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bert-mrpc-equi-I – AI Model by KRB1975 | AlphaNeural AI
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KRB1975
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bert-mrpc-equi-I
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transformers
tensorboard
safetensors
bert
text-classification
generated_from_trainer
sentence-similarity
google-bert/bert-base-uncased
finetune
apache-2.0
autotrain_compatible
text-embeddings-inference
endpoints_compatible
us
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bert-mrpc-equi-I
This model is a fine-tuned version of
bert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.7809
Accuracy: 0.8382
F1: 0.8870
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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3.0
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
No log
1.0
459
0.5148
0.7770
0.8580
0.5043
2.0
918
0.5140
0.8456
0.8927
0.2697
3.0
1377
0.7809
0.8382
0.8870
Framework versions
Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1
