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-v3-roberta
+  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-v3-roberta
+
+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: 1.0147
+- F1: [0.8571428571428572, 0.5714285714285714, 0.888888888888889, 0.8181818181818182, 0.8571428571428571, 0.4, 0.8571428571428572, 0.7777777777777777, 0.5, 0.7, 0.5333333333333333, 0.6956521739130436, 0.7826086956521738, 0.8235294117647058, 0.5, 0.8000000000000002, 0.8181818181818182, 0.9, 0.47058823529411764, 0.608695652173913]
+
+## 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                                                                                                                                                                                                                                                                                                                                                                                                  |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 95   | 2.2772          | [0.15384615384615383, 0.41379310344827586, 0.0, 0.45454545454545453, 0.0, 0.16666666666666669, 0.14285714285714288, 0.3684210526315789, 0.0, 0.16666666666666669, 0.0, 0.0, 0.13333333333333333, 0.30769230769230765, 0.16666666666666666, 0.358974358974359, 0.4615384615384615, 0.3846153846153846, 0.16666666666666669, 0.15384615384615383]                                                     |
+| No log        | 2.0   | 190  | 1.7800          | [0.6666666666666665, 0.43478260869565216, 0.4347826086956522, 0.4000000000000001, 0.0, 0.0, 0.6451612903225806, 0.608695652173913, 0.3636363636363636, 0.18181818181818182, 0.22222222222222224, 0.125, 0.5555555555555556, 0.7368421052631577, 0.22222222222222224, 0.5217391304347826, 0.631578947368421, 0.8571428571428572, 0.37037037037037035, 0.28571428571428564]                           |
+| No log        | 3.0   | 285  | 1.4763          | [0.7777777777777777, 0.4761904761904762, 0.6, 0.588235294117647, 0.6666666666666666, 0.4285714285714285, 0.6451612903225806, 0.631578947368421, 0.33333333333333326, 0.631578947368421, 0.36363636363636365, 0.6363636363636364, 0.8421052631578948, 0.7058823529411764, 0.4, 0.6956521739130435, 0.64, 0.8571428571428572, 0.5, 0.5]                                                               |
+| No log        | 4.0   | 380  | 1.2157          | [0.8571428571428572, 0.4444444444444444, 0.7499999999999999, 0.5263157894736842, 0.6666666666666666, 0.37499999999999994, 0.8333333333333333, 0.7272727272727272, 0.26666666666666666, 0.6666666666666666, 0.4, 0.6399999999999999, 0.761904761904762, 0.8235294117647058, 0.5454545454545454, 0.7826086956521738, 0.8000000000000002, 0.9523809523809523, 0.47058823529411764, 0.6363636363636365] |
+| No log        | 5.0   | 475  | 1.1712          | [0.7826086956521738, 0.6086956521739131, 0.761904761904762, 0.5555555555555556, 0.8571428571428571, 0.37499999999999994, 0.8571428571428572, 0.7368421052631577, 0.4000000000000001, 0.7368421052631577, 0.4615384615384615, 0.7272727272727273, 0.9, 0.8235294117647058, 0.47619047619047616, 0.9, 0.8571428571428572, 0.9, 0.30769230769230765, 0.6363636363636365]                               |
+| 1.6275        | 6.0   | 570  | 1.0779          | [0.8571428571428572, 0.46153846153846156, 0.888888888888889, 0.75, 0.8571428571428571, 0.37499999999999994, 0.9090909090909091, 0.7777777777777777, 0.4210526315789474, 0.7, 0.5714285714285714, 0.6956521739130436, 0.7826086956521738, 0.7499999999999999, 0.5263157894736842, 0.8000000000000002, 0.7272727272727272, 0.8571428571428572, 0.4444444444444445, 0.5454545454545454]                |
+| 1.6275        | 7.0   | 665  | 1.0801          | [0.8181818181818182, 0.5555555555555556, 0.8421052631578948, 0.7777777777777777, 0.8571428571428571, 0.4285714285714285, 0.8571428571428572, 0.7777777777777777, 0.5, 0.7777777777777777, 0.5333333333333333, 0.761904761904762, 0.75, 0.8235294117647058, 0.45454545454545453, 0.6666666666666665, 0.7826086956521738, 0.888888888888889, 0.5555555555555556, 0.5714285714285713]                  |
+| 1.6275        | 8.0   | 760  | 1.0020          | [0.8571428571428572, 0.46153846153846156, 0.8421052631578948, 0.8181818181818182, 0.8571428571428571, 0.4, 0.9, 0.7777777777777777, 0.47619047619047616, 0.7, 0.5333333333333333, 0.7272727272727273, 0.7272727272727272, 0.8235294117647058, 0.5714285714285713, 0.7272727272727272, 0.7272727272727272, 0.9523809523809523, 0.4210526315789474, 0.5263157894736842]                               |
+| 1.6275        | 9.0   | 855  | 1.0129          | [0.8571428571428572, 0.5714285714285714, 0.888888888888889, 0.8571428571428572, 0.8571428571428571, 0.4, 0.8571428571428572, 0.7777777777777777, 0.5714285714285713, 0.6666666666666666, 0.5333333333333333, 0.7272727272727273, 0.7826086956521738, 0.8235294117647058, 0.5263157894736842, 0.761904761904762, 0.7272727272727272, 0.9473684210526316, 0.5263157894736842, 0.5454545454545454]     |
+| 1.6275        | 10.0  | 950  | 1.0147          | [0.8571428571428572, 0.5714285714285714, 0.888888888888889, 0.8181818181818182, 0.8571428571428571, 0.4, 0.8571428571428572, 0.7777777777777777, 0.5, 0.7, 0.5333333333333333, 0.6956521739130436, 0.7826086956521738, 0.8235294117647058, 0.5, 0.8000000000000002, 0.8181818181818182, 0.9, 0.47058823529411764, 0.608695652173913]                                                                |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

model.safetensors

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