marzieh-maleki/defeasible-nli-bert-clf

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defeasible-nli-bert-clf

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

• —Loss: 1.3952
• —Accuracy: 0.5
• —F1: 0.6667
• —Precision: 0.5
• —Recall: 1.0

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
• —trainbatchsize: 16
• —evalbatchsize: 16
• —seed: 42
• —gradientaccumulationsteps: 8
• —totaltrainbatch_size: 128
• —optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
• —lrschedulertype: cosine
• —num_epochs: 3
• —mixedprecisiontraining: Native AMP

Training results

Framework versions

• —Transformers 5.14.1
• —Pytorch 2.5.1+cu121
• —Datasets 5.0.1
• —Tokenizers 0.22.2