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AbdelrehmanFouad/t5-efficient-base-usdjpy-forecaster

sourceHugging Faceapache-2.0updated 7mo agoView on Hugging Face
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t5-efficient-base-usdjpy-forecaster

This model is a fine-tuned version of google/t5-efficient-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8988

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:

  • learning_rate: 0.0001
  • trainbatchsize: 4
  • evalbatchsize: 8
  • seed: 42
  • gradientaccumulationsteps: 4
  • totaltrainbatch_size: 16
  • optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lrschedulertype: linear
  • training_steps: 1000

Training results

Training LossEpochStepValidation Loss
4.06650.84302001.0427
3.48261.68284000.9274
3.11122.52276000.9012
2.91133.36258000.8955
2.81384.202310000.8988

Framework versions

  • Transformers 5.2.0
  • Pytorch 2.10.0+cu128
  • Datasets 4.6.0
  • Tokenizers 0.22.2