README.md

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