metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - article500v3_wikigold_split
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: Article_500v3_NER_Model_3Epochs_UNAUGMENTED
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: article500v3_wikigold_split
          type: article500v3_wikigold_split
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.6712553261225828
          - name: Recall
            type: recall
            value: 0.6822118587608261
          - name: F1
            type: f1
            value: 0.6766892450024782
          - name: Accuracy
            type: accuracy
            value: 0.9297837351453659

Article_500v3_NER_Model_3Epochs_UNAUGMENTED

This model is a fine-tuned version of bert-base-cased on the article500v3_wikigold_split dataset. It achieves the following results on the evaluation set:

Loss: 0.2058
Precision: 0.6713
Recall: 0.6822
F1: 0.6767
Accuracy: 0.9298

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	60	0.2967	0.4017	0.4620	0.4297	0.8975
No log	2.0	120	0.2146	0.6402	0.6609	0.6504	0.9256
No log	3.0	180	0.2058	0.6713	0.6822	0.6767	0.9298

Framework versions

Transformers 4.17.0
Pytorch 1.11.0+cu113
Datasets 2.4.0
Tokenizers 0.11.6