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gpt_bio_gen

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README

License: apache-2.0

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-06
train_batch_size: 128
eval_batch_size: 128
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: 20

Training results

Table with columns: Training Loss, Epoch, Step, Validation Loss, Accuracy, F1
Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.3235	1.0	217	0.2289	0.9020	0.3235
0.2106	2.0	434	0.1942	0.9131	0.5709
0.1892	3.0	651	0.1853	0.9154	0.6031
0.1768	4.0	868	0.1830	0.9170

Framework versions

Transformers 5.12.1
Pytorch 2.11.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

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Model Details

Model Provider

specialsaucem

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Base

distilbert/distilbert-base-uncased

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Output Modalities

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Supported Functionality

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README

License: apache-2.0

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-06
train_batch_size: 128
eval_batch_size: 128
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: 20

Training results

Table with columns: Training Loss, Epoch, Step, Validation Loss, Accuracy, F1
Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.3235	1.0	217	0.2289	0.9020	0.3235
0.2106	2.0	434	0.1942	0.9131	0.5709
0.1892	3.0	651	0.1853	0.9154	0.6031
0.1768	4.0	868	0.1830	0.9170

Framework versions

Transformers 5.12.1
Pytorch 2.11.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2