sentiment-analysis | Deploying sentiment analysis model with Sagemaker | Predictive Analytics library

I am working on a CNN Sentiment analysis machine learning model which uses the IMDb dataset provided by the Torchtext library. On one of my lines of code

vocab = Vocab(counter, min_freq = 1, specials=('\', '\', '\', '\'))

I am getting a TypeError for the min_freq argument even though I am certain that it is one of the accepted arguments for the function. I am also getting UserWarning Lambda function is not supported for pickle, please use regular python function or functools partial instead. Full code

I am trying to a calculated list to serve as 2 additional columns in an existing csv, however I struggle with preparing them as 2 columns.

MWE:

I was following this tutorial regarding Databricks. In te final section, when calling the Language and Sentiment API, the sentiment column always return "Couldn't Detect Language". I'm not familiar enough with Scala to solve this problem.

https://docs.microsoft.com/en-us/azure/databricks/scenarios/databricks-sentiment-analysis-cognitive-services

This is the piece of code i'm running (The same as the tutorial):

I have fine-tuned my models with GPU but inferencing process is very slow, I think this is because inferencing uses CPU by default. Here is my inferencing code:

I want to predict the sentiment of thousands of sentences using huggingface.

I want to re-finetuned a transformer model but I get an unknown error when I tried to train the model. I can't change the "num_labels" on loading the model. So, I tried to change it manually

What's the straightforward way to distinguish between this two:

1. the movie received critical acclaim
2. the movie did not attain critical acclaim.

Seems to me 'sentiment analysis' of nlp could do it for me. So I'm using Textblob sentiment analysis. But both sentences' polarity is 0.0.

when i use the st.cache decorator to cash hugging-face transformer model i get

Unhashable TypeError

this is the code

I want to compare 2 column. If same will True if not same will False like this:

The output from my code is all False. But, the data actually is different. Why?

You can get my data github