A sentiment analysing web application for customer reviews. Positive, Negative and Neutral opinions are highlighted.
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Updated
Jan 9, 2022 - Python
A sentiment analysing web application for customer reviews. Positive, Negative and Neutral opinions are highlighted.
I will perform a text classification tast using various Transformer like BERT, DistilBERT, ElLECTRA models, mostly from Huggingface community
Thesis Project
1. Fine-tune DistilBERT on NLI and dentify the some salient or toxic features that the model learnt. 2. Sample annotations techniques and production of silver label using (EDA and Back Translation).
Fake news classification transformer model
🏥 Dr.Jarvis is a medical transcript classifier that helps patients to get their symptoms diagnosed in real-time on a Streamlit-powered web app. Trained by SVM, KNN, and Random Forest models of sklearn.
Deploying a pretrained distilBERT model with SageMaker
Kaggle Competition
Transformers
Sentiment Analysis On Stanford Dataset using State-of-the-Art models (Contextualized Embedding)
Multi-modal retrieval with a smooth weighting of negatives
Sentiment analysis on song lyrics using DistilBERT for NLP
Classification of German noun compounds based on the semantic relation between their constituents.
This is a production ready DistilBERT Sentiment Analysis model for service reviews designed to work as a low cost market research tool with the nuiance of an actual market researcher.
This repository contains the Romanian version of DistilBERT.
I performed sentiment analysis aimed at determining the sentiment of 50000 imDB movie reviews, whether they are positive, negative, or neutral. I employed various NLP approaches including lexicon based approaches, machine learning models, PLM models, and hybrid models, and assessed the performance on each type of model.
Summarize any text using the distilbart-cnn-12-6 model under the hood
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