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full-ami-ihm-bigrf-250516_0918
objects76/full-ami-ihm-bigrf-250516_0918
full-ami-ihm-bigrf-250516_0918 is a machine learning model from objects76. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- mit
- Model type
- pyannet
- Access
- Public
- Created
- May 16, 2025
- Updated
- May 16, 2025
- SHA
- 752438a5
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
full-ja4-2.25sec-big-rf
This model is a fine-tuned version of pyannote/segmentation-3.0 on the objects76/synthetic-ja4-speaker-overlap-6400 dataset. It achieves the following results on the evaluation set:
- eval_loss: 1.3497
- eval_der: 0.3641
- eval_false_alarm: 0.0785
- eval_missed_detection: 0.2430
- eval_confusion: 0.0426
- eval_runtime: 2.1638
- eval_samples_per_second: 295.775
- eval_steps_per_second: 0.462
- step: 0
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: 2048
- eval_batch_size: 2048
- seed: 42
- 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: 200
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
- Library
- transformers
- Type
- pyannet
- License
- mit
- Created
- May 16, 2025
- Updated
- May 16, 2025