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ChatGPT, OpenAIFor text generation, the evaluation metric often depends on the specific task and desired outcomes. However, some common evaluation metrics used in NLP for text generation tasks include:Perplexity:Definition: Perplexity measures how well a probability model predicts a sample. In the context of language models, lower perplexity indicates a better predictive model.Usage: It is widel..

The * in zip(*combined_dataset) is the "unpacking" operator in Python. It takes a list of tuples (in this case, combined_dataset, which consists of pairs like (review_text, label)) and "unzips" them into two separate tuples: one for texts and one for labels.In other words:texts will contain all the review texts.labels will contain all the corresponding labels.The * operator effectively transpose..

Join two tuples together:a = ("John", "Charles", "Mike")b = ("Jenny", "Christy", "Monica")x = zip(a, b)#use the tuple() function to display a readable version of the result:print(tuple(x))(('John', 'Jenny'), ('Charles', 'Christy'), ('Mike', 'Monica'))https://www.w3schools.com/python/ref_func_zip.asp W3Schools.comW3Schools offers free online tutorials, references and exercises in all the major la..

The model bert-base-uncased is used because it converts all text to lowercase before processing, ignoring case differences. This is particularly useful when case sensitivity is not important for the task, such as sentiment analysis, where "Happy" and "happy" should be treated the same. The "uncased" version is generally more efficient and performs well when the distinction between uppercase and ..

ChatGPT, OpenAINaive Bayes in Sentiment Analysis:Pros:Simplicity: Easy to implement and interpret.Efficiency: Works well with smaller datasets and requires less computational power.Baseline: Provides a strong baseline for comparison with more complex models.Cons:Assumption of Independence: Assumes features (words) are independent, which is often not true in language processing.Limited Understand..

https://medium.com/@sandyeep70/demystifying-text-summarization-with-deep-learning-ce08d99eda97 Text Summarization with BART ModelIntroductionmedium.comdef text_summarizer_from_pdf(pdf_path): pdf_text = extract_text_from_pdf(pdf_path) model_name = "facebook/bart-large-cnn" model = BartForConditionalGeneration.from_pretrained(model_name) tokenizer = BartTokenizer.from_pretrained(model_..

To determine P(J∣F,I) the probability Jill Stein spoke the words 'freedom' and 'immigration', we'll apply Bayes' Theorem: P(J∣F,I) =P(J)×P(F∣J)×P(I∣J) / P(F,I) Where: P(J) is the prior probability (the overall likelihood of Jill Stein giving a speech). In our case, P(J)=0.5P(J)=0.5. P(F∣J) and P(I∣J) are the likelihoods. These represent the probabilities of Jill Stein saying the words 'freedom' ..

Bayesian inference is a method of statistical analysis that allows us to update probability estimates as new data arrives. In the realm of Natural Language Processing (NLP), it is often used in spam detection, sentiment analysis, and more. Let's explore the initial steps of preprocessing text data for Bayesian inference. 1. Convert Text to Lowercase: To ensure consistency, we convert all text da..

When working with data in Python, the pandas library is a vital tool. However, a common hiccup new users face is the "NameError" related to its commonly used alias 'pd'. Let's understand and resolve this error. The message "NameError: name 'pd' is not defined" indicates that the pandas library, commonly aliased as "pd", hasn't been imported. The solution is straightforward. You need to ensure th..

In the context of the Naive Bayes classifier, probability normalization plays a vital role, especially when we want our probabilities to reflect the true likelihood of an event occurring in comparison to other events. When predicting class labels using the Naive Bayes formula, we compute the product of feature probabilities for each class. However, these products do not sum up to 1 across classe..

Let's break down the regex pattern \b\w+\b and explain it with examples. 1. \w The \w metacharacter matches any word character, which is equivalent to the character set [a-zA-Z0-9_]. This includes: Uppercase letters: A to Z Lowercase letters: a to z Digits: 0 to 9 Underscore: _ 2. \w+ The + is a quantifier that means "one or more" of the preceding character or group. So, \w+ matches one or more ..