[Solved] You run this code to analyze sentiment but get an error: from transformers import AutoTokenizer, AutoModelForSequenceClassification model = AutoModelForSequenceClassification.... | NLP

NLP - Sentiment Analysis Advanced

You run this code to analyze sentiment but get an error:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')
tokenizer = AutoTokenizer.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')

inputs = tokenizer('Das ist schlecht', return_tensors='pt')
outputs = model(inputs)

What is the cause of the error?

AMissing import for torch library.

BTokenizer is loaded after the model, causing mismatch.

CThe input text is in German, which the model cannot process.

DModel expects keyword arguments, but inputs passed as positional argument.

Step-by-Step Solution

Solution:

Step 1: Check how model is called
The model expects inputs as keyword arguments like model(**inputs), but here inputs are passed as a single positional argument.
Step 2: Analyze other options
Tokenizer order does not cause error. The model supports German. Missing torch import would cause a different error.
Final Answer:
Model expects keyword arguments, but inputs passed as positional argument. -> Option D
Quick Check:
Use model(**inputs) not model(inputs) [OK]

Quick Trick: Pass inputs with ** to model call [OK]

Common Mistakes:

MISTAKES

Passing inputs without unpacking as keyword args
Blaming language support incorrectly
Ignoring error message details

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You run this code to analyze sentiment but get an error:

Step 1: Check how model is called

Step 2: Analyze other options

Final Answer:

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