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multilabel_classification

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lucienbaumgartner/mtg-spike-multilabel-distilbert

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  1. README.md +21 -17
  2. adapter_model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -5,6 +5,8 @@ tags:
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  - generated_from_trainer
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  base_model: distilbert-base-uncased
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  metrics:
 
 
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  - accuracy
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  model-index:
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  - name: multilabel_classification
@@ -18,11 +20,13 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2725
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- - F1 Micro: 0.6677
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- - F1 Macro: 0.6644
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- - F1 Weighted: 0.6651
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- - Accuracy: 0.5900
 
 
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  ## Model description
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | F1 Weighted | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:--------:|
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- | No log | 1.0 | 406 | 0.3793 | 0.0637 | 0.0619 | 0.0621 | 0.1413 |
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- | 0.4136 | 2.0 | 812 | 0.2789 | 0.6272 | 0.6225 | 0.6236 | 0.5346 |
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- | 0.2499 | 3.0 | 1218 | 0.2725 | 0.6677 | 0.6644 | 0.6651 | 0.5900 |
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- | 0.2017 | 4.0 | 1624 | 0.2744 | 0.675 | 0.6716 | 0.6721 | 0.6066 |
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- | 0.1853 | 5.0 | 2030 | 0.2745 | 0.6833 | 0.6797 | 0.6805 | 0.6122 |
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- | 0.1853 | 6.0 | 2436 | 0.2771 | 0.6781 | 0.6764 | 0.6770 | 0.6177 |
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- | 0.1621 | 7.0 | 2842 | 0.2810 | 0.6980 | 0.6962 | 0.6970 | 0.6343 |
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- | 0.1536 | 8.0 | 3248 | 0.2832 | 0.6998 | 0.6982 | 0.6988 | 0.6427 |
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- | 0.1414 | 9.0 | 3654 | 0.2843 | 0.6958 | 0.6943 | 0.6948 | 0.6371 |
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- | 0.1429 | 10.0 | 4060 | 0.2861 | 0.6928 | 0.6905 | 0.6911 | 0.6316 |
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  ### Framework versions
 
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  - generated_from_trainer
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  base_model: distilbert-base-uncased
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  metrics:
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+ - precision
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+ - recall
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  - accuracy
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  model-index:
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  - name: multilabel_classification
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3549
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+ - F1 Micro: 0.8864
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+ - F1 Macro: 0.8088
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+ - F1 Weighted: 0.8819
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+ - Precision: 0.8811
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+ - Recall: 0.8864
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+ - Accuracy: 0.8864
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | F1 Weighted | Precision | Recall | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------:|:------:|:--------:|
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+ | No log | 1.0 | 406 | 0.3510 | 0.8886 | 0.8121 | 0.8841 | 0.8834 | 0.8886 | 0.8886 |
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+ | 0.082 | 2.0 | 812 | 0.3618 | 0.8909 | 0.8193 | 0.8875 | 0.8864 | 0.8909 | 0.8909 |
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+ | 0.0848 | 3.0 | 1218 | 0.3768 | 0.8925 | 0.8206 | 0.8887 | 0.8879 | 0.8925 | 0.8925 |
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+ | 0.0703 | 4.0 | 1624 | 0.3706 | 0.8953 | 0.8242 | 0.8913 | 0.8908 | 0.8953 | 0.8953 |
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+ | 0.0806 | 5.0 | 2030 | 0.3868 | 0.8903 | 0.8169 | 0.8864 | 0.8855 | 0.8903 | 0.8903 |
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+ | 0.0806 | 6.0 | 2436 | 0.3988 | 0.8920 | 0.8211 | 0.8887 | 0.8876 | 0.8920 | 0.8920 |
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+ | 0.0721 | 7.0 | 2842 | 0.4085 | 0.8898 | 0.8174 | 0.8864 | 0.8853 | 0.8898 | 0.8898 |
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+ | 0.0701 | 8.0 | 3248 | 0.4035 | 0.8881 | 0.8149 | 0.8847 | 0.8835 | 0.8881 | 0.8881 |
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+ | 0.0704 | 9.0 | 3654 | 0.4072 | 0.8886 | 0.8152 | 0.8851 | 0.8840 | 0.8886 | 0.8886 |
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+ | 0.0665 | 10.0 | 4060 | 0.4010 | 0.8909 | 0.8184 | 0.8872 | 0.8863 | 0.8909 | 0.8909 |
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  ### Framework versions
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