Transform in a sentence

As input to our model.Now as we can see the first sentence has 5 words, but the last sentence has 6 words, this will cause an imbalance in our model. So to take care of that issue what we do is max padding, which means we take the longest sentence from our document corpus and we pad the other sentence to be as long. This means if all of our sentences are 5 words and one sentence is 6 words we will make all the sentences of 6 words.HOW WE ADD EXTRA INDEX ??If you have noticed, we didn’t use 0 as an index number, and preferably that will not be used anywhere even if we have 100000 words long data corpus, hence we use 0 as our padding index. This also means that we are appending nothing to our actual sentence as 0 doesn’t represent any specific word, hence the integrity of our sentences is intact.Featured Article:- BEST FINAL YEAR AI PROJECTS2> BAG OF WORDS (BoW)Bag of Words or BoW is another form of encoding where we use the whole data corpus to encode our sentences. Data Corpus: [“a”, “bad”, “cat”, “good”, “has”, “he”, “is”, “mobile”, “not”, “phone”, “she”, “temper”, “this”]As we know that our data corpus will never change, so if we use this as a baseline to create encodings for our sentences, then we will be on the upper hand to not pad any extra words. Now, 1st sentence we have is “this is a good phone”. This is how our first sentence is represented.abadcatgoodhasheismobilenotphoneshetemperthis1001001001001Now, there are 2 kinds of BOW:1. Binary BoW2. BoWThe difference between them is, in Binary BoW we encode 1 or 0 for each word appearing or non-appearing in the sentence. We do not take into consideration the frequency of the word appearing in that sentence. In BoW we also take into consideration the frequency of each word occurring in that sentence.SCIKIT IMPLEMENTATION OF BoWIn this cell, we can see how the application of the fit() and transform() functions enabled us to create a vocabulary of 8 words for the document. The vectorizer fit allowed us to build the vocab and the transform encoded it into the number of appearances of each word. The indexing is done alphabetically.3> TF-IDF ENCODINGFig:- text encoding tf idfTerm Frequency-Inverse Data Frequency. As the name suggests, here we give every word a relative frequency coding with respect to the current sentence and the whole document.1. Term Frequency: Is the occurrence of the current word in the current sentence w.r.t the total number of words in the current sentence. 2. Inverse Data Frequency: Log of the Total number of words in the whole data corpus w.r.t the total number of sentences containing the current word.One thing to note here is we have to calculate the word frequency of each word for that particular sentence, because depending on the number of times a word occurs in a sentence the TF value can change, whereas the IDF value remains constant, until

0 Welcome (Well, start scrolling already!) Intro Welcome to this Skrollr tutorial! This tutorial will show you how to use the Skrollr plugin and the data attribute to quickly and easily add cool transitions and effects. After adding the Skrollr plug-in to your HTML code, users can start assigning data points. Data points represent how far you have scrolled down from the top of the page. According to the scroll counter in the top-right corner, we are around 2000 data points. How It Works Next you can start adding data points to your HTML code to make the Skrollr magic happen. Typically each data set will have four different data attributes. The example below defines how a sentence will behave based on the data attributes assigned to it. data-400="top:100; opacity:0;" data-1000="top:0; opacity:1;" data-1500="" data-2000="top:-100; opacity:0;" The first data point defines where the sentence is and when it is going to start appearing. At 400 data points its location is 100 percent below the top of the screen and its opacity is zero, meaning it's invisible. The second data point defines when the sentence will be fully visible. At 1000 data points the sentence will be fully on the screen and visible at opacity 1. The third data point is blank, which causes the sentence to pause on the screen until 1500 data points. Lastly, the fourth data point defines when the sentence will fully disappear. At 2000 data points the sentence will be -100 percent from the top and invisible at opacity 0.Attributes, such as top and opacity, can be substituted for other attributes to yield different effects. Transitions For example, you can create interesting transitions by making things slide in and out using top, bottom, right and left. Rotate in and out to add a dynamic effect using transform. Or disappear completely using opacity. Transitions Directions, transform and opacity are all different values that can be used within a data attribute. Color You can also do fun things with color, like change the background color and text using RGB values. You can even change the color of individual words. That covers some of the basics. Swipe or arrow-right over to go to page 2.

