English as a second language APT: Automated paraphrasing tool FAS: False accusation of a student SE: Add spelling errors (adversarial technique) IC: Increase complexity (adversarial technique) PR: Paraphrase (adversarial technique) GenAI: Generative AI FM: Foundation model ELL: English la...
Recent advancements in high-quality, large-scale English resources have pushed the frontier of English Automatic Text Simplification (ATS) research. Howeve... MJ Ryan,T Naous,W Xu 被引量: 0发表: 2023年 Technology and the state: Building capacity to tax via text The ability of the state to...
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Ch 12. Poetry: Hope is the Thing with... Ch 13. Nonfiction Texts: Gettysburg Address &... Ch 14. 9th Grade English: Media & Art... Ch 15. 9th Grade English: Word Choice &... Ch 16. 9th Grade English: Writing Skills... Ch 17. 9th Grade English: The Writing... Ch 18. 9th...
English Short Story about Family: The Secret Valentine's Day Love Story (Glossary, Illustrated Story, Comprehension Questions, Answer Key) The Invisible Warrior: A Native American Tale (Adapted for English Learners) Pwyll, Prince of Dyfed: A Welsh Tale for English Learners ...
Yes/No Questions in the Present Perfect SimpleTo create a question that will be answered with a yes or no, start the question with Have or Has, (Haven’t or Hasn’t for a negative question) then add a subject (the person or thing that has done the action) followed by the V3 (Past...
Practice using the simple past correctly with the exercises below. In the blank space in each sentence, fill in the correct simple past form based on the subject and verb specified (e.g., “[he / talk]” becomes “he talked”). Some answers may also be negative statements or questions....
Businesses can use a text marketing service provider like SimpleTexting to configure an SMS customer service solution that lets their customers instantly get in touch with questions about an order, special requests, or inquiries about whether a product is in stock. Unlike mass texting, which ...
("princeton-nlp/sup-simcse-bert-base-uncased")# Tokenize input textstexts=["There's a kid on a skateboard.","A kid is skateboarding.","A kid is inside the house."]inputs=tokenizer(texts,padding=True,truncation=True,return_tensors="pt")# Get the embeddingswithtorch.no_grad():...
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