Hugging Face's logo

tjl223
/
song-artist-classifier-v12-wd-0-02-bs-24

Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
song-artist-classifier-v12-wd-0-02-bs-24 / README.md
tjl223's picture
tjl223
End of training
e03c244 verified
preview code
|
raw
history blame contribute delete
6.67 kB
metadata
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
  - generated_from_trainer
metrics:
  - f1
model-index:
  - name: song-artist-classifier-v12-wd-0-02-bs-24
    results: []

song-artist-classifier-v12-wd-0-02-bs-24

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: 1.0564
  • F1: [0.6, 0.5263157894736842, 0.6666666666666666, 0.7777777777777778, 0.6153846153846153, 0.7, 0.8235294117647058, 0.4285714285714285, 0.9090909090909091, 0.7777777777777777, 0.5, 0.608695652173913, 0.7826086956521738, 0.48, 0.8000000000000002, 0.9, 0.7777777777777777, 0.7272727272727272, 0.7826086956521738, 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: 24
  • eval_batch_size: 24
  • 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 64 2.4446 [0.25, 0.10526315789473685, 0.0, 0.2105263157894737, 0.0, 0.38095238095238093, 0.0, 0.18181818181818182, 0.5806451612903225, 0.16666666666666669, 0.21428571428571427, 0.0, 0.3157894736842105, 0.16666666666666669, 0.0, 0.5128205128205129, 0.30769230769230765, 0.5714285714285715, 0.3076923076923077, 0.0]
No log 2.0 128 1.9189 [0.4210526315789474, 0.15384615384615383, 0.0, 0.380952380952381, 0.0, 0.42857142857142855, 0.7499999999999999, 0.16666666666666669, 0.8181818181818182, 0.30769230769230765, 0.25, 0.3333333333333333, 0.608695652173913, 0.4210526315789474, 0.7777777777777777, 0.7692307692307693, 0.6153846153846154, 0.48, 0.33333333333333337, 0.5]
No log 3.0 192 1.5611 [0.45454545454545453, 0.3636363636363636, 0.0, 0.5, 0.0, 0.5217391304347826, 0.6666666666666666, 0.4, 0.8695652173913044, 0.37499999999999994, 0.20000000000000004, 0.35294117647058826, 0.6956521739130435, 0.3529411764705882, 0.7777777777777777, 0.8, 0.6956521739130435, 0.608695652173913, 0.5454545454545454, 0.588235294117647]
No log 4.0 256 1.3575 [0.5, 0.47058823529411764, 0.4, 0.5217391304347826, 0.0, 0.608695652173913, 0.7499999999999999, 0.4, 0.8, 0.761904761904762, 0.25, 0.6666666666666665, 0.7826086956521738, 0.37499999999999994, 0.7777777777777777, 0.75, 0.8571428571428572, 0.6363636363636365, 0.4615384615384615, 0.5]
No log 5.0 320 1.2731 [0.6666666666666666, 0.5454545454545454, 0.6666666666666666, 0.5217391304347826, 0.4, 0.7272727272727272, 0.8235294117647058, 0.4615384615384615, 0.9, 0.6666666666666665, 0.37499999999999994, 0.5217391304347827, 0.7826086956521738, 0.5263157894736842, 0.8421052631578948, 0.8333333333333333, 0.761904761904762, 0.5833333333333334, 0.5263157894736842, 0.5]
No log 6.0 384 1.1545 [0.5714285714285713, 0.5263157894736842, 0.6666666666666666, 0.5454545454545455, 0.36363636363636365, 0.6666666666666666, 0.8235294117647058, 0.4615384615384615, 0.8571428571428572, 0.8181818181818182, 0.33333333333333326, 0.7368421052631579, 0.8181818181818182, 0.5714285714285713, 0.7777777777777777, 0.7692307692307693, 0.8571428571428572, 0.7, 0.6, 0.5555555555555556]
No log 7.0 448 1.1245 [0.608695652173913, 0.5, 0.6666666666666666, 0.5714285714285715, 0.2, 0.6956521739130435, 0.8235294117647058, 0.4285714285714285, 0.8695652173913044, 0.625, 0.5, 0.7000000000000001, 0.75, 0.5454545454545454, 0.8000000000000002, 0.9473684210526316, 0.6666666666666665, 0.6666666666666666, 0.5217391304347826, 0.5]
1.4848 8.0 512 1.0839 [0.6666666666666666, 0.5, 0.6666666666666666, 0.7368421052631577, 0.36363636363636365, 0.761904761904762, 0.8235294117647058, 0.4, 0.9090909090909091, 0.7777777777777777, 0.5263157894736842, 0.608695652173913, 0.7826086956521738, 0.5454545454545454, 0.8000000000000002, 0.9, 0.7777777777777777, 0.7272727272727272, 0.7692307692307693, 0.30769230769230765]
1.4848 9.0 576 1.0375 [0.6363636363636365, 0.47619047619047616, 0.6666666666666666, 0.7000000000000001, 0.6153846153846153, 0.7, 0.8235294117647058, 0.4285714285714285, 0.9090909090909091, 0.7058823529411764, 0.5263157894736842, 0.608695652173913, 0.7826086956521738, 0.5454545454545454, 0.8000000000000002, 0.9, 0.8000000000000002, 0.6666666666666666, 0.6666666666666666, 0.5333333333333333]
1.4848 10.0 640 1.0564 [0.6, 0.5263157894736842, 0.6666666666666666, 0.7777777777777778, 0.6153846153846153, 0.7, 0.8235294117647058, 0.4285714285714285, 0.9090909090909091, 0.7777777777777777, 0.5, 0.608695652173913, 0.7826086956521738, 0.48, 0.8000000000000002, 0.9, 0.7777777777777777, 0.7272727272727272, 0.7826086956521738, 0.5333333333333333]

Framework versions

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