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distilbert-finetuned-gesture-prediction-21-classes

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  1. README.md +16 -16
  2. model.safetensors +1 -1
  3. runs/May07_18-36-10_7570e4083a5e/events.out.tfevents.1715106977.7570e4083a5e.601.7 +2 -2
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.2008
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- - Precision: 0.7823
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- - Recall: 0.7823
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- - F1: 0.7823
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- - Accuracy: 0.7745
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  ## Model description
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@@ -55,21 +55,21 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.4985 | 1.0 | 26 | 1.0570 | 0.7288 | 0.7288 | 0.7288 | 0.7177 |
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- | 0.2387 | 2.0 | 52 | 1.0720 | 0.7775 | 0.7775 | 0.7775 | 0.7672 |
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- | 0.1348 | 3.0 | 78 | 1.0681 | 0.7775 | 0.7775 | 0.7775 | 0.7682 |
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- | 0.0811 | 4.0 | 104 | 1.1143 | 0.7884 | 0.7884 | 0.7884 | 0.7781 |
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- | 0.0565 | 5.0 | 130 | 1.1128 | 0.7867 | 0.7867 | 0.7867 | 0.7783 |
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- | 0.0419 | 6.0 | 156 | 1.1051 | 0.7966 | 0.7966 | 0.7966 | 0.7880 |
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- | 0.0257 | 7.0 | 182 | 1.1567 | 0.7839 | 0.7839 | 0.7839 | 0.7737 |
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- | 0.0217 | 8.0 | 208 | 1.1675 | 0.7859 | 0.7859 | 0.7859 | 0.7757 |
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- | 0.0145 | 9.0 | 234 | 1.1976 | 0.7847 | 0.7847 | 0.7847 | 0.7769 |
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- | 0.0115 | 10.0 | 260 | 1.2008 | 0.7823 | 0.7823 | 0.7823 | 0.7745 |
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  ### Framework versions
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  - Transformers 4.40.1
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  - Pytorch 2.2.1+cu121
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- - Datasets 2.19.0
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  - Tokenizers 0.19.1
 
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  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.0324
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+ - Precision: 0.8139
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+ - Recall: 0.8139
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+ - F1: 0.8139
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+ - Accuracy: 0.8022
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 2.1035 | 1.0 | 26 | 1.2829 | 0.7042 | 0.7042 | 0.7042 | 0.6806 |
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+ | 0.9959 | 2.0 | 52 | 0.9482 | 0.7756 | 0.7756 | 0.7756 | 0.7621 |
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+ | 0.6164 | 3.0 | 78 | 0.8716 | 0.7849 | 0.7849 | 0.7849 | 0.7685 |
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+ | 0.3812 | 4.0 | 104 | 0.8710 | 0.8004 | 0.8004 | 0.8004 | 0.7867 |
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+ | 0.2325 | 5.0 | 130 | 0.9558 | 0.7916 | 0.7916 | 0.7916 | 0.7788 |
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+ | 0.1558 | 6.0 | 156 | 0.9310 | 0.8077 | 0.8077 | 0.8077 | 0.7949 |
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+ | 0.0983 | 7.0 | 182 | 0.9989 | 0.8121 | 0.8121 | 0.8121 | 0.7992 |
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+ | 0.0697 | 8.0 | 208 | 1.0241 | 0.8083 | 0.8083 | 0.8083 | 0.7963 |
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+ | 0.05 | 9.0 | 234 | 1.0352 | 0.8110 | 0.8110 | 0.8110 | 0.7991 |
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+ | 0.0403 | 10.0 | 260 | 1.0324 | 0.8139 | 0.8139 | 0.8139 | 0.8022 |
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  ### Framework versions
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  - Transformers 4.40.1
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  - Pytorch 2.2.1+cu121
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+ - Datasets 2.19.1
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  - Tokenizers 0.19.1
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