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bert-large-uncased-whole-word-masking-finetuned-policy-named-insured – AI Model by Ineract | AlphaNeural AI
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Ineract
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bert-large-uncased-whole-word-masking-finetuned-policy-named-insured
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transformers
pytorch
tensorboard
bert
question-answering
generated_from_trainer
policies-named-insured
apache-2.0
endpoints_compatible
us
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bert-large-uncased-whole-word-masking-finetuned-policy-named-insured
This model is a fine-tuned version of
bert-large-uncased-whole-word-masking-finetuned-squad
on the policies-named-insured dataset. It achieves the following results on the evaluation set:
Loss: 0.0264
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
No log
1.0
478
0.0402
0.0557
2.0
956
0.0326
0.0106
3.0
1434
0.0264
Framework versions
Transformers 4.25.1
Pytorch 1.12.1
Datasets 2.8.0
Tokenizers 0.13.2
