--- license: mit library_name: sklearn tags: - sklearn - skops - tabular-classification model_file: HME_pickle widget: structuredData: anger: - 0.13340177 - 0.26429585 - 0.75805366 disgust: - 0.07661828 - 0.14570697 - 0.21044387 fear: - 0.094705686 - 0.057977196 - 0.003689876 joy: - 0.006762238 - 0.2627153 - 0.001755206 neutral: - 0.03295978 - 0.019884355 - 0.013996695 sadness: - 0.6507381 - 0.24445744 - 0.011482558 surprise: - 0.004814104 - 0.00496282 - 0.000578273 --- # Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters.
Click to expand | Hyperparameter | Value | |------------------|---------| | alpha | 1 | | class_prior | | | fit_prior | 1 | | norm | 0 |
### Model Plot The model plot is below.
ComplementNB()
Please rerun this cell to show the HTML repr or trust the notebook.
## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |----------|----------| | accuracy | 0.536424 | | f1 score | 0.536424 | # How to Get Started with the Model [More Information Needed] # Model Card Authors This model card is written by following authors: [More Information Needed] # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ``` # citation_bibtex bibtex @inproceedings{...,year={2022}} # model_card_authors skops_user # limitations This model is purely for academic purposes. # model_description This is a Complement NB model trained on a poetry dataset. # eval_method The model is evaluated using test split, on accuracy and F1 score with macro average.