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bert-base-uncased-qnli – AI Model by JeremiahZ | AlphaNeural AI
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JeremiahZ
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bert-base-uncased-qnli
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transformers
pytorch
tensorboard
safetensors
bert
text-classification
generated_from_trainer
en
glue
google-bert/bert-base-uncased
finetune
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
us
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bert-base-uncased-qnli
This model is a fine-tuned version of
bert-base-uncased
on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
Loss: 0.3208
Accuracy: 0.9125
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: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.289
1.0
3274
0.2289
0.9094
0.1801
2.0
6548
0.2493
0.9118
0.1074
3.0
9822
0.3208
0.9125
Framework versions
Transformers 4.20.0.dev0
Pytorch 1.11.0+cu113
Datasets 2.1.0
Tokenizers 0.12.1
