medicine-ner / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - jxner
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: distilbert-base-uncased
model-index:
  - name: medicine-ner
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: jxner
          type: jxner
          config: wnut_17
          split: test
          args: wnut_17
        metrics:
          - type: precision
            value: 0
            name: Precision
          - type: recall
            value: 0
            name: Recall
          - type: f1
            value: 0
            name: F1
          - type: accuracy
            value: 0.859375
            name: Accuracy

medicine-ner

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

  • Loss: 0.7996
  • Precision: 0.0
  • Recall: 0.0
  • F1: 0.0
  • Accuracy: 0.8594

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
No log 1.0 1 0.8644 0.0 0.0 0.0 0.8594
No log 2.0 2 0.7996 0.0 0.0 0.0 0.8594

Framework versions

  • Transformers 4.27.3
  • Pytorch 1.13.1+cu116
  • Datasets 2.10.1
  • Tokenizers 0.13.2