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BERT_QA_model – AI Model by Jahanzeb1 | AlphaNeural AI
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BERT_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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BERT_QA_model
This model is a fine-tuned version of
distilbert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 1.6925
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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4
Training results
Training Loss
Epoch
Step
Validation Loss
No log
1.0
250
2.3905
2.7186
2.0
500
1.7778
2.7186
3.0
750
1.6786
1.1856
4.0
1000
1.6925
Framework versions
Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.17.0
Tokenizers 0.15.2
