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checkpoint – AI Model by DeanAvI | AlphaNeural AI | AlphaNeural AI
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checkpoint
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transformers
safetensors
bert
text-classification
generated_from_trainer
prajjwal1/bert-tiny
finetune
mit
text-embeddings-inference
endpoints_compatible
us
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checkpoint
This model is a fine-tuned version of
prajjwal1/bert-tiny
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.5185
Accuracy: 0.7411
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: 64
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 0.1
num_epochs: 5
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.6544
1.0
134
0.6237
0.6867
0.5687
2.0
268
0.5582
0.7148
0.5196
3.0
402
0.5304
0.7298
0.4979
4.0
536
0.5183
0.7373
0.4982
5.0
670
0.5179
0.7326
Framework versions
Transformers 5.1.0
Pytorch 2.10.0+cu128
Datasets 4.5.0
Tokenizers 0.22.2
