Simple and Lightweight Text Classifiers with LLM Embeddings
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Updated
Jul 10, 2024 - Python
Simple and Lightweight Text Classifiers with LLM Embeddings
Source code of our KDD 2024 paper "Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive Learning"
Multilingual Deception Detection of GPT-generated Hotel Reviews
Tencent Pre-training framework in PyTorch & Pre-trained Model Zoo
Persian text emotion recognition using BERT-based models.
notebooks to finetune `bert-small-amharic`, `bert-mini-amharic`, and `xlm-roberta-base` models using an Amharic text classification dataset and the transformers library
Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo
Проект в рамках ВКР под названием "Разработка программного модуля для анализа документов, подтверждающих индивидуальные достижения"
This project explores the foundational concepts of ML, NLP, and model optimization to develop an efficient and user-friendly healthcare solution.
🤖 A PyTorch library of curated Transformer models and their composable components
Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing
Currently running NLP project about political communication on Twitter. You can find more projects in my portfolio.
ZaBantu is a fleet of light-weight Masked Language Models for Southern Bantu Languages
Punctuation Restoration for Khmer language
ML and Natual Language Processing
Fine tuned BERT, mBERT and XLMRoBERTa for Abusive Comments Detection in Telugu, Code-Mixed Telugu and Telugu-English.
This repository is a comprehensive project that leverages the XLM-Roberta model for intent detection. This repository is a valuable resource for developers looking to build and fine-tune intent detection models based on state-of-the-art techniques.
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.
THREATENING_TEXT_DETECTION_USING_CNN_LSTM_BILSTM_XLMROBERTA
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