Python 使用Bert进行多类分类时获得不可能的预测标签（全零）

我创建了一个包含9个类别的数据集：

classDict = {"text/dokujo-tsushin": "000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001",
"text/it-life-hack": "000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010",
"text/kaden-channel": "000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100",
"text/livedoor-homme": "000000000000000000000000000000000000000000000000000000000000000000000000000000000000001000",
"text/movie-enter": "000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000",
"text/peachy": "000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000",
"text/smax": "000000000000000000000000000000000000000000000000000000000000000000000000000000000001000000",
"text/sports-watch": "000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000",
"text/topic-news": "000000000000000000000000000000000000000000000000000000000000000000000000000000000100000000"}

虽然我通过以下行将标签的数量设置为9：

model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=args.num_labels)

我得到了第十个标签，全是零。我不知道出了什么问题。有没有人遇到过同样的问题，或者有人知道问题的原因

我使用glue检查训练数据，确保没有一个示例标记为全零