In the Comparing Models notebook, we’re evaluating
en.sentiment.imdb.glove model from JSL and
lvwerra/distilbert-imdb from HF which are models trained for text classification task. The notebook showcases how we can get a view of how the models compare. The final report output is different than normal and includes both models’s results.
Open in Collab
|Category||Hub||Task||Open In Colab|
|Multiple Model Comparison||Hugging Face/John Snow Labs/Spacy||NER/Text-Classification|
tests: defaults: min_pass_rate: 1.0 robustness: add_typo: min_pass_rate: 0.7 american_to_british: min_pass_rate: 0.7 accuracy: min_micro_f1_score: min_score: 0.7 bias: replace_to_female_pronouns: min_pass_rate: 0.7 replace_to_low_income_country: min_pass_rate: 0.7 fairness: min_gender_f1_score: min_score: 0.6 representation: min_label_representation_count: min_count: 50