distilbert_classifier_newsgroup

Training and evaluation data

• The training set contained 10182 rows with features - text and label.
• The evaluation set contained 7532 rows with features - text and label.

Training procedure

• Setup the model checkpoint as distilbert-base-uncased
• Intialize the starting weights with the model checkpoint and give it the number of labels - i.e., 20 in this case.
• Will be training for 3 epochs
• Using a batch size of 16

Training hyperparameters

• optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

• Results on the evaluation set: loss: 0.5660 - accuracy: 0.8371

Framework versions

• Transformers 4.28.0
• TensorFlow 2.12.0
• Datasets 2.12.0
• Tokenizers 0.13.3