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leomaurodesenv/nli-MiniLM2-L6-H768-nvidia-aegis-v1-augmented

sourceHugging Faceapache-2.0updated 15d agoView on Hugging Face
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Model Card

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nli-MiniLM2-L6-H768-nvidia-aegis-v1-augmented

This model is a fine-tuned version of cross-encoder/nli-MiniLM2-L6-H768 on an unknown dataset. It achieves the following results on the evaluation set:

  • —Loss: 0.2039
  • —Accuracy: 0.9498

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: 8
  • —evalbatchsize: 8
  • —seed: 42
  • —gradientaccumulationsteps: 2
  • —totaltrainbatch_size: 16
  • —optimizer: Use adamwtorchfused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • —lrschedulertype: linear
  • —lrschedulerwarmup_steps: 50
  • —num_epochs: 10

Training results

Training LossEpochStepValidation LossAccuracy
0.66611.030240.29870.8732
0.58382.060480.20870.9201
0.13553.090720.20360.9331
0.29754.0120960.20700.9408
0.17355.0151200.20350.9503
0.06106.0181440.22430.9468
0.09867.0211680.22930.9520
0.00888.0241920.25520.9525

Framework versions

  • —Transformers 5.2.0
  • —Pytorch 2.10.0+cu128
  • —Datasets 4.5.0
  • —Tokenizers 0.22.2