dborrelli / chat-intents Goto Github PK

@dborrelli When I specify a value for the "random_state" parameter in the "bayesian_search," I receive the following warning: "UserWarning: n_jobs value -1 overridden to 1 by setting random_state. Use no seed for parallelism. warn(f"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.")

Hyper Param tuning is taking significant amount of time.I want to use the 'random_state' parameter to ensure reproducibility, while also setting 'n_jobs' to -1 to enable parallel processing. What's the best way to achieve this?"

Hi,
I am using chat-intents and the clustering works very well.
However, I am working with french data and the label extraction gives poor results. I assume it's because this method necessarily uses a specialized spacy model for English.
I was wondering if the name of the loaded spacy model or at least the language could be passed as a parameter of apply_and_summarize_labels for example ?
This way, the performance could be much better for all languages other than English.

!pip install chatintents
leads to

ERROR: Could not find a version that satisfies the requirement chatintents (from versions: none)
ERROR: No matching distribution found for chatintents

I'm using google colab

Hi, how do I put a limit on the maximum number of topics to be generated? And a minimum? Is there a way to do this within the hyper parameter optimization?

Thanks,
Ari

Hi, while I'm using apply_and_summarize_labels,
it's causing an issue as below. Please help.

df_summary, labeled_docs = model.apply_and_summarize_labels(data_sample.sentence)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/tmp/ipykernel_21756/2555802596.py in <module>
----> 1 df_summary, labeled_docs = model.apply_and_summarize_labels(data_sample.sentence)

/opt/conda/lib/python3.9/site-packages/chatintents/ChatIntents.py in apply_and_summarize_labels(self, df_data)
    418         df_clustered[category_col] = self.best_clusters.labels_
    419 
--> 420         numerical_labels = df_clustered[category_col].unique()
    421 
    422         # create dictionary mapping the numerical category to the generated

AttributeError: 'numpy.ndarray' object has no attribute 'unique'