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Sentence Transformer | English Grammar for Class 6 PDF Download

Transformation of Sentences


To understand the transformation of sentences one needs to understand the Clauses.

What is clause and examples?

The clause refers to the sentence which is not a complete sentence as it contains a subject and predicate as a group of words.

There are two types of Clause

  1. Independent Clause: it is a sentence or a part of multi clause-sentence that contains subject and predicate both. The sentence can use a conjunction like or, for, nor, yet, but etc.Example- we visited Delhi, but we did not go to India Gate. 
  2. Dependent Clause: The clause that has subject and predicate and that cannot be stand alone as a sentence. 

Types of the dependent clause

  • Adjective clause or Relative clause: the clause where adjective comes before a noun or pronoun like who, which, that, where etc. Example- This is a palace that we saw in the movie.
  • Adverb or adverbial clause: The clause that starts from subordinating conjunction like because, although, when, until etc.
  • Noun Clause: a sentence where clause works as a compliment, the subject is called a noun. Example- what we saw at the theatre was unbelievable.

Transformation Sentences


Simple sentence
The simple sentence refers to the sentence where there is one independent clause. He confuses his illegal act.

Complex sentence
The complex sentence refers to a sentence that has one clause or one or more subordinate clause. Example – he confessed that he was guilty of his illegal act.

Compound sentence
The compound sentence refers to a sentence which has more than one main clause. Example-I went for a movie named Transformer but the movie tickets were all booked.

Rules for transforming the simple sentence into a complex sentence

  1. Present participle in a simple sentence can be converted into a complex sentence by adding since, as or when in the first half of the sentence.
    E.g. – Closing a door, I went back to the room
    Transformed - when I closed the door, I went back to the room.
  2. Being/ verb + ing can be converted by adding/then/since at the first half of the sentence.
    E.g. - after winning the match he cried.
    Transformed – As he won the match he cried.
  3. “to” in the simple sentence, to convert into a complex sentence by adding “so that” in the sentence.
    E.g. – we drink for thirst
    Transformed – we drink so that we are thirsty.
  4. In the simple sentence “in spite of/despite”, to convert into a complex sentence by adding “though/although” in the sentence.
    E.g. – In spite of being rich, she cook her food.
    Transformed – though she is rich she cook her food.
  5. Add “since” in the sentence instead of “because of” to transform the sentence into the complex.
    E.g. – because of his illness, he could not attend the school
    Transformed- since he was ill, he could not join the school.
  6. “subject+verb+object+ present participle” type of simple sentence can be converted into the complex by adding “subject+verb+object+relative+pronoun of the object+ be ver according to relative pronoun and tense + rest of the sentence”.
    E.g. -
    I saw a bird flying
    Transformed- I saw a bird which was flying.
  7. The simple sentence starting with “without” can be transformed into the complex by adding “if/in case” in the sentence.
    E.g. -  without adding the sugar the coffee will taste bad.
    Transformed- if you do not add sugar the coffee will taste bad.
The document Sentence Transformer | English Grammar for Class 6 is a part of the Class 6 Course English Grammar for Class 6.
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FAQs on Sentence Transformer - English Grammar for Class 6

1. What is the purpose of a sentence transformer?
Ans. A sentence transformer is designed to convert sentences into fixed-length numerical representations, which can then be used for various natural language processing tasks such as sentence similarity, text classification, and information retrieval.
2. How does a sentence transformer work?
Ans. A sentence transformer uses pre-trained models that encode the meaning of sentences into dense vectors. These vectors capture the semantic information of the sentences and can be used to measure their similarity or perform other NLP tasks.
3. What are the applications of sentence transformers?
Ans. Sentence transformers have various applications, including document retrieval, question answering, chatbots, text summarization, and sentiment analysis. They can be used in a wide range of NLP tasks that involve understanding and generating meaningful sentences.
4. Can sentence transformers be used for multilingual tasks?
Ans. Yes, sentence transformers can be trained on multilingual data and used for multilingual NLP tasks. By encoding sentences into fixed-length vectors, they can capture the semantic information of sentences regardless of the language they are written in.
5. Are pre-trained sentence transformers available for public use?
Ans. Yes, there are several pre-trained sentence transformer models available for public use, such as BERT, RoBERTa, and DistilBERT. These models can be fine-tuned on specific tasks or used as feature extractors for various NLP applications.
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