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distilbert-base-uncased-TASTESet-ner

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distilbert
named_entity_recognition
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metadata
license: apache-2.0
tags:
  - named_entity_recognition
  - food_recipes_ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: distilbert-base-uncased-TASTESet-ner
    results: []
datasets:
  - dmargutierrez/TASTESet

distilbert-base-uncased-TASTESet-ner

This model is a fine-tuned version of distilbert-base-uncased on the TASTESet dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3816
  • Precision: 0.8929
  • Recall: 0.9229
  • F1: 0.9076
  • Accuracy: 0.9130

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 31 1.0797 0.6027 0.6903 0.6435 0.7063
No log 2.0 62 0.6402 0.7681 0.8295 0.7976 0.8304
No log 3.0 93 0.4899 0.8379 0.8789 0.8579 0.8728
No log 4.0 124 0.4232 0.8716 0.8994 0.8853 0.8912
No log 5.0 155 0.3883 0.8798 0.9043 0.8919 0.8992
No log 6.0 186 0.3848 0.8769 0.9103 0.8933 0.9004
No log 7.0 217 0.3684 0.8864 0.9123 0.8991 0.9046
No log 8.0 248 0.3650 0.8930 0.9182 0.9054 0.9087
No log 9.0 279 0.3628 0.8908 0.9197 0.9050 0.9096
No log 10.0 310 0.3674 0.8933 0.9165 0.9047 0.9093
No log 11.0 341 0.3668 0.8958 0.9177 0.9066 0.9120
No log 12.0 372 0.3717 0.8904 0.9234 0.9066 0.9120
No log 13.0 403 0.3693 0.8940 0.9197 0.9067 0.9126
No log 14.0 434 0.3805 0.8913 0.9239 0.9073 0.9135
No log 15.0 465 0.3788 0.8954 0.9202 0.9076 0.9123
No log 16.0 496 0.3803 0.8935 0.9231 0.9081 0.9122
0.3275 17.0 527 0.3814 0.8918 0.9229 0.9071 0.9126
0.3275 18.0 558 0.3823 0.8921 0.9241 0.9079 0.9123
0.3275 19.0 589 0.3827 0.8928 0.9224 0.9074 0.9124
0.3275 20.0 620 0.3816 0.8929 0.9229 0.9076 0.9130

Framework versions

  • Transformers 4.26.0
  • Pytorch 1.13.1+cu117
  • Datasets 2.9.0
  • Tokenizers 0.13.2