Translation in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the translation model using Hugging Face Transformers.

Prompt Engineering / GenAI

from transformers import MarianMTModel, MarianTokenizer
model_name = 'Helsinki-NLP/opus-mt-en-de'
tokenizer = MarianTokenizer.from_pretrained([1])

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

A'en-de'

B'Helsinki-NLP/opus-mt-en-de'

Cmodel_name

DMarianTokenizer

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the model name as a string literal instead of using the variable.

Passing the class name instead of the model name.

The variable model_name holds the model identifier string, so it should be passed to from_pretrained() to load the tokenizer.

2fill in blank

medium

Complete the code to tokenize the input text for translation.

Prompt Engineering / GenAI

text = 'Hello, how are you?'
inputs = tokenizer([1], return_tensors='pt', padding=True)

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

Atext

B'text'

C['Hello']

D['Hello, how are you?']

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the string 'text' instead of the variable text.

Passing a list when a string is expected.

The variable text contains the input string and should be passed directly to the tokenizer.

3fill in blank

hard

Fix the error in generating the translated tokens from the model output.

Prompt Engineering / GenAI

translated_tokens = model.generate([1])

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

Ainputs['input_ids']

Binputs['attention_mask']

Cinputs

Dinputs['token_type_ids']

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the whole inputs dictionary instead of just the input IDs.

Passing attention mask or token type IDs instead of input IDs.

The generate method requires the input IDs tensor, which is accessed by inputs['input_ids'].

4fill in blank

hard

Fill both blanks to decode the translated tokens into a readable string.

Prompt Engineering / GenAI

translated_text = tokenizer.decode([1][0], skip_special_tokens=[2])

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

Atranslated_tokens

BTrue

CFalse

Dinputs

Attempts:

3 left

💡 Hint

Common Mistakes

Using the inputs variable instead of translated tokens.

Setting skip_special_tokens to False causing special tokens to appear.

The decode method takes the first element of translated_tokens and skips special tokens by setting skip_special_tokens=True.

5fill in blank

hard

Fill all three blanks to create a function that translates English text to German.

Prompt Engineering / GenAI

def translate_en_to_de(text):
    tokenizer = MarianTokenizer.from_pretrained([1])
    model = MarianMTModel.from_pretrained([2])
    inputs = tokenizer(text, return_tensors=[3], padding=True)
    translated_tokens = model.generate(inputs['input_ids'])
    return tokenizer.decode(translated_tokens[0], skip_special_tokens=True)

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

A'Helsinki-NLP/opus-mt-en-de'

C'pt'

D'tf'

Attempts:

3 left

💡 Hint

Common Mistakes

Using different model names for tokenizer and model.

Using 'tf' instead of 'pt' for return_tensors causing errors.

The function loads the tokenizer and model for English to German translation using the same model name string. The tokenizer returns PyTorch tensors, so return_tensors='pt' is used.