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bert-base-uncased-google-boolq – AI Model by pranay-j | AlphaNeural AI
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pranay-j
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bert-base-uncased-google-boolq
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transformers
tensorboard
safetensors
bert
text-classification
generated_from_trainer
en
google/boolq
google-bert/bert-base-uncased
finetune
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
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Bert Base Uncased Boolean Question Answer model
This model is a fine-tuned version of
bert-base-uncased
on the boolq dataset. It achieves the following results on the evaluation set:
Loss: 0.1993
Accuracy: 0.7150
Model description
Model type:
Text Classification model
Language(s) (NLP):
English
License:
Apache 2.0
Intended uses & limitations
More information needed
Training and evaluation data
Dataset
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
gradient_accumulation_steps: 4
total_train_batch_size: 64
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
Accuracy
0.2317
0.9966
147
0.2198
0.6569
0.2
2.0
295
0.2002
0.6960
0.1741
2.9966
442
0.1968
0.7122
0.1469
3.9864
588
0.1993
0.7150
Framework versions
Transformers 4.40.0
Pytorch 2.2.2+cu121
Datasets 2.19.0
Tokenizers 0.19.1
