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Rziane
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model.no_expe.dia.1.A_dataset.v3_CAENNAIS_06.05.25

Transformers
PyTorch
TensorBoard
Safetensors
French
pyannet
speaker-diarization
speaker-segmentation
Generated from Trainer
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End of training

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  1. README.md +25 -25
  2. model.safetensors +1 -1
  3. pytorch_model.bin +1 -1
  4. runs/May14_13-41-03_V301V-JGRCC1.campus.unicaen.fr/events.out.tfevents.1747223057.V301V-JGRCC1.campus.unicaen.fr.1428182.1 +2 -2
README.md CHANGED
@@ -22,12 +22,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the CAENNAIS dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7138
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  - Model Preparation Time: 0.0041
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- - Der: 0.3034
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- - False Alarm: 0.1110
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- - Missed Detection: 0.0722
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- - Confusion: 0.1201
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  ## Model description
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@@ -58,26 +58,26 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion |
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  |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
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- | 0.8835 | 1.0 | 61 | 0.8779 | 0.0041 | 0.4058 | 0.1194 | 0.0841 | 0.2023 |
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- | 0.791 | 2.0 | 122 | 0.7942 | 0.0041 | 0.3738 | 0.1123 | 0.0782 | 0.1833 |
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- | 0.7215 | 3.0 | 183 | 0.7541 | 0.0041 | 0.3351 | 0.1057 | 0.0807 | 0.1488 |
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- | 0.6958 | 4.0 | 244 | 0.7191 | 0.0041 | 0.3253 | 0.1097 | 0.0881 | 0.1275 |
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- | 0.6843 | 5.0 | 305 | 0.7058 | 0.0041 | 0.3189 | 0.0914 | 0.0895 | 0.1380 |
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- | 0.6585 | 6.0 | 366 | 0.8104 | 0.0041 | 0.3539 | 0.1340 | 0.0743 | 0.1456 |
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- | 0.6434 | 7.0 | 427 | 0.6870 | 0.0041 | 0.3004 | 0.0893 | 0.0903 | 0.1209 |
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- | 0.6255 | 8.0 | 488 | 0.7036 | 0.0041 | 0.3109 | 0.1001 | 0.0776 | 0.1332 |
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- | 0.6152 | 9.0 | 549 | 0.7808 | 0.0041 | 0.3452 | 0.1326 | 0.0635 | 0.1490 |
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- | 0.5997 | 10.0 | 610 | 0.8720 | 0.0041 | 0.3769 | 0.1698 | 0.0460 | 0.1612 |
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- | 0.5899 | 11.0 | 671 | 0.7213 | 0.0041 | 0.3109 | 0.1091 | 0.0718 | 0.1300 |
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- | 0.5652 | 12.0 | 732 | 0.6669 | 0.0041 | 0.2912 | 0.0970 | 0.0830 | 0.1112 |
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- | 0.5397 | 13.0 | 793 | 0.6903 | 0.0041 | 0.3024 | 0.1092 | 0.0750 | 0.1182 |
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- | 0.5471 | 14.0 | 854 | 0.7170 | 0.0041 | 0.3058 | 0.1086 | 0.0762 | 0.1210 |
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- | 0.5339 | 15.0 | 915 | 0.7573 | 0.0041 | 0.3148 | 0.1183 | 0.0682 | 0.1282 |
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- | 0.5361 | 16.0 | 976 | 0.7255 | 0.0041 | 0.3095 | 0.1134 | 0.0719 | 0.1242 |
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- | 0.543 | 17.0 | 1037 | 0.6978 | 0.0041 | 0.2993 | 0.1075 | 0.0740 | 0.1177 |
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- | 0.5184 | 18.0 | 1098 | 0.7068 | 0.0041 | 0.3022 | 0.1100 | 0.0729 | 0.1193 |
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- | 0.5235 | 19.0 | 1159 | 0.7153 | 0.0041 | 0.3037 | 0.1113 | 0.0719 | 0.1205 |
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- | 0.5474 | 20.0 | 1220 | 0.7138 | 0.0041 | 0.3034 | 0.1110 | 0.0722 | 0.1201 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the CAENNAIS dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7422
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  - Model Preparation Time: 0.0041
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+ - Der: 0.2936
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+ - False Alarm: 0.0989
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+ - Missed Detection: 0.0787
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+ - Confusion: 0.1161
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion |
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  |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
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+ | 0.5698 | 1.0 | 61 | 0.6907 | 0.0041 | 0.3030 | 0.1004 | 0.0775 | 0.1251 |
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+ | 0.5535 | 2.0 | 122 | 0.6962 | 0.0041 | 0.3184 | 0.1013 | 0.0807 | 0.1364 |
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+ | 0.5454 | 3.0 | 183 | 0.6919 | 0.0041 | 0.3042 | 0.1009 | 0.0746 | 0.1286 |
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+ | 0.5389 | 4.0 | 244 | 0.6756 | 0.0041 | 0.2919 | 0.0948 | 0.0779 | 0.1192 |
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+ | 0.553 | 5.0 | 305 | 0.6989 | 0.0041 | 0.2866 | 0.1044 | 0.0690 | 0.1132 |
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+ | 0.5181 | 6.0 | 366 | 0.6659 | 0.0041 | 0.2838 | 0.0953 | 0.0770 | 0.1116 |
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+ | 0.5094 | 7.0 | 427 | 0.6748 | 0.0041 | 0.2791 | 0.0924 | 0.0777 | 0.1090 |
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+ | 0.4743 | 8.0 | 488 | 0.6478 | 0.0041 | 0.2750 | 0.0885 | 0.0826 | 0.1039 |
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+ | 0.4937 | 9.0 | 549 | 0.7770 | 0.0041 | 0.3219 | 0.1185 | 0.0674 | 0.1359 |
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+ | 0.4631 | 10.0 | 610 | 0.7151 | 0.0041 | 0.2945 | 0.0984 | 0.0772 | 0.1189 |
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+ | 0.4519 | 11.0 | 671 | 0.6885 | 0.0041 | 0.2790 | 0.0931 | 0.0758 | 0.1101 |
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+ | 0.4389 | 12.0 | 732 | 0.7073 | 0.0041 | 0.2901 | 0.0913 | 0.0812 | 0.1177 |
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+ | 0.426 | 13.0 | 793 | 0.7431 | 0.0041 | 0.2985 | 0.1082 | 0.0710 | 0.1194 |
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+ | 0.4367 | 14.0 | 854 | 0.7154 | 0.0041 | 0.2891 | 0.0838 | 0.0861 | 0.1192 |
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+ | 0.4129 | 15.0 | 915 | 0.7112 | 0.0041 | 0.2831 | 0.0900 | 0.0800 | 0.1131 |
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+ | 0.4381 | 16.0 | 976 | 0.7039 | 0.0041 | 0.2760 | 0.0819 | 0.0885 | 0.1056 |
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+ | 0.4185 | 17.0 | 1037 | 0.7338 | 0.0041 | 0.2899 | 0.0925 | 0.0823 | 0.1151 |
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+ | 0.4194 | 18.0 | 1098 | 0.7542 | 0.0041 | 0.2953 | 0.1002 | 0.0783 | 0.1168 |
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+ | 0.4163 | 19.0 | 1159 | 0.7440 | 0.0041 | 0.2939 | 0.0994 | 0.0781 | 0.1165 |
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+ | 0.4319 | 20.0 | 1220 | 0.7422 | 0.0041 | 0.2936 | 0.0989 | 0.0787 | 0.1161 |
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  ### Framework versions
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