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distilbert-base-uncased-finetuned-advers – AI Model by T-qualizer | AlphaNeural AI
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distilbert-base-uncased-finetuned-advers
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transformers
pytorch
tensorboard
distilbert
question-answering
generated_from_trainer
adversarial_qa
apache-2.0
endpoints_compatible
us
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distilbert-base-uncased-finetuned-advers
This model is a fine-tuned version of
distilbert-base-uncased
on the adversarial_qa dataset. It achieves the following results on the evaluation set:
Loss: 3.6462
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: 9e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 3000
Training results
Training Loss
Epoch
Step
Validation Loss
3.6424
0.18
3000
3.6462
Framework versions
Transformers 4.17.0
Pytorch 1.10.0+cu111
Datasets 1.18.4
Tokenizers 0.11.6
