Sentiment Analysis with BERT and RoBERTa #933
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This project implements sentiment analysis using state-of-the-art transformer models, BERT and RoBERTa. The aim is to classify text data into positive and negative sentiments by fine-tuning pre-trained models on a custom dataset. The project includes data preprocessing, model training, and evaluation metrics such as accuracy, precision, recall, and F1 score, allowing for a comparative analysis of the two models' performance in sentiment classification tasks.
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Our team will soon review your PR. Thanks @sagar27sahu :) |
abhisheks008
requested changes
Oct 20, 2024
There was a problem hiding this comment.
Hi @sagar27sahu
- As this is an enhancement work, you don't have to create a new project folder. Check out the existing project folder and add your
ipynbfiles there with the implemented model names. - Do not use
.pyfiles, useipynbfiles only. - Update the
README.mdin the existing project folder as per your contributions.
Hope this is clear to you.
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@abhisheks008 i have resolved the issue,please check and please consider increasing label1 to label2 |
abhisheks008
requested changes
Oct 25, 2024
There was a problem hiding this comment.
- Remove the unnecessary files (
.DS_Store,.ideaand so on). - You were asked to not to change/update the existing project
.ipynbfile, still you have updated that. - No need to create a separate
README.md. In the existingREADME.mdmention your implemented models' details, as per the existing points of the README. - Update the file name as
requirements.txt, it's not a.docxfile.
Please have a look at the requested changes for this pull request.
@sagar27sahu
i can't find the .Ds_store file in folder .may be it is environment generated please check again.rest all are changed.
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i have add the file to git ignore ,so that it would be disabled.it was a file generated by mac os.please verify it
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Pull Request for DL-Simplified 💡
This project implements sentiment analysis using state-of-the-art transformer models, BERT and RoBERTa. The aim is to classify text data into positive and negative sentiments by fine-tuning pre-trained models on a custom dataset. The project includes data preprocessing, model training, and evaluation metrics such as accuracy, precision, recall, and F1 score, allowing for a comparative analysis of the two models' performance in sentiment classification tasks.
Issue Title :
JWOC Participant) <hacktoberfest and gssoc-ext 2024>Closes: #588
Describe the add-ons or changes you've made 📃
i have made sentiment analysis using Bert and Robert
Type of change ☑️
What sort of change have you made: