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Nicknotname/IntentDetection

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face
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Model Card

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Nicknotname/IntentDetection

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Train Loss: 0.0024
  • Validation Loss: 0.0013
  • Train Accuracy: 1.0
  • Epoch: 1

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • optimizer: {'name': 'Adam', 'weightdecay': None, 'clipnorm': None, 'globalclipnorm': None, 'clipvalue': None, 'useema': False, 'emamomentum': 0.99, 'emaoverwritefrequency': None, 'jitcompile': False, 'islegacyoptimizer': False, 'learningrate': {'module': 'keras.optimizers.schedules', 'classname': 'PolynomialDecay', 'config': {'initiallearningrate': 2e-05, 'decaysteps': 522, 'endlearningrate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registeredname': None}, 'beta1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
  • training_precision: float32

Training results

Train LossValidation LossTrain AccuracyEpoch
0.07340.00301.00
0.00240.00131.01

Framework versions

  • Transformers 4.45.0
  • TensorFlow 2.16.1
  • Datasets 2.20.0
  • Tokenizers 0.20.0