don't understand this line

[lpschaub]

hello,
I don't get this line :

x_test = test[125000:140000]
it gives me : x text = Empty DataFrame
Columns: [000idbert22889, Hola saber si chaqueta repelente agua referencia, 0.0, 0.0.1, 0.0.2, 0.0.3, 0.0.4, 0.0.5, 0.0.6, 0.0.7, 0.0.8, 0.0.9, 0.0.10, 0.0.11, 0.0.12, 0.0.13, 0.0.14, 0.0.15, 0.0.16, 0.0.17, 0.0.18, 0.0.19, 21.0, 0.0.20, 0.0.21, 0.0.22, 0.0.23, 0.0.24, 0.0.25, 0.0.26, 0.0.27, 0.0.28, 0.0.29, 0.0.30, 0.0.31, 0.0.32, 0.0.33, 0.0.34, 0.0.35, 0.0.36, 0.0.37, 0.0.38, 0.0.39, 0.0.40, 0.0.41, 0.0.42, 0.0.43, 0.0.44, 0.0.45, 0.0.46, 0.0.47, 0.0.48, 0.0.49, 0.0.50, 0.0.51, 0.0.52, 0.0.53, 0.0.54, 0.0.55, 0.0.56, 0.0.57, 0.0.58, 0.0.59, 0.0.60, 0.0.61, 0.0.62, 0.0.63, 0.0.64, 0.0.65, 0.0.66, 0.0.67, 0.0.68, 0.0.69, 0.0.70, 0.0.71, 0.0.72, 0.0.73, 0.0.74, 0.0.75, 0.0.76, 0.0.77, 0.0.78, 0.0.79, 0.0.80, 0.0.81, 0.0.82, 0.0.83, 0.0.84, 0.0.85, 0.0.86, 0.0.87, 0.0.88, 0.0.89, 0.0.90, 0.0.91, 0.0.92, 0.0.93, 0.0.94, 0.0.95, 0.0.96, ...]
Index: []

when i print it
and the error : Traceback (most recent call last):
File "Bert.py", line 904, in
dff.columns = BERTtest.LABEL_COLUMNS
File "/home/tf/.local/lib/python3.6/site-packages/pandas/core/generic.py", line 5080, in setattr
return object.setattr(self, name, value)
File "pandas/_libs/properties.pyx", line 69, in pandas._libs.properties.AxisProperty.set
File "/home/tf/.local/lib/python3.6/site-packages/pandas/core/generic.py", line 638, in _set_axis
self._data.set_axis(axis, labels)
File "/home/tf/.local/lib/python3.6/site-packages/pandas/core/internals/managers.py", line 155, in set_axis
'values have {new} elements'.format(old=old_len, new=new_len))
ValueError: Length mismatch: Expected axis has 0 elements, new values have 114 elements
Any idea ?

thanks

[BreakBB]

You can fix the latter issue by changing for (i, prediction) in enumerate(predictions): in "create_output" to for prediction in predictions:. The index "i" isn't used.

And about the test splitting: I have no clue where @javaidnabi31 got those numbers, but I changed the splitting of the data like this:

split_1 = int(0.8 * len(train))
split_2 = int(0.9 * len(train))

x_train = train[:split_1]
x_eval = train[split_1:split_2]
x_test = train[split_2:]

This will split the dataset of "train" into 80% train, 10% eval and 10% test data.

[javaidnabi31]

Actually 10k i used just to tun a quicker run. Otherwise you can use x_train= train[:SIZE_TRAIN]

[javaidnabi31]

I ran it on Kaggle https://www.kaggle.com/javaidnabi/toxic-comment-classification-using-bert