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:
+- sparkleai/Diarizers_dataset_test
+model-index:
+- name: Diarizers_finetuned_model_test
+  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. -->
+
+# Diarizers_finetuned_model_test
+
+This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the sparkleai/Diarizers_dataset_test default dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7692
+- Der: 0.2484
+- False Alarm: 0.0450
+- Missed Detection: 0.0910
+- Confusion: 0.1124
+
+## 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: 0.001
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
+| No log        | 1.0   | 6    | 0.8050          | 0.2813 | 0.0708      | 0.1119           | 0.0986    |
+| No log        | 2.0   | 12   | 0.7748          | 0.2592 | 0.0547      | 0.0991           | 0.1054    |
+| No log        | 3.0   | 18   | 0.7718          | 0.2502 | 0.0447      | 0.0941           | 0.1114    |
+| No log        | 4.0   | 24   | 0.7677          | 0.2484 | 0.0448      | 0.0915           | 0.1121    |
+| No log        | 5.0   | 30   | 0.7692          | 0.2484 | 0.0450      | 0.0910           | 0.1124    |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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