End of training

README.md

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+---
+library_name: transformers
+language:
+- id
+license: mit
+base_model: pyannote/speaker-diarization-3.1
+tags:
+- speaker-diarization
+- speaker-segmentation
+- generated_from_trainer
+datasets:
+- speaker-segmentation
+model-index:
+- name: speaker-segmentation-fine-tuned-datasetID-hugging_2_4
+  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. -->
+
+# speaker-segmentation-fine-tuned-datasetID-hugging_2_4
+
+This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the speaker-segmentation dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4833
+- Model Preparation Time: 0.014
+- Der: 0.1592
+- False Alarm: 0.0257
+- Missed Detection: 0.0139
+- Confusion: 0.1196
+
+## 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: 43
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.4966        | 1.0   | 104  | 0.5339          | 0.014                  | 0.1742 | 0.0272      | 0.0153           | 0.1317    |
+| 0.4372        | 2.0   | 208  | 0.4908          | 0.014                  | 0.1606 | 0.0257      | 0.0139           | 0.1210    |
+| 0.4143        | 3.0   | 312  | 0.4868          | 0.014                  | 0.1607 | 0.0251      | 0.0146           | 0.1211    |
+| 0.3953        | 4.0   | 416  | 0.4840          | 0.014                  | 0.1600 | 0.0258      | 0.0138           | 0.1204    |
+| 0.3828        | 5.0   | 520  | 0.4833          | 0.014                  | 0.1592 | 0.0257      | 0.0139           | 0.1196    |
+
+
+### Framework versions
+
+- Transformers 4.50.3
+- Pytorch 2.6.0+cu124
+- Datasets 3.5.0
+- Tokenizers 0.21.1

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