roberta-base_topic_classification_nyt_news

This model is a fine-tuned version of roberta-base on the NYT News dataset, which contains 256,000 news titles from articles published from 2000 to the present (https://www.kaggle.com/datasets/aryansingh0909/nyt-articles-21m-2000-present). It achieves the following results on the test set of 51200 cases: - Accuracy: 0.91 - F1: 0.91 - Precision: 0.91 - Recall: 0.91 class |Description -|- 0 |Sports 1 |Arts, Culture, and Entertainment 2 |Business and Finance 3 |Health and Wellness 4 |Lifestyle and Fashion 5 |Science and Technology 6 |Politics 7 |Crime The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5

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