metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - article250v4_wikigold_split
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: Article_250v4_NER_Model_3Epochs_UNAUGMENTED
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: article250v4_wikigold_split
          type: article250v4_wikigold_split
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.40273125483122907
          - name: Recall
            type: recall
            value: 0.433684794672586
          - name: F1
            type: f1
            value: 0.4176352705410822
          - name: Accuracy
            type: accuracy
            value: 0.8774915169033556

Article_250v4_NER_Model_3Epochs_UNAUGMENTED

This model is a fine-tuned version of bert-base-cased on the article250v4_wikigold_split dataset. It achieves the following results on the evaluation set:

Loss: 0.3243
Precision: 0.4027
Recall: 0.4337
F1: 0.4176
Accuracy: 0.8775

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	28	0.5309	0.0816	0.0144	0.0245	0.7931
No log	2.0	56	0.3620	0.3795	0.3674	0.3733	0.8623
No log	3.0	84	0.3243	0.4027	0.4337	0.4176	0.8775

Framework versions

Transformers 4.17.0
Pytorch 1.11.0+cu113
Datasets 2.4.0
Tokenizers 0.11.6