Why production NLP needs engineering - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a pre-trained NLP model using Hugging Face Transformers.

NLP

from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained([1])

Drag options to blanks, or click blank then click option'

Atransformers

Bbert-base-uncased

Cload_model

D"bert-base-uncased"

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to put quotes around the model name.

Using a variable name instead of a string.

The model name must be a string, so it needs to be in quotes.

2fill in blank

medium

Complete the code to tokenize input text for the NLP model.

NLP

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
inputs = tokenizer([1], return_tensors="pt")

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A"Hello, how are you?"

Binput_text

Ctext

D['Hello', 'world']

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a variable name without defining it.

Passing a list instead of a string.

The tokenizer expects a string input, so the text must be in quotes.

3fill in blank

hard

Fix the error in the code to get model predictions from tokenized inputs.

NLP

outputs = model([1])
predictions = outputs.logits.argmax(dim=1)

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Ainputs['input_ids']

Binputs.input_ids

Cinputs['tokens']

Dinputs

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the whole inputs dictionary instead of input_ids.

Using a wrong key like 'tokens'.

The model expects input_ids as a keyword argument, so we pass inputs['input_ids'].

4fill in blank

hard

Fill both blanks to create a function that preprocesses text and returns model predictions.

NLP

def predict(text):
    inputs = tokenizer([1], return_tensors=[2])
    outputs = model(inputs['input_ids'])
    return outputs.logits.argmax(dim=1).item()

Drag options to blanks, or click blank then click option'

Atext

B"pt"

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the wrong variable or forgetting quotes around 'pt'.

Using inconsistent variable names.

The function takes text as input and tokenizer needs return_tensors='pt' for PyTorch tensors.

5fill in blank

hard

Fill all three blanks to add batch processing and return a list of predictions.

NLP

def batch_predict(texts):
    inputs = tokenizer([1], padding=True, truncation=True, return_tensors=[2])
    outputs = model(inputs['input_ids'])
    preds = outputs.logits.argmax(dim=[3])
    return preds.tolist()

Drag options to blanks, or click blank then click option'

Atexts

B"pt"

C1

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using dim=0 which would give wrong axis for batch predictions.

Passing a single text instead of a list.

We pass the list of texts, use 'pt' for PyTorch tensors, and argmax over dim=1 for batch dimension.