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How_to_fine-tune_a_model_for_common_downstream_tasks_V3 – AI Model by Tural | AlphaNeural AI
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How_to_fine-tune_a_model_for_common_downstream_tasks_V3
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transformers
pytorch
bert
question-answering
generated_from_trainer
squad
google-bert/bert-base-uncased
finetune
apache-2.0
endpoints_compatible
us
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How_to_fine-tune_a_model_for_common_downstream_tasks_V3
This model is a fine-tuned version of
bert-base-uncased
on the squad dataset. It achieves the following results on the evaluation set:
Loss: 1.0517
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: 24
eval_batch_size: 32
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
1.1053
1.0
3650
1.0316
0.8401
2.0
7300
0.9943
0.6316
3.0
10950
1.0517
Framework versions
Transformers 4.34.0
Pytorch 2.0.0
Datasets 2.14.5
Tokenizers 0.14.1
