AnanthZeke's picture
update model card README.md
ab454bd
metadata
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: tabert-1k-naamapadam
    results: []

tabert-1k-naamapadam

This model is a fine-tuned version of livinNector/tabert-1k on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2825
  • Precision: 0.7764
  • Recall: 0.8055
  • F1: 0.7907
  • Accuracy: 0.9068

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: 5e-05
  • train_batch_size: 64
  • eval_batch_size: 128
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.4618 0.05 400 0.3963 0.7329 0.6498 0.6889 0.8716
0.3869 0.1 800 0.3583 0.7145 0.7347 0.7244 0.8828
0.3642 0.15 1200 0.3511 0.7241 0.7412 0.7325 0.8842
0.3533 0.21 1600 0.3451 0.7393 0.7429 0.7411 0.8873
0.3501 0.26 2000 0.3367 0.7456 0.7562 0.7509 0.8899
0.3369 0.31 2400 0.3343 0.7476 0.7549 0.7512 0.8909
0.3302 0.36 2800 0.3282 0.7413 0.7584 0.7497 0.8926
0.3327 0.41 3200 0.3238 0.7584 0.7717 0.7650 0.8961
0.3248 0.46 3600 0.3209 0.7468 0.7795 0.7628 0.8956
0.3175 0.51 4000 0.3140 0.7659 0.7681 0.7670 0.8985
0.3132 0.57 4400 0.3111 0.7537 0.7795 0.7664 0.8970
0.3141 0.62 4800 0.3122 0.7529 0.7797 0.7661 0.8972
0.3077 0.67 5200 0.3138 0.7493 0.7844 0.7665 0.8974
0.309 0.72 5600 0.3099 0.7674 0.7729 0.7702 0.8992
0.3085 0.77 6000 0.3038 0.7626 0.7940 0.7780 0.9009
0.3031 0.82 6400 0.3055 0.7633 0.7834 0.7732 0.8992
0.2958 0.87 6800 0.3054 0.7621 0.7924 0.7770 0.8991
0.2953 0.93 7200 0.3076 0.7714 0.7834 0.7774 0.9005
0.2978 0.98 7600 0.3003 0.7729 0.7855 0.7792 0.9017
0.2826 1.03 8000 0.3016 0.7665 0.7905 0.7783 0.9012
0.2757 1.08 8400 0.3053 0.7520 0.8072 0.7786 0.8996
0.2751 1.13 8800 0.3026 0.7626 0.7982 0.7800 0.9008
0.2694 1.18 9200 0.2957 0.7682 0.8007 0.7841 0.9039
0.2723 1.23 9600 0.2944 0.7698 0.8005 0.7849 0.9039
0.2726 1.29 10000 0.2912 0.7774 0.7930 0.7851 0.9042
0.2674 1.34 10400 0.2912 0.7739 0.7973 0.7854 0.9043
0.2714 1.39 10800 0.2907 0.7729 0.7995 0.7860 0.9036
0.2625 1.44 11200 0.2949 0.7716 0.7965 0.7838 0.9041
0.2669 1.49 11600 0.2883 0.7701 0.8087 0.7889 0.9054
0.2601 1.54 12000 0.2868 0.7759 0.8069 0.7911 0.9066
0.2633 1.59 12400 0.2895 0.7659 0.8125 0.7885 0.9051
0.2641 1.65 12800 0.2878 0.7790 0.7972 0.7880 0.9059
0.2661 1.7 13200 0.2875 0.7800 0.7999 0.7898 0.9068
0.2719 1.75 13600 0.2853 0.7783 0.8025 0.7902 0.9070
0.2602 1.8 14000 0.2827 0.7801 0.8051 0.7924 0.9070
0.2688 1.85 14400 0.2819 0.7742 0.8061 0.7898 0.9066
0.2615 1.9 14800 0.2828 0.7764 0.8017 0.7888 0.9065
0.2623 1.95 15200 0.2825 0.7764 0.8055 0.7907 0.9068

Framework versions

  • Transformers 4.29.2
  • Pytorch 2.0.0
  • Datasets 2.12.0
  • Tokenizers 0.13.3