model.no2_expe.dia.1.A_data_ESLO_06.05.25

Rziane/model.no2_expe.dia.1.A_data_ESLO_06.05.25

model.no2_expe.dia.1.A_data_ESLO_06.05.25 is a machine learning model from Rziane. 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 12, 2025
Updated: May 12, 2025
SHA: 4179cd29

Base models

pyannote/segmentation-3.0

Datasets

CAENNAIS

Model card

From the Hugging Face model README

model.no2_expe.dia.1.A_data_ESLO_06.05.25

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

Loss: 0.7552
Model Preparation Time: 0.0039
Der: 0.4612
False Alarm: 0.1526
Missed Detection: 0.2132
Confusion: 0.0953

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Der	False Alarm	Missed Detection	Confusion
0.8598	1.0	270	0.8043	0.0039	0.5158	0.1643	0.2233	0.1282
0.8108	2.0	540	0.7766	0.0039	0.4948	0.1529	0.2280	0.1139
0.795	3.0	810	0.7713	0.0039	0.4886	0.1697	0.1979	0.1210
0.7601	4.0	1080	0.7588	0.0039	0.4746	0.1590	0.2119	0.1037
0.7705	5.0	1350	0.7490	0.0039	0.4675	0.1374	0.2429	0.0871
0.7667	6.0	1620	0.7848	0.0039	0.4784	0.1537	0.2247	0.1000
0.7198	7.0	1890	0.7692	0.0039	0.4855	0.1353	0.2505	0.0996
0.7266	8.0	2160	0.7474	0.0039	0.4671	0.1448	0.2267	0.0955
0.7011	9.0	2430	0.7509	0.0039	0.4622	0.1675	0.1915	0.1032
0.717	10.0	2700	0.7523	0.0039	0.4656	0.1578	0.2123	0.0955
0.7083	11.0	2970	0.7439	0.0039	0.4624	0.1443	0.2320	0.0861
0.7153	12.0	3240	0.7462	0.0039	0.4614	0.1548	0.2091	0.0975
0.6498	13.0	3510	0.7512	0.0039	0.4663	0.1564	0.2122	0.0978
0.6935	14.0	3780	0.7501	0.0039	0.4621	0.1533	0.2139	0.0950
0.6746	15.0	4050	0.7534	0.0039	0.4632	0.1544	0.2139	0.0950
0.6751	16.0	4320	0.7515	0.0039	0.4627	0.1552	0.2122	0.0952
0.6848	17.0	4590	0.7542	0.0039	0.4632	0.1516	0.2187	0.0928
0.6759	18.0	4860	0.7577	0.0039	0.4605	0.1534	0.2121	0.0951
0.6856	19.0	5130	0.7540	0.0039	0.4609	0.1525	0.2135	0.0949
0.7036	20.0	5400	0.7552	0.0039	0.4612	0.1526	0.2132	0.0953

Framework versions

Transformers 4.45.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.20.0

Library: transformers
Type: pyannet
License: mit
Languages: fr
Created: May 12, 2025
Updated: May 12, 2025