metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - article250v5_wikigold_split
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: Article_250v5_NER_Model_3Epochs_UNAUGMENTED
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: article250v5_wikigold_split
          type: article250v5_wikigold_split
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.3979099678456592
          - name: Recall
            type: recall
            value: 0.4221148379761228
          - name: F1
            type: f1
            value: 0.4096551724137931
          - name: Accuracy
            type: accuracy
            value: 0.8778839730743538

Article_250v5_NER_Model_3Epochs_UNAUGMENTED

This model is a fine-tuned version of bert-base-cased on the article250v5_wikigold_split dataset. It achieves the following results on the evaluation set:

Loss: 0.3250
Precision: 0.3979
Recall: 0.4221
F1: 0.4097
Accuracy: 0.8779

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	0.5229	0.1336	0.0344	0.0547	0.8008
No log	2.0	62	0.3701	0.3628	0.3357	0.3487	0.8596
No log	3.0	93	0.3250	0.3979	0.4221	0.4097	0.8779

Framework versions

Transformers 4.17.0
Pytorch 1.11.0+cu113
Datasets 2.4.0
Tokenizers 0.11.6