metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - ontonotes5
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-finetuned-ner-ontonotes
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: ontonotes5
          type: ontonotes5
          config: ontonotes5
          split: train
          args: ontonotes5
        metrics:
          - name: Precision
            type: precision
            value: 0.8567258883248731
          - name: Recall
            type: recall
            value: 0.8841595180407308
          - name: F1
            type: f1
            value: 0.8702265476459025
          - name: Accuracy
            type: accuracy
            value: 0.9754933764288157

bert-finetuned-ner-ontonotes

This model is a fine-tuned version of bert-base-cased on the ontonotes5 dataset. It achieves the following results on the evaluation set:

Loss: 0.1503
Precision: 0.8567
Recall: 0.8842
F1: 0.8702
Accuracy: 0.9755

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: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0842	1.0	7491	0.0950	0.8524	0.8715	0.8618	0.9745
0.0523	2.0	14982	0.1044	0.8449	0.8827	0.8634	0.9744
0.036	3.0	22473	0.1118	0.8529	0.8843	0.8683	0.9760
0.0231	4.0	29964	0.1240	0.8589	0.8805	0.8696	0.9752
0.0118	5.0	37455	0.1416	0.8570	0.8804	0.8685	0.9753
0.0077	6.0	44946	0.1503	0.8567	0.8842	0.8702	0.9755

Framework versions

Transformers 4.22.1
Pytorch 1.12.1+cu113
Datasets 2.5.1
Tokenizers 0.12.1