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Bert-QA – AI Model by DiDiR6 | AlphaNeural AI
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DiDiR6
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Bert-QA
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transformers
tensorboard
safetensors
bert
question-answering
generated_from_trainer
google-bert/bert-base-multilingual-cased
finetune
apache-2.0
endpoints_compatible
us
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Bert-QA
This model is a fine-tuned version of
google-bert/bert-base-multilingual-cased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 1.0662
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: 3
Training results
Training Loss
Epoch
Step
Validation Loss
0.9847
1.0
8271
0.9703
0.7136
2.0
16542
0.9771
0.5174
3.0
24813
1.0662
Framework versions
Transformers 4.40.1
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.19.1
