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
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Dataset used to train nikitakapitan/bert-base-uncased-finetuned-clinc_oos

Evaluation results