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transient_data – AI Model by Jingni | AlphaNeural AI
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Jingni
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transient_data
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transformers
safetensors
distilbert
text-classification
generated_from_trainer
distilbert/distilbert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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transient_data
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: 0.7541
Accuracy: 0.7980
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: 256
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
No log
1.0
204
0.1647
0.9579
No log
1.0
204
0.6233
0.8001
No log
2.0
408
0.1083
0.9699
No log
2.0
408
0.7254
0.7910
0.2523
3.0
612
0.0809
0.9797
0.2523
3.0
612
0.7541
0.7980
Framework versions
Transformers 4.38.1
Pytorch 2.2.1+cpu
Datasets 2.17.1
Tokenizers 0.15.2
