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metadata
license: mit
base_model: microsoft/deberta-v3-small
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: DeBERTaV3_model_multilabel
    results: []

DeBERTaV3_model_multilabel

This model is a fine-tuned version of microsoft/deberta-v3-small on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0221
  • Accuracy: 0.9919
  • F1: 0.3922
  • Precision: 0.6667
  • Recall: 0.2778

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: 5
  • eval_batch_size: 5
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
No log 1.0 25 0.4442 0.9516 0.1475 0.0884 0.4444
No log 2.0 50 0.1757 0.9919 0.3922 0.6667 0.2778
No log 3.0 75 0.0655 0.9919 0.3922 0.6667 0.2778
No log 4.0 100 0.0378 0.9919 0.3922 0.6667 0.2778
No log 5.0 125 0.0292 0.9919 0.3922 0.6667 0.2778
No log 6.0 150 0.0255 0.9919 0.3922 0.6667 0.2778
No log 7.0 175 0.0238 0.9919 0.3922 0.6667 0.2778
No log 8.0 200 0.0227 0.9919 0.3922 0.6667 0.2778
No log 9.0 225 0.0222 0.9919 0.3922 0.6667 0.2778
No log 10.0 250 0.0221 0.9919 0.3922 0.6667 0.2778

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.1+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1