End of training

README.md

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+---
+license: mit
+base_model: pyannote/segmentation-3.0
+tags:
+- speaker-diarization
+- speaker-segmentation
+- generated_from_trainer
+datasets:
+- ArtFair/diarizers_dataset_70-15-15
+model-index:
+- name: fine_tuned_segmentation-3.0_1e-5_32
+  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. -->
+
+# fine_tuned_segmentation-3.0_1e-5_32
+
+This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the ArtFair/diarizers_dataset_70-15-15 default dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4908
+- Der: 0.3585
+- False Alarm: 0.2041
+- Missed Detection: 0.1214
+- Confusion: 0.0330
+
+## 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: 1e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- num_epochs: 5.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.621         | 1.0   | 929  | 0.5601          | 0.3774 | 0.2133      | 0.1279           | 0.0361    |
+| 0.5472        | 2.0   | 1858 | 0.5149          | 0.3680 | 0.2084      | 0.1254           | 0.0342    |
+| 0.5353        | 3.0   | 2787 | 0.4969          | 0.3615 | 0.2049      | 0.1234           | 0.0333    |
+| 0.517         | 4.0   | 3716 | 0.4919          | 0.3590 | 0.2042      | 0.1216           | 0.0331    |
+| 0.5217        | 5.0   | 4645 | 0.4908          | 0.3585 | 0.2041      | 0.1214           | 0.0330    |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.4.1+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

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