End of training

README.md

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+---
+library_name: transformers
+language:
+- spa
+license: mit
+base_model: pyannote/speaker-diarization-3.1
+tags:
+- speaker-diarization
+- speaker-segmentation
+- generated_from_trainer
+datasets:
+- diarizers-community/callhome
+model-index:
+- name: speaker-segmentation-fine-tuned-callhome-spa
+  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-callhome-spa
+
+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/callhome dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5595
+- Der: 0.2894
+- False Alarm: 0.2353
+- Missed Detection: 0.0536
+- Confusion: 0.0005
+
+## 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: 64
+- eval_batch_size: 64
+- 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 | Der    | False Alarm | Missed Detection | Confusion |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.3614        | 1.0   | 226  | 0.4962          | 0.2910 | 0.2389      | 0.0520           | 0.0001    |
+| 0.3465        | 2.0   | 452  | 0.5067          | 0.2860 | 0.2179      | 0.0679           | 0.0002    |
+| 0.3325        | 3.0   | 678  | 0.5343          | 0.2941 | 0.2300      | 0.0636           | 0.0005    |
+| 0.3189        | 4.0   | 904  | 0.5613          | 0.2906 | 0.2380      | 0.0522           | 0.0004    |
+| 0.3238        | 5.0   | 1130 | 0.5595          | 0.2894 | 0.2353      | 0.0536           | 0.0005    |
+
+
+### Framework versions
+
+- Transformers 4.45.1
+- Pytorch 2.4.1
+- Datasets 3.0.1
+- Tokenizers 0.20.0

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