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GItaf/bert-base-uncased-bert-base-uncased-mc-weight1-epoch15

sourceHugging Faceupdated 4y agoView on Hugging Face
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Model Card

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bert-base-uncased-bert-base-uncased-mc-weight1-epoch15

This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set:

  • —Loss: 7.8027
  • —Cls loss: 3.4449
  • —Lm loss: 4.3556
  • —Cls Accuracy: 0.5706
  • —Cls F1: 0.5697
  • —Cls Precision: 0.5753
  • —Cls Recall: 0.5706
  • —Perplexity: 77.91

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: 2
  • —evalbatchsize: 2
  • —seed: 42
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: linear
  • —num_epochs: 15

Training results

Training LossEpochStepValidation LossCls lossLm lossCls AccuracyCls F1Cls PrecisionCls RecallPerplexity
6.95261.034706.31541.77484.53990.49910.45770.44210.499193.68
6.08762.069406.14271.67734.46430.55450.53420.57170.554586.86
5.72313.0104106.02061.59554.42400.59020.57590.60200.590283.43
5.38774.0138805.98571.57724.40730.60920.60310.60520.609282.05
5.10925.0173506.37421.99814.37480.59420.59010.59640.594279.42
4.85046.0208206.45112.07764.37370.58900.58750.60410.589079.34
4.63697.0242906.98572.62684.35710.58270.57960.59790.582778.03
4.46678.0277606.95502.60754.34580.58330.58310.59040.583377.16
4.31279.0312307.20412.85184.35040.59020.58560.59350.590277.51
4.177710.0347007.42333.07464.34670.57930.57700.58290.579377.22
4.087111.0381707.49973.14884.34890.57460.57490.58530.574677.39
3.999112.0416407.66363.31134.35020.56020.56050.56760.560277.49
3.946113.0451107.60653.25144.35300.56950.56900.57380.569577.71
3.901314.0485807.75623.40174.35230.57870.57850.58230.578777.65
3.873115.0520507.80273.44494.35560.57060.56970.57530.570677.91

Framework versions

  • —Transformers 4.21.2
  • —Pytorch 1.12.1
  • —Datasets 2.4.0
  • —Tokenizers 0.12.1