my_ner_model

This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2675

  • Accuracy: 0.9418

  • F1: 0.4060

  • Classification Report: precision recall f1-score support

    corporation 0.27 0.11 0.15 66

creative-work 0.17 0.01 0.01 142 group 0.43 0.04 0.07 165 location 0.42 0.45 0.43 150 person 0.71 0.59 0.65 429 product 0.08 0.02 0.03 127

micro avg       0.57      0.31      0.41      1079
macro avg       0.35      0.20      0.22      1079

weighted avg 0.45 0.31 0.34 1079

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: 2

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Classification Report
No log 1.0 213 0.2799 0.9390 0.3613 precision recall f1-score support

corporation 0.00 0.00 0.00 66 creative-work 0.00 0.00 0.00 142 group 0.00 0.00 0.00 165 location 0.38 0.34 0.36 150 person 0.69 0.53 0.60 429 product 0.00 0.00 0.00 127

micro avg       0.59      0.26      0.36      1079
macro avg       0.18      0.15      0.16      1079

weighted avg 0.33 0.26 0.29 1079 | | No log | 2.0 | 426 | 0.2675 | 0.9418 | 0.4060 | precision recall f1-score support

corporation 0.27 0.11 0.15 66 creative-work 0.17 0.01 0.01 142 group 0.43 0.04 0.07 165 location 0.42 0.45 0.43 150 person 0.71 0.59 0.65 429 product 0.08 0.02 0.03 127

micro avg       0.57      0.31      0.41      1079
macro avg       0.35      0.20      0.22      1079

weighted avg 0.45 0.31 0.34 1079 |

Framework versions

  • Transformers 4.16.2
  • Pytorch 2.4.1+cpu
  • Datasets 1.16.1
  • Tokenizers 0.21.0
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Dataset used to train mukazhanovn/my_ner_model

Evaluation results