And simple sentences that does not transform the sentence properly into a complex one. 4. Which one among the given is the correct transformation of the given simple sentence into complex?Owing to her high fever, Niharika did not attend the meeting.a) Niharika had high fever and therefore did not attend the meetingb) Niharika did not attend the meeting as she had high feverc) Niharika had high fever due to which she did not attend the meetingd) The meeting was not attended by Niharika due to high feverView AnswerAnswer: bExplanation: The sentence, “Niharika did not attend the meeting as she had high fever” is the correct transformation of the given sentence from simple to complex. The conjunction ‘as’ is used to join the two clauses as one dependent and one independent in the resulting complex sentence.5. The correct transformation of the given sentence into complex is ______________Only shareholders can attend the meeting.a) You cannot attend the meeting since you are not a shareholderb) The shareholder can attend the meeting but the others cannotc) The meeting can be attended only by shareholdersd) If you are not a shareholder, you cannot attend the meetingView AnswerAnswer: dExplanation: The words ‘Only shareholders can attend’ in the given sentence is changed into ‘if you are not a shareholder, you cannot attend’ which transforms the sentence into a complex one. The sentence is thus framed as a combination of one dependent and one independent clause, ‘if you are not a shareholder’ and ‘you cannot attend the meeting’. Thus, the correct transformation of the sentence is, ‘if you are not a shareholder, you cannot attend the meeting’. To practice all topics of English Grammar, here is complete set of 5000+ Questions and Answers. Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He lives in Bangalore, and focuses on development of Linux Kernel, SAN Technologies, Advanced C, Data Structures & Alogrithms.Stay connected with him at LinkedIn.Subscribe to his free Masterclasses at Youtube & discussions at Telegram SanfoundryClasses.

Ever found yourself staring at a spreadsheet full of text in all caps and feeling a bit overwhelmed? You're not alone. Whether you're dealing with a list of names, addresses, or product descriptions, all caps can be hard on the eyes and even harder to work with. Fortunately, Excel offers several ways to convert that shouting text into more reader-friendly sentence case. Let's jump into how you can transform your data with ease.In this article, we'll walk you through various methods to change all caps to sentence case in Excel. From using built-in functions to leveraging a little VBA magic, we've got you covered. By the end, you'll be equipped with practical skills and tips to handle your data like a pro.⚡The best AI spreadsheet: Bricks makes it easy to create & share reports, presentations, charts, and visuals — all backed by your data. Try it free →Before we roll up our sleeves, let's get clear on what sentence case means. Essentially, sentence case is the standard way of writing where only the first letter of the first word in a sentence is capitalized. Meanwhile, the rest of the words are in lowercase, unless they're proper nouns. It's the default style you see in everyday writing, like this paragraph.Why is this important in Excel? Well, using sentence case can make your data more readable and professional, especially if you're preparing reports or presentations. Imagine presenting a list of client names to your boss, and they're all screaming in all caps. Not the best look, right? Switching to sentence case can make everything look neat and polished.This might seem like a small detail, but attention to these nuances can make a big difference in how your work is perceived. Now that we know what we're aiming for, let's dive into the methods for achieving this in Excel.One of the easiest ways to convert text to sentence case in Excel is by using the PROPER function. This function capitalizes the first letter of each word in a text string and changes the rest to lowercase. While it's not exactly sentence case, it's

