EasyCard – AI Model by ZON8955 | AlphaNeural AI

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ZON8955/
EasyCard

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text-classification

generated_from_trainer

google-bert/bert-base-chinese

text-embeddings-inference

endpoints_compatible

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EasyCard

This model is a fine-tuned version of bert-base-chinese on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0046

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
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: 10

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	5	0.0792
0.4505	2.0	10	0.0441
0.4505	3.0	15	0.0246
0.1388	4.0	20	0.0155
0.1388	5.0	25	0.0098
0.0600	6.0	30	0.0075
0.0600	7.0	35	0.0087
0.0358	8.0	40	0.0065
0.0358	9.0	45	0.0050
0.0260	10.0	50	0.0046

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2