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geocoder_relevancy_model – AI Model by azamat | AlphaNeural AI
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geocoder_relevancy_model
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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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geocoder_model
This model is a fine-tuned version of
bert-base-multilingual-cased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.2632
Accuracy: {'accuracy': 0.9005447386872337}
F1: {'f1': 0.8323636363636362}
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: 2
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
0.26
1.0
4636
0.2405
{'accuracy': 0.8972547327544361}
{'f1': 0.827866630523177}
0.2069
2.0
9272
0.2632
{'accuracy': 0.9005447386872337}
{'f1': 0.8323636363636362}
Framework versions
Transformers 4.25.1
Pytorch 1.13.0+cu116
Tokenizers 0.13.2
