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SWiedemann
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distilbert-base-uncased-finetuned-emotion

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@@ -23,10 +23,10 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.89
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  - name: F1
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  type: f1
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- value: 0.8909727258350819
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -36,10 +36,10 @@ 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 emotion dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6248
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- - Accuracy: 0.89
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- - Balanced accuracy: 0.8764
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- - F1: 0.8910
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  ## Model description
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@@ -70,26 +70,26 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Balanced accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:------:|
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- | 0.0269 | 1.0 | 25 | 0.4880 | 0.905 | 0.8890 | 0.9058 |
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- | 0.0204 | 2.0 | 50 | 0.5177 | 0.89 | 0.8934 | 0.8896 |
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- | 0.009 | 3.0 | 75 | 0.4983 | 0.89 | 0.8787 | 0.8911 |
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- | 0.0089 | 4.0 | 100 | 0.5681 | 0.895 | 0.8724 | 0.8947 |
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- | 0.0048 | 5.0 | 125 | 0.5800 | 0.88 | 0.8662 | 0.8819 |
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- | 0.0023 | 6.0 | 150 | 0.5706 | 0.89 | 0.8959 | 0.8917 |
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- | 0.0035 | 7.0 | 175 | 0.6086 | 0.895 | 0.8760 | 0.8955 |
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- | 0.006 | 8.0 | 200 | 0.6522 | 0.88 | 0.9011 | 0.8811 |
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- | 0.0017 | 9.0 | 225 | 0.5806 | 0.89 | 0.8715 | 0.8907 |
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- | 0.0014 | 10.0 | 250 | 0.5809 | 0.885 | 0.9001 | 0.8868 |
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- | 0.0011 | 11.0 | 275 | 0.5942 | 0.885 | 0.8729 | 0.8864 |
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- | 0.001 | 12.0 | 300 | 0.5997 | 0.895 | 0.8826 | 0.8963 |
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- | 0.0009 | 13.0 | 325 | 0.6006 | 0.89 | 0.8791 | 0.8912 |
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- | 0.001 | 14.0 | 350 | 0.6135 | 0.885 | 0.9013 | 0.8857 |
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- | 0.0009 | 15.0 | 375 | 0.6199 | 0.885 | 0.8740 | 0.8858 |
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- | 0.0008 | 16.0 | 400 | 0.6257 | 0.885 | 0.8740 | 0.8858 |
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- | 0.0007 | 17.0 | 425 | 0.6254 | 0.885 | 0.8740 | 0.8858 |
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- | 0.0007 | 18.0 | 450 | 0.6273 | 0.885 | 0.8740 | 0.8858 |
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- | 0.0007 | 19.0 | 475 | 0.6248 | 0.885 | 0.8740 | 0.8858 |
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- | 0.0007 | 20.0 | 500 | 0.6248 | 0.89 | 0.8764 | 0.8910 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.895
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  - name: F1
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  type: f1
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+ value: 0.8961058726378275
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7264
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+ - Accuracy: 0.895
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+ - Balanced accuracy: 0.8746
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+ - F1: 0.8961
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Balanced accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:------:|
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+ | 0.001 | 1.0 | 25 | 0.7713 | 0.89 | 0.8807 | 0.8915 |
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+ | 0.0069 | 2.0 | 50 | 0.7734 | 0.905 | 0.8906 | 0.9070 |
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+ | 0.0019 | 3.0 | 75 | 0.8670 | 0.88 | 0.8749 | 0.8819 |
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+ | 0.0012 | 4.0 | 100 | 0.7387 | 0.895 | 0.8806 | 0.8953 |
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+ | 0.0002 | 5.0 | 125 | 0.7841 | 0.885 | 0.8649 | 0.8858 |
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+ | 0.0002 | 6.0 | 150 | 0.7415 | 0.9 | 0.8753 | 0.9001 |
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+ | 0.0002 | 7.0 | 175 | 0.7378 | 0.895 | 0.8719 | 0.8955 |
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+ | 0.0002 | 8.0 | 200 | 0.7452 | 0.89 | 0.8711 | 0.8910 |
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+ | 0.0002 | 9.0 | 225 | 0.7555 | 0.89 | 0.8787 | 0.8908 |
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+ | 0.0001 | 10.0 | 250 | 0.7541 | 0.895 | 0.8822 | 0.8959 |
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+ | 0.0001 | 11.0 | 275 | 0.7536 | 0.9 | 0.8857 | 0.9009 |
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+ | 0.0001 | 12.0 | 300 | 0.7530 | 0.9 | 0.8857 | 0.9009 |
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+ | 0.0001 | 13.0 | 325 | 0.7542 | 0.9 | 0.8857 | 0.9009 |
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+ | 0.0001 | 14.0 | 350 | 0.7532 | 0.895 | 0.8746 | 0.8957 |
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+ | 0.0002 | 15.0 | 375 | 0.8554 | 0.88 | 0.8424 | 0.8803 |
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+ | 0.0001 | 16.0 | 400 | 0.7700 | 0.9 | 0.8867 | 0.9011 |
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+ | 0.0001 | 17.0 | 425 | 0.7302 | 0.895 | 0.8746 | 0.8961 |
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+ | 0.0001 | 18.0 | 450 | 0.7304 | 0.895 | 0.8746 | 0.8961 |
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+ | 0.0001 | 19.0 | 475 | 0.7284 | 0.895 | 0.8746 | 0.8961 |
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+ | 0.0001 | 20.0 | 500 | 0.7264 | 0.895 | 0.8746 | 0.8961 |
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  ### Framework versions