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emotions_google_gemma

PEFT
Safetensors
gemma
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emotions_google_gemma / README.md
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metadata
license: gemma
base_model: google/gemma-1.1-2b-it
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: emotions_google_gemma
    results: []

emotions_google_gemma

This model is a fine-tuned version of google/gemma-1.1-2b-it on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8963
  • F1 Micro: 0.6927
  • F1 Macro: 0.5713
  • Accuracy: 0.2537

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: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss F1 Micro F1 Macro Accuracy
0.7843 0.1035 20 0.6332 0.6164 0.4369 0.1301
0.5717 0.2070 40 0.5575 0.6468 0.5359 0.1793
0.5341 0.3105 60 0.5292 0.6788 0.5562 0.2006
0.5054 0.4140 80 0.5143 0.6830 0.5716 0.2045
0.4748 0.5175 100 0.5039 0.6875 0.5797 0.1754
0.5144 0.6210 120 0.5028 0.6804 0.5988 0.1631
0.5055 0.7245 140 0.5101 0.6823 0.5728 0.2039
0.5124 0.8279 160 0.4851 0.6854 0.5947 0.1793
0.488 0.9314 180 0.4906 0.6777 0.5947 0.1638
0.4867 1.0349 200 0.4970 0.6845 0.6033 0.2227
0.3367 1.1384 220 0.5478 0.6977 0.5848 0.2188
0.3342 1.2419 240 0.5531 0.6860 0.5898 0.2110
0.3161 1.3454 260 0.5754 0.6719 0.5768 0.1955
0.3312 1.4489 280 0.5335 0.6840 0.5906 0.1961
0.3633 1.5524 300 0.5255 0.6799 0.5940 0.1883
0.3199 1.6559 320 0.5461 0.6722 0.5868 0.1922
0.3385 1.7594 340 0.5417 0.6888 0.5795 0.2149
0.3292 1.8629 360 0.5324 0.6883 0.5969 0.1981
0.3347 1.9664 380 0.5274 0.6890 0.5881 0.2006
0.2122 2.0699 400 0.6957 0.6755 0.5671 0.2350
0.1289 2.1734 420 0.6570 0.6814 0.5825 0.1974
0.1505 2.2768 440 0.6495 0.6854 0.5857 0.2117
0.1345 2.3803 460 0.7193 0.6813 0.5681 0.2045
0.1438 2.4838 480 0.7042 0.6782 0.5649 0.2065
0.14 2.5873 500 0.6777 0.6855 0.5826 0.2104
0.146 2.6908 520 0.6699 0.6837 0.5840 0.2129
0.138 2.7943 540 0.6954 0.6884 0.5820 0.2369
0.1302 2.8978 560 0.7090 0.6828 0.5777 0.2220
0.1324 3.0013 580 0.7075 0.6845 0.5818 0.2259
0.0472 3.1048 600 0.8346 0.6867 0.5575 0.2414
0.0544 3.2083 620 0.7725 0.6785 0.5706 0.2207
0.0483 3.3118 640 0.8136 0.6865 0.5659 0.2291
0.0465 3.4153 660 0.8333 0.6797 0.5613 0.2278
0.0511 3.5188 680 0.8234 0.6852 0.5641 0.2265
0.0511 3.6223 700 0.8298 0.6905 0.5712 0.2401
0.0406 3.7257 720 0.8292 0.6886 0.5721 0.2421
0.0565 3.8292 740 0.8266 0.6927 0.5721 0.2408
0.0554 3.9327 760 0.7764 0.6887 0.5765 0.2350
0.0319 4.0362 780 0.8450 0.6825 0.5650 0.2388
0.0161 4.1397 800 0.8948 0.6892 0.5648 0.2524
0.0174 4.2432 820 0.9146 0.6910 0.5659 0.2570
0.0168 4.3467 840 0.9068 0.6874 0.5657 0.2414
0.0184 4.4502 860 0.9225 0.6872 0.5615 0.2531
0.0123 4.5537 880 0.9062 0.6882 0.5639 0.2511
0.0149 4.6572 900 0.9087 0.6889 0.5660 0.2492
0.0199 4.7607 920 0.8948 0.6917 0.5722 0.2472
0.0144 4.8642 940 0.8944 0.6929 0.5724 0.2518
0.015 4.9677 960 0.8963 0.6925 0.5709 0.2531

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1