README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - conll2003
  - autoevaluate/conll2003-sample
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: distilbert-base-uncased
model-index:
  - name: entity-extraction
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          args: conll2003
        metrics:
          - type: precision
            value: 0.8862817854414493
            name: Precision
          - type: recall
            value: 0.9084908826490659
            name: Recall
          - type: f1
            value: 0.8972489227709645
            name: F1
          - type: accuracy
            value: 0.9774889986814304
            name: Accuracy

entity-extraction

This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0808
• Precision: 0.8863
• Recall: 0.9085
• F1: 0.8972
• Accuracy: 0.9775

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: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2552	1.0	878	0.0808	0.8863	0.9085	0.8972	0.9775

Framework versions

• Transformers 4.19.2
• Pytorch 1.11.0+cu113
• Datasets 2.2.2
• Tokenizers 0.12.1