--- 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](https://huggingface.co/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