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Dave12121/Fsentiment

This model is a fine-tuned version of distilbert-base-uncased on the financial phrasebank sentences all agree dataset.

  • Train Loss: 0.0517
  • Validation Loss: 0.2117
  • Train Accuracy: 0.9384
  • Epoch: 2

It achieves an macro f1 score on the validation set financial phrasebank sentences 75% of: 0.92

The testing data is a data subset of the finantial phrasebank in which 66% annotators agreed on the label.

The reported macro f1 score on the test set is: 0.86

Model description

More information needed

Intended uses & limitations

Model should be treated with care. A simple review showed first signs of gender bias within the model.

Training and evaluation data

More information needed

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': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 705, '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
0.5028 0.3128 0.8904 0
0.1137 0.2117 0.9375 1
0.0517 0.2117 0.9384 2

Framework versions

  • Transformers 4.35.0
  • TensorFlow 2.11.1
  • Datasets 2.14.6
  • Tokenizers 0.14.1
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Finetuned from

Dataset used to train Dave12121/Fsentiment