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deberta-v3-base_fine_tuned_food_ner

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metadata
license: mit
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: deberta-v3-base_fine_tuned_food_ner
    results: []

deberta-v3-base_fine_tuned_food_ner

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: 0.4164
  • Precision: 0.9268
  • Recall: 0.9446
  • F1: 0.9356
  • Accuracy: 0.9197

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: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 40 0.8425 0.8323 0.8323 0.8323 0.8073
No log 2.0 80 0.5533 0.8703 0.8941 0.8820 0.8731
No log 3.0 120 0.4855 0.8771 0.9109 0.8937 0.8797
No log 4.0 160 0.4238 0.8949 0.9222 0.9083 0.8964
No log 5.0 200 0.4176 0.9048 0.9302 0.9173 0.9008
No log 6.0 240 0.4127 0.9065 0.9342 0.9202 0.9004
No log 7.0 280 0.4409 0.9294 0.9302 0.9298 0.9043
No log 8.0 320 0.3971 0.9129 0.9334 0.9230 0.9061
No log 9.0 360 0.3941 0.9112 0.9390 0.9249 0.9061
No log 10.0 400 0.4069 0.9233 0.9366 0.9299 0.9148
No log 11.0 440 0.4039 0.9213 0.9390 0.9300 0.9162
No log 12.0 480 0.4000 0.9126 0.9470 0.9295 0.9113
0.3799 13.0 520 0.4126 0.9323 0.9390 0.9356 0.9179
0.3799 14.0 560 0.4076 0.9272 0.9398 0.9334 0.9140
0.3799 15.0 600 0.4129 0.9317 0.9414 0.9365 0.9188
0.3799 16.0 640 0.4000 0.9239 0.9446 0.9341 0.9162
0.3799 17.0 680 0.4098 0.9267 0.9438 0.9352 0.9179
0.3799 18.0 720 0.4110 0.9232 0.9454 0.9342 0.9188
0.3799 19.0 760 0.4202 0.9275 0.9446 0.9360 0.9183
0.3799 20.0 800 0.4164 0.9268 0.9446 0.9356 0.9197

Framework versions

  • Transformers 4.21.0
  • Pytorch 1.12.0+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1