End of training

README.md

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+---
+library_name: transformers
+language:
+- en
+license: mit
+base_model: pyannote/speaker-diarization-3.1
+tags:
+- speaker-diarization
+- speaker-segmentation
+- generated_from_trainer
+datasets:
+- diarizers-community/voxconverse
+model-index:
+- name: JSWOOK/pyannote_finetuning
+  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. -->
+
+# JSWOOK/pyannote_finetuning
+
+This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the diarizers-community/voxconverse dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1283
+- Model Preparation Time: 0.0036
+- Der: 0.0490
+- False Alarm: 0.0309
+- Missed Detection: 0.0091
+- Confusion: 0.0090
+
+## 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
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
+| No log        | 1.0   | 21   | 0.1258          | 0.0036                 | 0.0485 | 0.0287      | 0.0105           | 0.0093    |
+| 0.228         | 2.0   | 42   | 0.1327          | 0.0036                 | 0.0509 | 0.0300      | 0.0098           | 0.0112    |
+| 0.1873        | 3.0   | 63   | 0.1280          | 0.0036                 | 0.0496 | 0.0307      | 0.0092           | 0.0097    |
+| 0.166         | 4.0   | 84   | 0.1280          | 0.0036                 | 0.0487 | 0.0307      | 0.0091           | 0.0090    |
+| 0.152         | 5.0   | 105  | 0.1283          | 0.0036                 | 0.0490 | 0.0309      | 0.0091           | 0.0090    |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.5.0+cu121
+- Datasets 3.1.0
+- Tokenizers 0.19.1

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