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cfr-qa-model – AI Model by AsherArmstrong | AlphaNeural AI
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AsherArmstrong
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cfr-qa-model
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transformers
tensorboard
safetensors
distilbert
question-answering
generated_from_trainer
distilbert/distilbert-base-uncased
finetune
apache-2.0
endpoints_compatible
us
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cfr-qa-model
This model is a fine-tuned version of
distilbert/distilbert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 2.9458
Exact Match: 0.0
F1: 0.0
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: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Exact Match
F1
No log
1.0
5
4.7252
0.0
0.0
No log
2.0
10
3.5616
0.0
0.0
No log
3.0
15
2.9458
0.0
0.0
Framework versions
Transformers 4.55.0
Pytorch 2.6.0+cu124
Datasets 4.0.0
Tokenizers 0.21.4
