my_ner_model

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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

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