Python 3.x 未通过spacy debug data CLI正确加载文本分类器训练数据 背景

我试图在Google Colab笔记本中的Spacy中训练一个

多类

（标签是互斥的）文本分类模型。 课程是

• 肯定的
• 否定的
• 中立的

我将训练数据格式化为指定的注释格式

下面是我做的注释示例

[.
.
["Happy #MothersDay to all ... ", {'cats': {'NEUTRAL': 1.0}}],
["Happy mothers day ..", {"cats": {"POSITIVE": 1.0}}],
.
.]

问题 当我尝试使用spacy CLI中的选项和以下命令调试数据时（在Jupyter笔记本中完成）

我得到以下输出

=========================== Data format validation ===========================
✔ Corpus is loadable

=============================== Training stats ===============================
Training pipeline: textcat
Starting with blank model 'en'
0 training docs
0 evaluation docs
✘ No evaluation docs
✔ No overlap between training and evaluation data
✘ Low number of examples to train from a blank model (0)

============================== Vocab & Vectors ==============================
ℹ 0 total words in the data (0 unique)
ℹ No word vectors present in the model

============================ Text Classification ============================
ℹ Text Classification: 0 new label(s), 0 existing label(s)
ℹ The train data contains only instances with mutually-exclusive
classes.

================================== Summary ==================================
✔ 2 checks passed
✘ 2 errors

它无法正确读取数据，但我已经检查了文件，我至少有1000多个样本，如上面所述

链接到和JSON

---

我在我的数据中找不到任何错误，有人能指出错误吗？提前谢谢

spacy debug data命令需要spacy内部JSON训练格式的数据，如下所述：

---

这里有一些例子：。同一目录中的转换脚本显示如何从JSONL格式转换，该格式与示例脚本中使用的

TRAIN\u DATA

-类型格式非常相似。

spacy debug DATA命令需要spacy内部JSON训练格式的数据，如下所述：

这里有一些例子：。同一目录中的转换脚本显示了如何从与示例脚本中使用的

TRAIN\u DATA

-类型格式非常相似的JSONL格式转换

=========================== Data format validation ===========================
✔ Corpus is loadable

=============================== Training stats ===============================
Training pipeline: textcat
Starting with blank model 'en'
0 training docs
0 evaluation docs
✘ No evaluation docs
✔ No overlap between training and evaluation data
✘ Low number of examples to train from a blank model (0)

============================== Vocab & Vectors ==============================
ℹ 0 total words in the data (0 unique)
ℹ No word vectors present in the model

============================ Text Classification ============================
ℹ Text Classification: 0 new label(s), 0 existing label(s)
ℹ The train data contains only instances with mutually-exclusive
classes.

================================== Summary ==================================
✔ 2 checks passed
✘ 2 errors