README.md

metadata

library_name: transformers
license: mit
base_model: avinasht/deberta-v3-base-Label_B-768-epochs-5
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: >-
      Dblefinetune_Noisy-deberta-v3-base-Label_B-768-epochs-5-Label_B-768-epochs-1
    results: []

Dblefinetune_Noisy-deberta-v3-base-Label_B-768-epochs-5-Label_B-768-epochs-1

This model is a fine-tuned version of avinasht/deberta-v3-base-Label_B-768-epochs-5 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1098
• Accuracy: 0.9832
• F1: 0.9832
• Precision: 0.9836
• Recall: 0.9832

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: 12
• eval_batch_size: 12
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 48
• optimizer: Use 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: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.0002	0.9995	1066	0.1098	0.9832	0.9832	0.9836	0.9832

Framework versions

• Transformers 4.46.3
• Pytorch 2.4.0
• Datasets 3.1.0
• Tokenizers 0.20.3