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metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - clinc_oos
metrics:
  - accuracy
  - f1
model-index:
  - name: bert-base-uncased-finetuned-clinc_oos
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: clinc_oos
          type: clinc_oos
          config: plus
          split: test
          args: plus
        metrics:
          - name: Accuracy
            type: accuracy
            value:
              accuracy: 0.8672727272727273
          - name: F1
            type: f1
            value:
              f1: 0.8593551627139002

bert-base-uncased-finetuned-clinc_oos

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

  • Loss: 1.0863
  • Accuracy: {'accuracy': 0.8672727272727273}
  • F1: {'f1': 0.8593551627139002}

Model Training Details

Parameter Value
Task text-classification
Base Model Name bert-base-uncased
Dataset Name clinc_oos
Dataset Config plus
Batch Size 16
Number of Epochs 3
Learning Rate 0.00002

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: 3

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
4.3415 1.0 954 2.4724 {'accuracy': 0.7769090909090909} {'f1': 0.7596942777117995}
1.7949 2.0 1908 1.3415 {'accuracy': 0.8538181818181818} {'f1': 0.8441232118060242}
0.8898 3.0 2862 1.0863 {'accuracy': 0.8672727272727273} {'f1': 0.8593551627139002}

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3