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update model card README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - article250v1_wikigold_split
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: Article_250v1_NER_Model_3Epochs_UNAUGMENTED
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: article250v1_wikigold_split
          type: article250v1_wikigold_split
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.46870653685674546
          - name: Recall
            type: recall
            value: 0.47571993224167136
          - name: F1
            type: f1
            value: 0.47218719349866894
          - name: Accuracy
            type: accuracy
            value: 0.9010771263083021

Article_250v1_NER_Model_3Epochs_UNAUGMENTED

This model is a fine-tuned version of bert-base-cased on the article250v1_wikigold_split dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2871
  • Precision: 0.4687
  • Recall: 0.4757
  • F1: 0.4722
  • Accuracy: 0.9011

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 31 0.4885 0.1689 0.0426 0.0681 0.8016
No log 2.0 62 0.3231 0.3894 0.3834 0.3864 0.8874
No log 3.0 93 0.2871 0.4687 0.4757 0.4722 0.9011

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.11.0+cu113
  • Datasets 2.4.0
  • Tokenizers 0.11.6