classification

Hi I would like to propose a better implementation for 'test_indices':

We can remove the unneeded np.array casting:

Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))

Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))

Describe the bug
Unable to read data from a web location using address filed

To Reproduce

from pycaret.datasets import get_data
data = get_data(
    "economic_indicators_all_ex_3mo_china_inc_treas3mo", 
address="https://raw.githubusercontent.com/ngupta23/DS6373_TimeSeries/2b40f0071c3b7ec6a05dc0106f64e041f8cbaaef/Projects/gdp_prediction/data/",
)

In principle it seems getting the parameters from FLAML to C# LightGBM seems to work, but I dont have any metrics yet. The names of parameters are slightly different but documentation is adequate to match them. Microsoft.ML seems to have version 2.3.1 of LightGBM.

Another approach that might be useful, especially for anyone working with .NET, would be having some samples about conversion to ONN

Add a way to change the sample id output in the annotation process to a specific number (see picture).

Reason: I want to annotate large text and the app don't like it when the documents to annotate are too large, so I spitted in a sentence the document but I would like to be able to