AttributeError: 'ChatGLMTokenizer' object has no attribute 'tokenizer'. Did you mean: 'tokenize'?
时间: 2023-12-09 22:36:43 浏览: 70
这个错误提示表明ChatGLMTokenizer对象没有名为tokenizer的属性,但是它建议您使用名为tokenize的属性。这可能是由于模型适配器中的代码与您使用的LLaMATokenizer不兼容导致的。您可以尝试更改代码以使用正确的tokenizer属性,或者尝试使用其他适配器或tokenizer来解决此问题。
相关问题
AttributeError: 'ChatGLMTokenizer' object has no attribute 'tokenizer'. Did you mean: 'tokenize'?zw
这个错误提示表明在Python代码中,你尝试访问`ChatGLMTokenizer`对象的一个名为'tokenizer'的属性,但是该对象实际上没有这个属性。`tokenizer`可能是你期望存在的一个方法或者属性名,但实际上`ChatGLMTokenizer`可能提供的是`tokenize`方法而不是直接的'tokenizer'。
解决这个问题的方法通常是在调用属性或方法之前检查一下文档或者对象的帮助信息,确认`tokenizer`是否是一个正确的属性名,或者是否应该使用`tokenize()`方法来执行分词操作。确保你在使用前正确地实例化了tokenizer,并且正确调用了其提供的相应功能。
相关问题:
1. `tokenizer`和`tokenize`的区别是什么?
2. 如何在`ChatGLMTokenizer`中找到正确的分词方法?
3. 如何查看`ChatGLMTokenizer`对象的文档或帮助信息?
AttributeError: DataFrame object has no attribute append . Did you mean: _append ?
This error occurs when you try to call the `append` method on a Pandas DataFrame object, but the object does not have an `append` attribute.
One possible reason for this error is that you are trying to append a DataFrame to another DataFrame using the `append` method, but you are not using it correctly. In Pandas, the `append` method does not modify the original DataFrame, but instead it returns a new DataFrame that contains the rows from both DataFrames. Therefore, you need to assign the result of the `append` method to a new variable or to the original DataFrame, like this:
```
df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'A': [5, 6], 'B': [7, 8]})
df3 = df1.append(df2)
```
In this example, `df3` will contain the rows from both `df1` and `df2`.
If you are still getting the AttributeError, it's possible that the DataFrame object you are trying to append to does not have an `append` attribute. In this case, you can try using the `_append` attribute instead, which is a private method that is used internally by Pandas. However, be aware that using private methods may not be supported in future versions of Pandas and may cause unexpected behavior.
```
df1._append(df2)
```
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