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- walidbosso Roberta_Fine_Tuning_Sentiment_Analysis - GitHub
Application in Sentiment Analysis: Explore how we applied RoBERTa to sentiment analysis, specifically on a dataset of movie reviews Environment Setup: Learn about the libraries and tools we used, including PyTorch, Transformers, and the Hugging Face library
- Sentiment analysis of movie reviews: A flask application using CNN with . . .
In this work, we analyze sentiment in IMDB movie reviews using a hybrid deep learning model combining RoBERTa embeddings with a convolutional neural network (R-CNN)
- Sentiment_Analysis_Movie_Reviews. ipynb - Colab
Notebook to train an XLNet model to perform sentiment analysis The dataset used is a balanced collection of (50,000 - 1:1 train-test ratio) IMDB movie reviews with binary labels: postive
- Sentiment Analysis on IMDB Reviews - kshubham96. github. io
Overview : Extracted and analyzed 50K IMDb movie reviews to classify sentiments, with a focus on both positive and negative feedback
- Sentiment Analysis using RoBERTa to train your model.
In this guide, I will walk you through a step-by-step process of performing sentiment analysis using a pre-trained RoBERTa model, specifically the one available from Hugging Face
- Fine-Tuning RoBERTa with Adapters for Sentiment Analysis . . . - GitHub
This project showcases an advanced approach to sentiment analysis by fine-tuning the RoBERTa pretrained model from Hugging Face's Transformers library The goal is to classify movie reviews as either positive or negative, reflecting the sentiment conveyed in the text
- IMDB Sentiment Analysis | IMDB_Sentiment_Analysis
The goal of this project is to build and compare different neural network models for sentiment analysis on the IMDB dataset The models are designed to classify movie reviews as either positive or negative
- sentiment-analysis-using-roberta. ipynb - Colab
We will be creating a neural network with the RobertaClass This network will have the Roberta Language model followed by a dropout and finally a Linear layer to obtain the final outputs
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