End of training

README.md

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+---
+license: apache-2.0
+base_model: distilbert/distilroberta-base
+tags:
+- generated_from_trainer
+metrics:
+- f1
+model-index:
+- name: song-artist-classifier-v15-segmented_data
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# song-artist-classifier-v15-segmented_data
+
+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: 1.3400
+- F1: [0.8235294117647058, 0.7142857142857143, 0.6666666666666665, 0.8571428571428571, 0.5714285714285713, 0.631578947368421, 0.47058823529411764, 0.8235294117647058, 0.8, 0.4210526315789474, 0.8000000000000002, 0.7272727272727272, 0.18181818181818182, 0.9, 0.4375, 0.7142857142857143, 0.8421052631578948, 0.26666666666666666, 0.625, 0.631578947368421]
+
+## 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: 16
+- eval_batch_size: 16
+- 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                                                                                                                                                                                                                                                                                                                                                                      |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| 2.3819        | 1.0   | 601  | 1.7801          | [0.16666666666666669, 0.19999999999999998, 0.375, 0.6666666666666666, 0.4347826086956522, 0.16666666666666669, 0.3333333333333333, 0.7499999999999999, 0.7200000000000001, 0.125, 0.631578947368421, 0.5, 0.18181818181818182, 0.5, 0.2777777777777778, 0.4375, 0.7272727272727272, 0.0, 0.0, 0.588235294117647]                                                        |
+| 1.693         | 2.0   | 1202 | 1.2739          | [0.8421052631578948, 0.3636363636363636, 0.7999999999999999, 0.8571428571428571, 0.34782608695652173, 0.6153846153846154, 0.5263157894736842, 0.6666666666666666, 0.8571428571428572, 0.33333333333333326, 0.8421052631578948, 0.6363636363636365, 0.4, 0.9, 0.5, 0.7200000000000001, 0.7777777777777777, 0.0, 0.5714285714285713, 0.5454545454545454]                  |
+| 1.253         | 3.0   | 1803 | 1.1600          | [0.888888888888889, 0.5, 0.625, 0.8571428571428571, 0.47058823529411764, 0.6666666666666665, 0.47619047619047616, 0.888888888888889, 0.8333333333333333, 0.37499999999999994, 0.8421052631578948, 0.7058823529411764, 0.18181818181818182, 0.8181818181818182, 0.6, 0.6428571428571429, 0.8000000000000002, 0.25, 0.5217391304347826, 0.6666666666666666]               |
+| 0.9138        | 4.0   | 2404 | 1.3108          | [0.7499999999999999, 0.3636363636363636, 0.7000000000000001, 0.75, 0.47058823529411764, 0.5454545454545454, 0.4000000000000001, 0.888888888888889, 0.7692307692307693, 0.125, 0.761904761904762, 0.6666666666666666, 0.30769230769230765, 0.9, 0.42857142857142855, 0.6060606060606061, 0.7272727272727272, 0.22222222222222224, 0.4444444444444445, 0.588235294117647] |
+| 0.4867        | 5.0   | 3005 | 1.3483          | [0.888888888888889, 0.5, 0.7368421052631579, 0.75, 0.6666666666666666, 0.5217391304347826, 0.5263157894736842, 0.8235294117647058, 0.7200000000000001, 0.33333333333333326, 0.7368421052631577, 0.631578947368421, 0.33333333333333337, 0.7200000000000001, 0.41379310344827586, 0.625, 0.761904761904762, 0.22222222222222224, 0.18181818181818182, 0.631578947368421] |
+| 0.3353        | 6.0   | 3606 | 1.3618          | [0.9473684210526316, 0.7142857142857143, 0.625, 0.8571428571428571, 0.3529411764705882, 0.3529411764705882, 0.5, 0.7777777777777777, 0.7407407407407407, 0.13333333333333333, 0.7777777777777777, 0.6363636363636365, 0.26666666666666666, 0.9, 0.5, 0.4878048780487805, 0.6666666666666665, 0.2, 0.5263157894736842, 0.5263157894736842]                               |
+| 0.2703        | 7.0   | 4207 | 1.3844          | [0.888888888888889, 0.5, 0.7142857142857143, 0.8571428571428571, 0.5, 0.5, 0.4000000000000001, 0.7777777777777777, 0.8, 0.25, 0.8421052631578948, 0.6666666666666666, 0.16666666666666669, 0.9, 0.47058823529411764, 0.625, 0.7058823529411764, 0.18181818181818182, 0.6666666666666665, 0.7368421052631577]                                                            |
+| 0.1997        | 8.0   | 4808 | 1.4064          | [0.888888888888889, 0.6153846153846153, 0.4615384615384615, 0.8571428571428571, 0.5, 0.45454545454545453, 0.5263157894736842, 0.761904761904762, 0.8, 0.5, 0.7777777777777777, 0.6666666666666666, 0.15384615384615383, 0.9, 0.5333333333333333, 0.5714285714285715, 0.6666666666666666, 0.3333333333333333, 0.625, 0.6666666666666665]                                 |
+| 0.1635        | 9.0   | 5409 | 1.4331          | [0.8235294117647058, 0.6153846153846153, 0.7058823529411765, 0.8571428571428571, 0.47619047619047616, 0.5, 0.4444444444444445, 0.7777777777777777, 0.8, 0.4444444444444445, 0.7368421052631577, 0.761904761904762, 0.16666666666666669, 0.9, 0.48484848484848486, 0.5882352941176471, 0.6666666666666666, 0.2857142857142857, 0.7058823529411764, 0.588235294117647]    |
+| 0.112         | 10.0  | 6010 | 1.3400          | [0.8235294117647058, 0.7142857142857143, 0.6666666666666665, 0.8571428571428571, 0.5714285714285713, 0.631578947368421, 0.47058823529411764, 0.8235294117647058, 0.8, 0.4210526315789474, 0.8000000000000002, 0.7272727272727272, 0.18181818181818182, 0.9, 0.4375, 0.7142857142857143, 0.8421052631578948, 0.26666666666666666, 0.625, 0.631578947368421]              |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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