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Q9-PHQ – AI Model by ishwarbb23 | AlphaNeural AI
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ishwarbb23
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Q9-PHQ
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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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Model card
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Q9-PHQ
This model is a fine-tuned version of
distilbert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.3267
Accuracy: 0.8875
Mcc: 0.6295
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
Accuracy
Mcc
No log
1.0
51
0.4744
0.8075
0.0
No log
2.0
102
0.3437
0.8825
0.5807
No log
3.0
153
0.3191
0.8825
0.6130
No log
4.0
204
0.3412
0.8725
0.6176
No log
5.0
255
0.3267
0.8875
0.6295
Framework versions
Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1
