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Case
A dataset for training a neural network in tasks of matching text descriptions of first impressions of a person with a photograph of that person
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Кейс Сбор впечатлений о людях

Generation
The ability to automatically create plausible objects
Machine Learning
The system's ability to automatically interpret data and predict outcomes
NLP
The system's ability to understand, analyze, and interpret human languages
Data Collection
Gathering suitable data for subsequent labeling
100 000
text descriptions
5 weeks
duration
Case Description
The dataset was collected on the "Toloka" platform by Roman Kutsev as part of his personal project in 2019 - "Impressor"
User would send any photo in the chat, and within 5 minutes, a bot described its first impression of the person in the photo
The dataset contains 100,000 textual descriptions of people's photos. Each text fragment was checked for grammatical errors, insults, profanity, and other ethical violations
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APPLICATION AREAS OF THE DATASET
  • NLP for analyzing opinions about people on social media.
Social Media Sentiment Analysis:
  • NLP for determining potential clients or buyers
Identifying Potential Customers:
  • NLP to improve the performance of recommender systems, for example, movie or product recommendation systems
Recommender Systems:

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Collection and transcription of audio recordings in Russian. The dataset solves the problems of recognition and synthesis of toponyms in Russian

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This dataset is designed to assist with text recognition tasks in different languages
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3 903 people in this dataset. Each person took a selfie on a webcam, took a selfie on a mobile phone
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THE FINAL COST OF THE PROJECT IS INFLUENCED BY:
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Markup complexity
Timing
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Our data quality guarantee is 95%. When ordering markup with quality above 95%, we offer enterprise solutions
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