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multilabel_disease_bert_classification – AI Model by rseeto | AlphaNeural AI
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rseeto
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multilabel_disease_bert_classification
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peft
tensorboard
safetensors
adapter
lora
transformers
google-bert/bert-base-uncased
adapter
apache-2.0
us
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bert-base-uncased
This model is a fine-tuned version of
bert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: nan
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: 16
eval_batch_size: 16
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
num_epochs: 8
Training results
Training Loss
Epoch
Step
Validation Loss
No log
1.0
60
nan
0.3696
2.0
120
nan
0.3696
3.0
180
nan
0.1117
4.0
240
nan
0.0942
5.0
300
nan
0.0942
6.0
360
nan
0.0894
7.0
420
nan
0.0894
8.0
480
nan
Framework versions
PEFT 0.18.0
Transformers 4.57.2
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1
