Question
Question: 1
Which of the following is NOT a step in the process of tokenizing a dataset to be used in a Natural Language Processing (NLP) model?
Count the number of unique words in the dataset
Create numerical sequences from the input sentences
Adjust sequences to be the same length
Converting the words to numerical values
Question: 2
Which of the following best explains why the OOV Token is important when tokenizing the dataset?
There are an unlimited number of words in the language and the OOV Token is needed to capture the more obscure words.
The OOV Token is used for all misspelled words in the dataset.
The model is trained on all words in the dataset, but the OOV Token is used for new words that might come up with a prediction text.
While a model could use all words, it is common to limit the selection to only common words found in the dataset, and the OOV Token is used for less common words.
Question: 3
What is a supervised model in the context of a neural network?
A supervised model is a model where the input data is validated before being used to create the model.
A supervised model is a model where labels are used on the training data to establish a relationship between the input and output.
A supervised model is a model where the results are evaluated after each epoch to allow the model to be stopped once a sufficient result is found.
A supervised model is a model where a validation dataset is used to compare how well the model fits the data.
Which of the following is NOT a step in the process of tokenizing a dataset to be used in a Natural Language Processing (NLP) model?
Count the number of unique words in the dataset
Create numerical sequences from the input sentences
Adjust sequences to be the same length
Converting the words to numerical values
Question: 2
Which of the following best explains why the OOV Token is important when tokenizing the dataset?
There are an unlimited number of words in the language and the OOV Token is needed to capture the more obscure words.
The OOV Token is used for all misspelled words in the dataset.
The model is trained on all words in the dataset, but the OOV Token is used for new words that might come up with a prediction text.
While a model could use all words, it is common to limit the selection to only common words found in the dataset, and the OOV Token is used for less common words.
Question: 3
What is a supervised model in the context of a neural network?
A supervised model is a model where the input data is validated before being used to create the model.
A supervised model is a model where labels are used on the training data to establish a relationship between the input and output.
A supervised model is a model where the results are evaluated after each epoch to allow the model to be stopped once a sufficient result is found.
A supervised model is a model where a validation dataset is used to compare how well the model fits the data.
Answers
GPT-4o mini
### Answers:
**Question 1:**
Which of the following is NOT a step in the process of tokenizing a dataset to be used in a Natural Language Processing (NLP) model?
- **Answer:** Count the number of unique words in the dataset
**Explanation:** Tokenization typically involves converting text into tokens (words or subwords), creating numerical sequences, and adjusting sequences to uniform lengths. Counting unique words is more of a statistical analysis and is not a direct step in the tokenization process itself.
---
**Question 2:**
Which of the following best explains why the OOV Token is important when tokenizing the dataset?
- **Answer:** The model is trained on all words in the dataset, but the OOV Token is used for new words that might come up with a prediction text.
**Explanation:** The Out-Of-Vocabulary (OOV) token is crucial for handling words that were not present in the training dataset but may appear in new sentences or contexts, ensuring the model can still make predictions even when encountering unfamiliar terms.
---
**Question 3:**
What is a supervised model in the context of a neural network?
- **Answer:** A supervised model is a model where labels are used on the training data to establish a relationship between the input and output.
**Explanation:** Supervised learning involves training a model on labeled data, meaning that each input data point is associated with a corresponding output label, allowing the model to learn the mapping from inputs to outputs effectively.
**Question 1:**
Which of the following is NOT a step in the process of tokenizing a dataset to be used in a Natural Language Processing (NLP) model?
- **Answer:** Count the number of unique words in the dataset
**Explanation:** Tokenization typically involves converting text into tokens (words or subwords), creating numerical sequences, and adjusting sequences to uniform lengths. Counting unique words is more of a statistical analysis and is not a direct step in the tokenization process itself.
---
**Question 2:**
Which of the following best explains why the OOV Token is important when tokenizing the dataset?
- **Answer:** The model is trained on all words in the dataset, but the OOV Token is used for new words that might come up with a prediction text.
**Explanation:** The Out-Of-Vocabulary (OOV) token is crucial for handling words that were not present in the training dataset but may appear in new sentences or contexts, ensuring the model can still make predictions even when encountering unfamiliar terms.
---
**Question 3:**
What is a supervised model in the context of a neural network?
- **Answer:** A supervised model is a model where labels are used on the training data to establish a relationship between the input and output.
**Explanation:** Supervised learning involves training a model on labeled data, meaning that each input data point is associated with a corresponding output label, allowing the model to learn the mapping from inputs to outputs effectively.