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Running
| Sentiment Analysis | |
| --- | |
| ### Usage | |
| 1. Clone the repository | |
| 2. `cd` into the repository | |
| 3. Run `just install` to install the dependencies | |
| 4. Run `just run --help` to see the available commands | |
| ### Datasets | |
| - [Sentiment140](https://www.kaggle.com/datasets/kazanova/sentiment140) | |
| - [IMDb](https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews) | |
| - [Amazon Reviews](https://www.kaggle.com/datasets/bittlingmayer/amazonreviews) | |
| ### Required tools | |
| - `just` | |
| - `poetry` | |
| ### TODO | |
| - [ ] CLI using `click` (commands: predict, train, evaluate) with settings set via flags or environment variables | |
| - [ ] GUI using `gradio` (tabs: predict, train, evaluate, compare, settings) | |
| - [ ] For the sklearn model, add more classifiers | |
| - [ ] Use random search for hyperparameter tuning and grid search for fine-tuning | |
| - [ ] Finish the text pre-processing transformer | |
| - [ ] For vectorization, use custom stopwords | |
| - [ ] Write own tokenizer/vectorizer | |
| - [ ] Add more datasets | |
| - [ ] Add more models (e.g. BERT) | |
| - [ ] Write tests | |
| - [ ] Use xgboost? | |
| - [ ] Deploy to huggingface? | |