Class langchain_text_splitters.sentence_transformers.SentenceTransformersTokenTextSplitter(chunk_overlap: int = 50, model_name: str = 'sentence-transformers/all-mpnet-base-v2', tokens_per_chunk: int | None = None, **kwargs: Any)[source]#Splitting text to tokens using sentence model tokenizer.Create a new TextSplitter.MethodsParameters:chunk_overlap (int)model_name (str)tokens_per_chunk (Optional[int])kwargs (Any)__init__(chunk_overlap: int = 50, model_name: str = 'sentence-transformers/all-mpnet-base-v2', tokens_per_chunk: int | None = None, **kwargs: Any) → None[source]#Create a new TextSplitter.Parameters:chunk_overlap (int)model_name (str)tokens_per_chunk (int | None)kwargs (Any)Return type:Noneasync atransform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document]#Asynchronously transform a list of documents.Parameters:documents (Sequence[Document]) – A sequence of Documents to be transformed.kwargs (Any)Returns:A sequence of transformed Documents.Return type:Sequence[Document]count_tokens(*, text: str) → int[source]#Counts the number of tokens in the given text.This method encodes the input text using a private _encode method andcalculates the total number of tokens in the encoded result.Parameters:text (str) – The input text for which the token count is calculated.Returns:The number of tokens in the encoded text.Return type:intcreate_documents(texts: List[str], metadatas: List[dict] | None = None) → List[Document]#Create documents from a list of texts.Parameters:texts (List[str])metadatas (List[dict] | None)Return type:List[Document]classmethod from_huggingface_tokenizer(tokenizer: Any, **kwargs: Any) → TextSplitter#Text splitter that uses HuggingFace tokenizer to count length.Parameters:tokenizer (Any)kwargs (Any)Return type:TextSplitterclassmethod from_tiktoken_encoder(encoding_name: str = 'gpt2', model_name: str | None = None, allowed_special: Literal['all'] | AbstractSet[str] = {}, disallowed_special: Literal['all'] | Collection[str] = 'all', **kwargs: Any) → TS#Text splitter that uses tiktoken encoder to count length.Parameters:encoding_name (str)model_name (str | None)allowed_special (Literal['all'] | ~typing.AbstractSet[str])disallowed_special (Literal['all'] | ~typing.Collection[str])kwargs (Any)Return type:TSsplit_documents(documents: Iterable[Document]) → List[Document]#Split documents.Parameters:documents (Iterable[Document])Return type:List[Document]split_text(text: str) → List[str][source]#Splits the input text into smaller components by splitting text on tokens.This method encodes the input text using a private _encode method, thenstrips the start and stop token IDs from the encoded result. It returns theprocessed segments as a list of strings.Parameters:text (str) – The input text to be split.Returns:A list of string components derived from the input text afterencoding and processing.Return type:List[str]transform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document]#Transform sequence of documents by splitting them.Parameters:documents (Sequence[Document])kwargs (Any)Return type:Sequence[Document]Examples using SentenceTransformersTokenTextSplitterHow to split text by tokens

Text in the cell is changed, the PROPER function will automatically update the capitalized version, providing a dynamic and up-to-date representation of your data.By utilizing the PROPER function in Excel, you can easily capitalize the first letter of each word in your text strings, ensuring consistency and professionalism in your data.Applying text case using Excel's built-in commandsMicrosoft Excel offers several built-in commands that allow users to easily change the case of text within their spreadsheets. These commands provide a convenient way to transform text to meet specific formatting requirements. In this chapter, we will explore the various options available and provide instructions on how to access and apply these commands effectively.Introduce the built-in commands available in Excel for changing text case.Excel provides users with several built-in commands specifically designed to change the case of text. These commands are accessible through the Ribbon interface and offer a range of options to suit different formatting needs. By utilizing these commands, users can quickly modify the case of their text without the need for complex formulas or manual editing.Discuss the options like Sentence case, UPPERCASE, lowercase, and Capitalize Each Word.Excel's built-in commands include four primary options for changing text case: Sentence case: This option capitalizes the first letter of each sentence and converts the remaining letters to lowercase. It is ideal for ensuring consistent capitalization within paragraphs or blocks of text. UPPERCASE: Choosing this option converts all text to uppercase letters. It is useful when a user wants to emphasize text or create