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finetuned-parsbert-uncased-ArmanEmo

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

  • Loss: 0.0060
  • Accuracy: 1.0
  • Precision Macro: 1.0
  • Recall Macro: 1.0
  • F1 Macro: 1.0
  • F1 C0: 1.0
  • F1 C1: 1.0
  • F1 C2: 1.0
  • F1 C3: 1.0
  • F1 C4: 1.0
  • F1 C5: 1.0
  • F1 C6: 1.0
  • Recall C0: 1.0
  • Recall C1: 1.0
  • Recall C2: 1.0
  • Recall C3: 1.0
  • Recall C4: 1.0
  • Recall C5: 1.0
  • Recall C6: 1.0
  • Precision C0: 1.0
  • Precision C1: 1.0
  • Precision C2: 1.0
  • Precision C3: 1.0
  • Precision C4: 1.0
  • Precision C5: 1.0
  • Precision C6: 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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Macro Recall Macro F1 Macro F1 C0 F1 C1 F1 C2 F1 C3 F1 C4 F1 C5 F1 C6 Recall C0 Recall C1 Recall C2 Recall C3 Recall C4 Recall C5 Recall C6 Precision C0 Precision C1 Precision C2 Precision C3 Precision C4 Precision C5 Precision C6
No log 1.0 144 0.3531 0.5065 0.3687 0.3986 0.3583 0.6766 0.3370 0.4966 0.0 0.4164 0.0 0.5817 0.9091 0.2366 0.7143 0.0 0.3862 0.0 0.5440 0.5388 0.5849 0.3806 0.0 0.4516 0.0 0.625
No log 2.0 288 0.2289 0.7298 0.5210 0.5631 0.5333 0.8451 0.7790 0.6063 0.0 0.6905 0.0 0.8120 0.9818 0.8206 0.5 0.0 0.8 0.0 0.8394 0.7418 0.7414 0.77 0.0 0.6073 0.0 0.7864
No log 3.0 432 0.1129 0.9270 0.9239 0.8955 0.9061 0.9762 0.9492 0.8910 0.9027 0.8792 0.8108 0.9333 0.9709 0.9618 0.9026 0.8947 0.9034 0.6923 0.9430 0.9816 0.9368 0.8797 0.9107 0.8562 0.9783 0.9239
0.3027 4.0 576 0.0567 0.9652 0.9491 0.9545 0.9507 0.9909 0.9847 0.9161 0.9402 0.9565 0.8872 0.9796 0.9927 0.9847 0.8506 0.9649 0.9862 0.9077 0.9948 0.9891 0.9847 0.9924 0.9167 0.9286 0.8676 0.9648
0.3027 5.0 720 0.0296 0.9844 0.9736 0.9819 0.9776 0.9964 0.9885 0.9673 0.9913 0.9862 0.9185 0.9948 0.9927 0.9847 0.9610 1.0 0.9862 0.9538 0.9948 1.0 0.9923 0.9737 0.9828 0.9862 0.8857 0.9948
0.3027 6.0 864 0.0144 0.9965 0.9939 0.9937 0.9938 0.9982 1.0 0.9935 0.9913 0.9965 0.9767 1.0 1.0 1.0 0.9935 1.0 0.9931 0.9692 1.0 0.9964 1.0 0.9935 0.9828 1.0 0.9844 1.0
0.0482 7.0 1008 0.0088 0.9991 0.9993 0.9990 0.9991 1.0 1.0 1.0 1.0 0.9965 1.0 0.9974 1.0 1.0 1.0 1.0 0.9931 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.9948
0.0482 8.0 1152 0.0069 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
0.0482 9.0 1296 0.0063 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
0.0482 10.0 1440 0.0060 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Framework versions

  • Transformers 4.38.1
  • Pytorch 2.1.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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