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ner_bert_model

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metadata
license: apache-2.0
base_model: distilbert-base-cased
tags:
  - generated_from_trainer
datasets:
  - shipping_label_ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ner_bert_model
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: shipping_label_ner
          type: shipping_label_ner
          config: shipping_label_ner
          split: validation
          args: shipping_label_ner
        metrics:
          - name: Precision
            type: precision
            value: 0.5178571428571429
          - name: Recall
            type: recall
            value: 0.7837837837837838
          - name: F1
            type: f1
            value: 0.6236559139784947
          - name: Accuracy
            type: accuracy
            value: 0.7627118644067796

ner_bert_model

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

  • Loss: 1.2010
  • Precision: 0.5179
  • Recall: 0.7838
  • F1: 0.6237
  • Accuracy: 0.7627

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: 4
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 14 0.6828 0.5179 0.7838 0.6237 0.7627
No log 2.0 28 0.8587 0.5273 0.7838 0.6304 0.7712
No log 3.0 42 0.7206 0.5577 0.7838 0.6517 0.8136
No log 4.0 56 0.8983 0.5370 0.7838 0.6374 0.7797
No log 5.0 70 0.6964 0.5472 0.7838 0.6444 0.8051
No log 6.0 84 0.9793 0.5179 0.7838 0.6237 0.7627
No log 7.0 98 0.6047 0.5472 0.7838 0.6444 0.8051
No log 8.0 112 1.0809 0.5179 0.7838 0.6237 0.7797
No log 9.0 126 1.1726 0.5179 0.7838 0.6237 0.7627
No log 10.0 140 1.0067 0.5179 0.7838 0.6237 0.7627
No log 11.0 154 1.1439 0.5088 0.7838 0.6170 0.7627
No log 12.0 168 0.8971 0.5370 0.7838 0.6374 0.7881
No log 13.0 182 1.0603 0.5179 0.7838 0.6237 0.7542
No log 14.0 196 1.2095 0.5179 0.7838 0.6237 0.7627
No log 15.0 210 1.2395 0.5179 0.7838 0.6237 0.7627
No log 16.0 224 1.2509 0.5179 0.7838 0.6237 0.7627
No log 17.0 238 1.2317 0.5179 0.7838 0.6237 0.7542
No log 18.0 252 1.2656 0.5179 0.7838 0.6237 0.7542
No log 19.0 266 1.1950 0.5179 0.7838 0.6237 0.7627
No log 20.0 280 1.2010 0.5179 0.7838 0.6237 0.7627

Framework versions

  • Transformers 4.39.1
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2