https://github.com/alessandromonolo/descriptive-texts-classification-by-usage-purposes-of-estate-properties
The project aims to identify the best model for the classification of texts derived from descriptions of assets subject to Italian judicial auctions. The employed models include both conventional models, such as Logistic Regression, Naive Bayes, SVM, and XGBoost, and neural network models, such as Fasttext and XLM-Roberta.
https://github.com/alessandromonolo/descriptive-texts-classification-by-usage-purposes-of-estate-properties
fasttext logistic-regression naive-bayes nlp python pytorch scikit-learn seaborn spacy svm text-classification tfidf tokenizer xgboost xlm-roberta
Last synced: 8 months ago
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The project aims to identify the best model for the classification of texts derived from descriptions of assets subject to Italian judicial auctions. The employed models include both conventional models, such as Logistic Regression, Naive Bayes, SVM, and XGBoost, and neural network models, such as Fasttext and XLM-Roberta.
- Host: GitHub
- URL: https://github.com/alessandromonolo/descriptive-texts-classification-by-usage-purposes-of-estate-properties
- Owner: alessandromonolo
- Created: 2023-12-21T15:55:18.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-22T15:54:01.000Z (almost 2 years ago)
- Last Synced: 2024-01-29T08:48:23.329Z (over 1 year ago)
- Topics: fasttext, logistic-regression, naive-bayes, nlp, python, pytorch, scikit-learn, seaborn, spacy, svm, text-classification, tfidf, tokenizer, xgboost, xlm-roberta
- Language: Jupyter Notebook
- Homepage:
- Size: 14.3 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Thesis Project Master Data Science & Artificial Intelligence at Politecnico di Milano
The project aims to identify the best model for the classification of texts derived from descriptions of assets subject to Italian judicial auctions.
The employed models include conventional models, such as Logistic Regression, Naive Bayes, SVM, and XGBoost, and neural network models such as Fasttext and XLM-Roberta.