Using object oriented programming for training the model. Different scikit models are trained with one command.
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This repository contains sentiment classification model , which takes text as input and outputs the whether the statement is positive or negative comment.
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The app.py is main file of webapp deployed on huggingface, uses streamlit framework as ui.
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Try the app here - https://huggingface.co/spaces/SSahas/sentiment_classifier_airline
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The fast_api_swagger.py is code to create restful api using fastapi and uvicorn as server, swagger documentation is integrated with it, takes statement as input and outputs the prediction in the form of json.
| mean_fit_time | std_fit_time | mean_score_time | std_score_time | params | mean_test_score | std_test_score | model_name |
|---|---|---|---|---|---|---|---|
| 75.238871 | 7.649687 | 1.116081 | 0.396170 | {'alpha': 0.9} | 0.887878 | 0.010712 | Ridge_classifier |
| 32.970682 | 19.089618 | 0.537239 | 0.069481 | {'alpha': 1.0} | 0.887532 | 0.011108 | Ridge_classifier |
| 59.446653 | 4.041303 | 0.925700 | 0.067038 | {'alpha': 0.8} | 0.887098 | 0.010756 | Ridge_classifier |
| 4.578112 | 0.228420 | 0.208683 | 0.015610 | {'max_depth': None, 'n_estimators': 10} | 0.842732 | 0.049028 | Randomforestclassifier |
| 33.682326 | 6.913221 | 0.231637 | 0.023633 | {'alpha': 9.5e-05} | 0.838400 | 0.021505 | SGDClassifier |
| 56.504217 | 3.619727 | 0.524913 | 0.152613 | {'max_depth': None, 'n_estimators': 150} | 0.833893 | 0.096404 | Randomforestclassifier |
| 1.457530 | 0.724146 | 0.263000 | 0.058040 | {'alpha': 0.5} | 0.830604 | 0.016622 | MultinomialNB |
| 40.424593 | 12.100794 | 0.216021 | 0.011447 | {'alpha': 8e-05} | 0.830603 | 0.014611 | SGDClassifier |