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debertabase_term_paper – AI Model by muratti18462 | AlphaNeural AI
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muratti18462
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debertabase_term_paper
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transformers
tensorboard
safetensors
deberta-v2
token-classification
generated_from_trainer
microsoft/deberta-v3-base
finetune
mit
autotrain_compatible
endpoints_compatible
us
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debertabase_term_paper
This model is a fine-tuned version of
microsoft/deberta-v3-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.1196
Precision: 0.9279
Recall: 0.9363
F1: 0.9320
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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 3
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Precision
Recall
F1
1.2937
1.0
789
0.1477
0.9083
0.9224
0.9151
1.2937
2.0
1578
0.1311
0.9227
0.9322
0.9270
1.2937
3.0
2367
0.1196
0.9279
0.9363
0.9320
Framework versions
Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.2
