metadata

license: mit
base_model: dslim/bert-large-NER
tags:
  - generated_from_trainer
datasets:
  - job-titles
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: my_awesome_wnut_model
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: job-titles
          type: job-titles
          config: job-titles
          split: test
          args: job-titles
        metrics:
          - name: Precision
            type: precision
            value: 0.9992003198720512
          - name: Recall
            type: recall
            value: 0.9996
          - name: F1
            type: f1
            value: 0.9994001199760049
          - name: Accuracy
            type: accuracy
            value: 0.6346958244661334

my_awesome_wnut_model

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

Loss: 0.6603
Precision: 0.9992
Recall: 0.9996
F1: 0.9994
Accuracy: 0.6347

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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.6666	1.0	4587	0.6615	1.0	1.0	1.0	0.6331
0.6617	2.0	9174	0.6603	0.9992	0.9996	0.9994	0.6347

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.5
Tokenizers 0.14.1