"Embeddings" Example Sentences
1. The neural network used for language processing requires word embeddings.
2. She analyzed the text's embeddings to understand the author's intent.
3. The embeddings of the images were used to train the computer vision model.
4. In natural language processing, embeddings represent words as vectors.
5. Word embeddings are helpful for sentiment analysis of text.
6. They used word embeddings to create a recommendation system for books.
7. The embeddings of a song can be used to find other songs with a similar mood.
8. The chatbot's responses were improved by using pre-trained embeddings.
9. The embeddings of genetic data can be used for personalized medicine.
10. The embeddings of customer reviews were analyzed for consumer research.
11. They used embeddings to map out the relationships between different species in biology.
12. Word embeddings can be used to understand the context and tone of a message.
13. The embeddings of financial data were analyzed for stock market predictions.
14. The embeddings of brain activity were studied to understand patterns of thought.
15. Emotion embeddings were used to create a personalized music playlist based on mood.
16. The embeddings of social media posts can be used to understand public opinion.
17. Word embeddings can be used for machine translation of text.
18. The embeddings of medical images were analyzed for disease detection.
19. The embeddings of sound data were used to improve speech recognition technology.
20. They used sentence embeddings to detect plagiarism in academic writing.
21. The embeddings of food images were analyzed to create personalized diet plans.
22. The embeddings of voice recordings were used to identify individuals in forensic investigations.
23. They used embeddings to find patterns in job postings for predictive hiring.
24. The embeddings of weather data were used for climate modeling.
25. Neuroscientists use embeddings to analyze patterns of brain activity during sleep.
26. The embeddings of geographic data were analyzed for urban planning.
27. Word embeddings were used to understand patterns of language evolution.
28. They used embeddings to identify patterns in customer behavior for marketing purposes.
29. The embeddings of social network connections were analyzed for understanding how information spreads.
30. The embeddings of sensor data were used to monitor and predict traffic patterns.
Common Phases
1. The
embeddings extracted from the dataset were used to train the model;
2. The performance of the classification algorithm was improved by incorporating word
embeddings;
3. A visualization of the
embeddings showed a clear clustering of similar words;
4. The sentence representation was enriched by adding contextual
embeddings;
5. The
embeddings were fine-tuned to improve the model's accuracy on a specific task;
6. A pre-trained document
embeddings model was used to generate feature vectors for the document classification task;
7. The word
embeddings were trained using a skip-gram neural network architecture;
8. The
embeddings were used to compute similarity between pairs of sentences;
9. An attention mechanism was used to weight the importance of different
embeddings for the final output;
10. The multi-lingual sentence
embeddings enabled cross-lingual transfer learning.