- The project was worked in a team of two while participating in Kaggle's Summer 2020 challenge named Tweet Sentiment Extraction.
- A model was required to be made which would extract phrases from a given tweet which reflects a given sentiment.
- Hugging Face's DistilBERT was used to train the model. The data for which was downloaded from Kaggle itself.
- A question answering model was made using the above-mentioned model while applying some pre-processing and post-processing methods inferred from the results and the given dataset.
- An accuracy of 70.636 was achieved in the final private leaderboard.
-
Notifications
You must be signed in to change notification settings - Fork 0
banagg/Kaggle-TSE
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
About
Kaggle's Tweet Sentiment Extraction challenge. Model had to extract phrases out of a tweet which maximise a given sentiment.
Topics
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published