Source code for ISPRAS-2021 journal paper "Language Models Application in Sentiment Attitude Extraction Task" (in Russian)
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Updated
Aug 12, 2021 - Python
Source code for ISPRAS-2021 journal paper "Language Models Application in Sentiment Attitude Extraction Task" (in Russian)
Fine tuning de modèle de pré-entrainement BERT pour classification des sentiments des commentaires des clients
A web server to host Google BERT trained model
Clinical Notes Model for medaCy (BERT)
Identify and classify toxic online comments,based on kaggle dataset
hate speech detection using weakly supervised methods.
An analysis model that classifies twitter tweets using BERT and Tensor Flow libraries.
The semantic volatility of neologisms.
University project on Deep Learning
Multi-Label Classification using BERT and XLNet
Created a robust method to prioritize reviews and evaluate responses by extracting the emotion associated with the review using deep learning models.
Developed a machine learning algorithm using Bidirectional Encoder Representations from Transformers (BERT) for email spam detection
In this Machine Learning project, I harness the power of advanced algorithms to effectively categorize resumes found within a specified directory. Through an intricate analysis of their content, the script automates the process of sorting these resumes, thereby enhancing and streamlining the hiring process. 🚀
Text summarising is the process of producing a brief, coherent summary of a text while keeping its essential details and primary concepts.
Sentiment Analysis of imdb movie comments, implemented by transformers (Using Pytorch)
Named entity recognition for Clinical records. Using MultiHead-Bert + CRF
Ensemble Network Including Transformer Models for NLP Patient Text and ED Visit Prediction
Attempt to use BERT pre-trained model fine-tuned for medical purposes to classify various symptoms
The goal of this project is to develop a Named Entity Recognition (NER) system that can identify and classify named entities (such as names of people, organizations, locations, dates, etc.) in a given text using the BERT model from Hugging Face's Transformers library.
Multimodal Model which take text audio and video to predict the turn taking. That is, to predict whether the speaker in a discussion will change.
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