You can deploy this model and start earning money today!

hsethu/
fine_tuned_segmentation-3.0_1e-5_32

Name: fine_tuned_segmentation-3.0_1e-5_32
Availability: InStock

generated_from_trainer

ArtFair/diarizers_dataset_70-15-15

pyannote/segmentation-3.0

finetune

mit

Views

No views yet

fine_tuned_segmentation-3.0_1e-5_32

This model is a fine-tuned version of pyannote/segmentation-3.0 on the ArtFair/diarizers_dataset_70-15-15 default dataset. It achieves the following results on the evaluation set:

Loss: 0.4908
Der: 0.3585
False Alarm: 0.2041
Missed Detection: 0.1214
Confusion: 0.0330

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: 1e-05
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: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss	Der	False Alarm	Missed Detection	Confusion
0.621	1.0	929	0.5601	0.3774	0.2133	0.1279	0.0361
0.5472	2.0	1858	0.5149	0.3680	0.2084	0.1254	0.0342
0.5353	3.0	2787	0.4969	0.3615	0.2049	0.1234	0.0333
0.517	4.0	3716	0.4919	0.3590	0.2042	0.1216	0.0331
0.5217	5.0	4645	0.4908	0.3585	0.2041	0.1214	0.0330

Framework versions

Transformers 4.37.2
Pytorch 2.4.1+cu121
Datasets 2.17.0
Tokenizers 0.15.2

hsethu/fine_tuned_segmentation-3.0_1e-5_32

fine_tuned_segmentation-3.0_1e-5_32

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hsethu/
fine_tuned_segmentation-3.0_1e-5_32