The model was trained on the SetFit/Subj dataset. The dataset, I believe, contains plot lines from movies and reviews. The former are all labeled as "objective" while the latter "subjective".

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

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': 2500, '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

Train Loss	Validation Loss	Train Accuracy	Epoch
0.1849	0.1106	0.9595	0
0.0652	0.1081	0.9625	1
0.0234	0.1242	0.965	2

Framework versions

Transformers 4.30.2
TensorFlow 2.12.0
Datasets 2.13.1
Tokenizers 0.13.3

duyvt6663/RoBERTa_Subj

RoBERTa_Subj

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

duyvt6663/
RoBERTa_Subj