README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: distilbert-base-uncased-finetuned-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: conll2003
          type: conll2003
          args: conll2003
        metrics:
          - name: Precision
            type: precision
            value: 0.875445994161531
          - name: Recall
            type: recall
            value: 0.9058060185703098
          - name: F1
            type: f1
            value: 0.8903672751264571
          - name: Accuracy
            type: accuracy
            value: 0.9763292928971993

distilbert-base-uncased-finetuned-ner

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: 0.0845
• Precision: 0.8754
• Recall: 0.9058
• F1: 0.8904
• Accuracy: 0.9763

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
0.2529	1.0	878	0.0845	0.8754	0.9058	0.8904	0.9763

Framework versions

• Transformers 4.13.0
• Pytorch 1.10.0+cu111
• Datasets 1.16.1
• Tokenizers 0.10.3