dts_ESLO.06.05.25_exp.ft.dia.1.A_mdl.no2

Rziane/dts_ESLO.06.05.25_exp.ft.dia.1.A_mdl.no2

dts_ESLO.06.05.25_exp.ft.dia.1.A_mdl.no2 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. --

Downloads · 30 days

14% of all-time downloads

All-time downloads

140

Public

Parameters

1.5M

125 MB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin5.9 MB · 50%

At a glance

Library: transformers
License: mit
Model type: pyannet
Access: Public
Created: May 18, 2025
Updated: May 18, 2025
SHA: ed82bca4

Base models

pyannote/segmentation-3.0

Datasets

CAENNAIS

Model card

From the Hugging Face model README

dts_ESLO.06.05.25_exp.ft.dia.1.A_mdl.no2

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: 0.7678
Model Preparation Time: 0.0043
Der: 0.4560
False Alarm: 0.1445
Missed Detection: 0.2165
Confusion: 0.0950

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
0.8687	1.0	270	0.8003	0.0043	0.5137	0.1447	0.2577	0.1113
0.8032	2.0	540	0.7798	0.0043	0.5002	0.1203	0.2856	0.0943
0.7846	3.0	810	0.7556	0.0043	0.4809	0.1545	0.2251	0.1013
0.7547	4.0	1080	0.7515	0.0043	0.4625	0.1559	0.2046	0.1021
0.7571	5.0	1350	0.7644	0.0043	0.4757	0.1400	0.2389	0.0968
0.7751	6.0	1620	0.7725	0.0043	0.4773	0.1317	0.2519	0.0938
0.7187	7.0	1890	0.7781	0.0043	0.4726	0.1338	0.2455	0.0933
0.7351	8.0	2160	0.7491	0.0043	0.4640	0.1362	0.2408	0.0870
0.7112	9.0	2430	0.7530	0.0043	0.4610	0.1535	0.2129	0.0946
0.7275	10.0	2700	0.7666	0.0043	0.4597	0.1377	0.2329	0.0891
0.7159	11.0	2970	0.7624	0.0043	0.4567	0.1375	0.2314	0.0878
0.7201	12.0	3240	0.7474	0.0043	0.4598	0.1410	0.2271	0.0917
0.6565	13.0	3510	0.7511	0.0043	0.4579	0.1475	0.2137	0.0966
0.6864	14.0	3780	0.7658	0.0043	0.4636	0.1429	0.2278	0.0930
0.695	15.0	4050	0.7739	0.0043	0.4557	0.1459	0.2143	0.0955
0.6775	16.0	4320	0.7636	0.0043	0.4541	0.1467	0.2139	0.0935
0.6821	17.0	4590	0.7694	0.0043	0.4565	0.1444	0.2166	0.0955
0.6742	18.0	4860	0.7674	0.0043	0.4575	0.1441	0.2181	0.0952
0.693	19.0	5130	0.7672	0.0043	0.4557	0.1441	0.2168	0.0948
0.7177	20.0	5400	0.7678	0.0043	0.4560	0.1445	0.2165	0.0950

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 18, 2025
Updated: May 18, 2025