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song-artist-classifier-v13-wd-0-02-bs-20

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metadata
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
  - generated_from_trainer
metrics:
  - f1
model-index:
  - name: song-artist-classifier-v13-wd-0-02-bs-20
    results: []

song-artist-classifier-v13-wd-0-02-bs-20

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

  • Loss: 1.0270
  • F1: [0.56, 0.7368421052631577, 0.6666666666666666, 0.823529411764706, 0.5, 0.761904761904762, 0.8235294117647058, 0.37499999999999994, 0.9, 0.7368421052631577, 0.47058823529411764, 0.6666666666666667, 0.7272727272727272, 0.45454545454545453, 0.761904761904762, 0.9090909090909091, 0.761904761904762, 0.6956521739130435, 0.9, 0.5333333333333333]

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
  • train_batch_size: 20
  • eval_batch_size: 20
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss F1
No log 1.0 76 2.3773 [0.26666666666666666, 0.0, 0.0, 0.3870967741935484, 0.0, 0.4, 0.47058823529411764, 0.0, 0.39215686274509803, 0.18181818181818182, 0.16666666666666669, 0.14285714285714288, 0.20000000000000004, 0.3157894736842105, 0.3157894736842105, 0.45454545454545453, 0.28571428571428575, 0.608695652173913, 0.5185185185185185, 0.0]
No log 2.0 152 1.8409 [0.3529411764705882, 0.3333333333333333, 0.0, 0.4444444444444444, 0.0, 0.5714285714285713, 0.7499999999999999, 0.30769230769230765, 0.8571428571428572, 0.4285714285714285, 0.33333333333333326, 0.3, 0.7, 0.4, 0.47058823529411764, 0.6666666666666666, 0.5714285714285714, 0.5714285714285713, 0.5714285714285713, 0.4]
No log 3.0 228 1.5677 [0.4000000000000001, 0.5217391304347826, 0.4, 0.6250000000000001, 0.0, 0.6666666666666666, 0.6666666666666666, 0.28571428571428575, 0.9, 0.7, 0.22222222222222224, 0.5454545454545454, 0.761904761904762, 0.37499999999999994, 0.7, 0.6451612903225806, 0.5833333333333334, 0.7, 0.64, 0.588235294117647]
No log 4.0 304 1.3527 [0.45714285714285713, 0.5714285714285715, 0.6666666666666666, 0.6666666666666666, 0.36363636363636365, 0.761904761904762, 0.7058823529411764, 0.4, 0.8181818181818182, 0.7826086956521738, 0.25, 0.631578947368421, 0.8000000000000002, 0.4444444444444445, 0.7777777777777777, 0.8, 0.6923076923076923, 0.7368421052631577, 0.8235294117647058, 0.5]
No log 5.0 380 1.2553 [0.5833333333333334, 0.6, 0.8571428571428571, 0.75, 0.4444444444444445, 0.761904761904762, 0.7499999999999999, 0.37499999999999994, 0.9, 0.625, 0.28571428571428564, 0.56, 0.7777777777777777, 0.608695652173913, 0.7, 0.75, 0.8181818181818182, 0.7272727272727272, 0.8571428571428572, 0.5333333333333333]
No log 6.0 456 1.1302 [0.5833333333333334, 0.7, 0.6666666666666666, 0.5714285714285714, 0.4, 0.761904761904762, 0.7499999999999999, 0.37499999999999994, 0.9, 0.7368421052631577, 0.4000000000000001, 0.6666666666666667, 0.7368421052631577, 0.45454545454545453, 0.7368421052631577, 0.8695652173913044, 0.6666666666666667, 0.8000000000000002, 0.8571428571428572, 0.4444444444444445]
1.5569 7.0 532 1.0635 [0.5833333333333334, 0.6363636363636365, 0.8571428571428571, 0.823529411764706, 0.5, 0.761904761904762, 0.8235294117647058, 0.37499999999999994, 0.9, 0.6666666666666665, 0.47058823529411764, 0.7368421052631579, 0.8181818181818182, 0.47619047619047616, 0.8000000000000002, 0.8333333333333333, 0.8000000000000002, 0.7272727272727272, 0.8181818181818182, 0.5333333333333333]
1.5569 8.0 608 1.0725 [0.5185185185185185, 0.6666666666666666, 0.8571428571428571, 0.75, 0.4, 0.8000000000000002, 0.8235294117647058, 0.47058823529411764, 0.9, 0.7058823529411764, 0.5, 0.608695652173913, 0.7826086956521738, 0.47619047619047616, 0.761904761904762, 0.9090909090909091, 0.8000000000000002, 0.7272727272727272, 0.8571428571428572, 0.5333333333333333]
1.5569 9.0 684 1.0536 [0.5185185185185185, 0.7, 0.6666666666666666, 0.823529411764706, 0.5, 0.761904761904762, 0.8235294117647058, 0.37499999999999994, 0.9, 0.7058823529411764, 0.47058823529411764, 0.6363636363636364, 0.7272727272727272, 0.47619047619047616, 0.7272727272727272, 0.9523809523809523, 0.761904761904762, 0.6956521739130435, 0.9473684210526316, 0.5333333333333333]
1.5569 10.0 760 1.0270 [0.56, 0.7368421052631577, 0.6666666666666666, 0.823529411764706, 0.5, 0.761904761904762, 0.8235294117647058, 0.37499999999999994, 0.9, 0.7368421052631577, 0.47058823529411764, 0.6666666666666667, 0.7272727272727272, 0.45454545454545453, 0.761904761904762, 0.9090909090909091, 0.761904761904762, 0.6956521739130435, 0.9, 0.5333333333333333]

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2