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--- |
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library_name: transformers |
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language: |
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- spa |
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license: mit |
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base_model: diarizers-community/speaker-segmentation-fine-tuned-callhome-spa |
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tags: |
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- speaker-diarization |
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- speaker-segmentation |
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- generated_from_trainer |
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datasets: |
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- diarizers-community/callhome |
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model-index: |
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- name: speaker-segmentation-fine-tuned-callhome-spa |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# speaker-segmentation-fine-tuned-callhome-spa |
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This model is a fine-tuned version of [diarizers-community/speaker-segmentation-fine-tuned-callhome-spa](https://huggingface.co/diarizers-community/speaker-segmentation-fine-tuned-callhome-spa) on the diarizers-community/callhome dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3513 |
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- Der: 0.2029 |
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- False Alarm: 0.1480 |
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- Missed Detection: 0.0549 |
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- Confusion: 0.0000 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:| |
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| 0.3117 | 1.0 | 281 | 0.3448 | 0.2096 | 0.1526 | 0.0545 | 0.0024 | |
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| 0.2973 | 2.0 | 562 | 0.3260 | 0.1961 | 0.1359 | 0.0601 | 0.0001 | |
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| 0.2937 | 3.0 | 843 | 0.3413 | 0.2027 | 0.1468 | 0.0555 | 0.0004 | |
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| 0.2953 | 4.0 | 1124 | 0.3466 | 0.2023 | 0.1467 | 0.0555 | 0.0000 | |
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| 0.2725 | 5.0 | 1405 | 0.3513 | 0.2029 | 0.1480 | 0.0549 | 0.0000 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.1 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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