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End of training
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metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - szeged_ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: my_awesome_wnut_model
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: szeged_ner
          type: szeged_ner
          config: business
          split: test
          args: business
        metrics:
          - name: Precision
            type: precision
            value: 0.8253343823760818
          - name: Recall
            type: recall
            value: 0.856326530612245
          - name: F1
            type: f1
            value: 0.8405448717948719
          - name: Accuracy
            type: accuracy
            value: 0.9829550592277783

my_awesome_wnut_model

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

  • Loss: 0.0590
  • Precision: 0.8253
  • Recall: 0.8563
  • F1: 0.8405
  • Accuracy: 0.9830

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 Precision Recall F1 Accuracy
0.2068 1.0 511 0.0724 0.8008 0.8237 0.8121 0.9797
0.0835 2.0 1022 0.0590 0.8253 0.8563 0.8405 0.9830

Framework versions

  • Transformers 4.32.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3