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t5-efficient-base-usdjpy-forecaster – AI Model by AbdelrehmanFouad | AlphaNeural AI
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t5-efficient-base-usdjpy-forecaster
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transformers
safetensors
t5
text2text-generation
trackio
https
generated_from_trainer
google/t5-efficient-base
finetune
apache-2.0
text-generation-inference
endpoints_compatible
us
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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
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
training_steps: 1000
Training results
Training Loss
Epoch
Step
Validation Loss
4.0665
0.8430
200
1.0427
3.4826
1.6828
400
0.9274
3.1112
2.5227
600
0.9012
2.9113
3.3625
800
0.8955
2.8138
4.2023
1000
0.8988
Framework versions
Transformers 5.2.0
Pytorch 2.10.0+cu128
Datasets 4.6.0
Tokenizers 0.22.2
