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bert-term-importance-v0 – AI Model by nikmandava | AlphaNeural AI
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bert-term-importance-v0
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transformers
safetensors
bert
token-classification
generated_from_trainer
google-bert/bert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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bert-term-importance-v0
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: 0.0022
Mse: 0.0021
Rmse: 0.0463
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: Use 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
Training results
Training Loss
Epoch
Step
Validation Loss
Mse
Rmse
0.0034
1.0
675
0.0045
0.0044
0.0662
0.0036
2.0
1350
0.0044
0.0043
0.0653
0.0034
3.0
2025
0.0044
0.0043
0.0652
Framework versions
Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.5.1
Tokenizers 0.21.0
