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bert-base-uncased-with-misspellings-correction – AI Model by mnicamartins8 | AlphaNeural AI | AlphaNeural AI
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bert-base-uncased-with-misspellings-correction
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transformers
pytorch
tensorboard
bert
text-classification
generated_from_trainer
apache-2.0
autotrain_compatible
endpoints_compatible
us
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bert-base-uncased-with-misspellings-correction
This model is a fine-tuned version of
bert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.2307
Accuracy: 0.9023
Precision: 0.9090
Recall: 0.9023
F1: 0.9045
Balanced Acc: 0.8858
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: 3e-05
train_batch_size: 32
eval_batch_size: 32
seed: 0
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1
Training results
Framework versions
Transformers 4.29.2
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3
