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Question 118 MCQ · 0.0 marks
General Note:
• If you find the given information is insufficient in any of the Numerical Answer Type (NAT)
questions, enter -1 as the answer.
• In all your calculations, take at least two digits. For example, if the intermediate result is
1.860875, then take it as 1.86 to the next step.
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Instructions has been mentioned above.
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This Instructions is just for a reference & not for an evaluation.
A published solution is not available for this question yet.
Consider following assertion and reason pair:
**Assertion:** A small vocabulary is desirable, as it reduces the size of the embedding matrix.
**Reason:** Decreasing the size of the vocabulary increases the number of tokens in the input.
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Assertion and Reason are both true and Reason is a correct explanation of
Assertion.
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Assertion and Reason are both true and Reason is not a correct explanation of
Assertion.
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Assertion is true but Reason is false.
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Assertion is false but Reason is true.
A published solution is not available for this question yet.
Consider following statement and mark if it is true or false:
Repeating examples in the pre-training datasets are completely harmless for downstream
performance.
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TRUE
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FALSE
A published solution is not available for this question yet.
What is the order of the language modeling pipeline?
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First, the model, which handles text and returns raw predictions. The tokenizer
then makes sense of these predictions and converts them back to text when needed.
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First, the tokenizer, which handles text and returns IDs. The model handles
these IDs and outputs a prediction, which can be some text.
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The tokenizer handles text and returns IDs. The model handles these IDs and
outputs a prediction. The tokenizer can then be used once again to convert these predictions back
to some text.
A published solution is not available for this question yet.
Consider fine-tuning the T5 model on a sentiment classification task. The T5 model was pre-
trained using the C4 dataset. Suppose, in the original dataset, the positive samples are denoted by
integer 2, neutral samples are denoted by integer 1 and the negative samples are denoted by
integer 0. Choose the necessary modifications that one needs to perform to use the T5 model for
fine-tuning:
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Map the label 0 to “negative” and 1 to “positive” in the dataset
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Map the label 0 to “negative”, 1 to “neutral” and 2 to “positive” in the dataset
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Map the label 0 to “0”, 1 to “1” and 2 to “2” in the dataset
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Update the vocabulary of T5 with the modified labels
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Add at least one linear layer on top of the decoder for final classification
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None of these.
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Suppose the T5 base model has to be fine tuned with gradual unfreezing. First layer of the
encoder takes the input in appropriate format. Gradual unfreezing is divided in 12 (i.e. no. of
layers) phases.
Choose the correct statements:
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In the first phase, weights of only the last layer of encoder and decoder (both)
are unfrozen.
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In the first phase, weights of only the last layer of decoder only are unfrozen.
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In the first phase, weights of only the last layer of encoder only are unfrozen.
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In the second phase, weights of only the second last layer of encoder and
decoder (both) are unfrozen.
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In the second phase, weights of the last two layers of encoder and decoder
(both) are unfrozen.
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In the first phase, weights of only the first layer of encoder and decoder (both)
are unfrozen.
A published solution is not available for this question yet.
Which pair will be merged in the third merge? Say the pair is (‘ab’,‘c’), then enter **“abc”** (without
quotes and white spaces). If there is a tie between two or more candidates, pick the one that
occurs first in the original vocabulary.
**NOTE:** Enter the exact answer without any extra space in the beginning or at the end.
A published solution is not available for this question yet.
**i-NLP**
**Section Id :** 64065382587
**Section Number :** 8
**Section type :** Online
**Mandatory or Optional :** Mandatory
**Number of Questions :** 19
**Number of Questions to be attempted :** 19
A published solution is not available for this question yet.
