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2023_AI_HW_001 – AI Model by JessCatWu | AlphaNeural AI
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2023_AI_HW_001
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transformers
tensorboard
safetensors
distilbert
text-classification
generated_from_trainer
distilbert/distilbert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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2023_AI_HW_001
This model is a fine-tuned version of
distilbert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.8709
Matthews Correlation: 0.5435
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: 5
Training results
Training Loss
Epoch
Step
Validation Loss
Matthews Correlation
0.5139
1.0
535
0.4551
0.4542
0.3388
2.0
1070
0.4634
0.5256
0.2334
3.0
1605
0.6328
0.5256
0.1635
4.0
2140
0.7833
0.5378
0.1169
5.0
2675
0.8709
0.5435
Framework versions
Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0
