metadata

license: mit
base_model: dslim/bert-base-NER
tags:
  - generated_from_trainer
datasets:
  - conll2003job
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: my_xlm-roberta-large-finetuned-conlljob01
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: conll2003job
          type: conll2003job
          config: conll2003job
          split: test
          args: conll2003job
        metrics:
          - name: Precision
            type: precision
            value: 0.9057427125152732
          - name: Recall
            type: recall
            value: 0.9187322946175638
          - name: F1
            type: f1
            value: 0.9121912630746243
          - name: Accuracy
            type: accuracy
            value: 0.9825347259610208

my_xlm-roberta-large-finetuned-conlljob01

This model is a fine-tuned version of dslim/bert-base-NER on the conll2003job dataset. It achieves the following results on the evaluation set:

Loss: 0.1690
Precision: 0.9057
Recall: 0.9187
F1: 0.9122
Accuracy: 0.9825

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: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0372	1.0	896	0.1439	0.8943	0.9184	0.9062	0.9816
0.0043	2.0	1792	0.1532	0.9047	0.9209	0.9127	0.9824
0.0019	3.0	2688	0.1652	0.9102	0.9186	0.9143	0.9828
0.0013	4.0	3584	0.1690	0.9057	0.9187	0.9122	0.9825

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.5
Tokenizers 0.14.1