CoolFace
Modelpublic

Ruchita-debug/distilbert-base-uncased-lora-text-classification

sourceHugging Faceapache-2.0updated 3y agoView on Hugging Face
0likes10downloads
Model Card

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

distilbert-base-uncased-lora-text-classification

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • —Loss: 1.3644
  • —Accuracy: {'accuracy': 0.858}

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: 0.001
  • —trainbatchsize: 4
  • —evalbatchsize: 4
  • —seed: 42
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: linear
  • —num_epochs: 10

Training results

Training LossEpochStepValidation LossAccuracy
No log1.02500.3793{'accuracy': 0.856}
0.4352.05000.5190{'accuracy': 0.858}
0.4353.07500.8326{'accuracy': 0.857}
0.20054.010000.9137{'accuracy': 0.856}
0.20055.012501.0362{'accuracy': 0.862}
0.08276.015001.2331{'accuracy': 0.852}
0.08277.017501.2110{'accuracy': 0.856}
0.0338.020001.2963{'accuracy': 0.864}
0.0339.022501.3438{'accuracy': 0.863}
0.012810.025001.3644{'accuracy': 0.858}

Framework versions

  • —PEFT 0.7.1
  • —Transformers 4.36.0
  • —Pytorch 2.1.1+cpu
  • —Datasets 2.15.0
  • —Tokenizers 0.15.0