这些文本转换为相应的嵌入

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('bert-base-nli-mean-tokens')
df['movieName_embed'] = df.apply(lambda row : model.encode(row['movieName']), axis = 1)
df['usertags_embed'] = df.apply(lambda row : model.encode(row['usertags']), axis = 1)

经过这种嵌入插入和其他几种编码技术之后，dataframe看起来像这样。

然后我创建特征的目标如下：

X = df[['movieName_embed', 'usertags_embed', 'rating']]
y = df[['genre_fe']]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)

movieName_embed和usertags_embed列为type: list of list of numbers，不适合在xgboost中进行训练。因此，当我执行xgboost.XGBRegressor.fit(X_train,y_train)时，我将遇到错误-

ValueError: DataFrame.dtypes for data must be int, float or bool.
                Did not expect the data types in fields movieName_embed, usertags_embed

那么我该如何转换嵌入使其适合训练呢？