es_ar

Hellina/es_ar

es_ar is a machine learning model from Hellina. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

Downloads · 30 days

14% of all-time downloads

All-time downloads

Public

Parameters

316M

2.5 GB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors1.3 GB · 100%

At a glance

License: apache-2.0
Model type: wav2vec2
Access: Public
Created: Sep 10, 2024
Updated: Sep 12, 2024
SHA: 36c2bf1f

Base models

facebook/wav2vec2-xls-r-300m

Model card

From the Hugging Face model README

es_ar

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4940
Wer: 0.3263
Cer: 0.2414

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: 0.0003
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 15.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
3.6158	0.4	500	3.7004	0.9886	0.9625
1.0731	0.8	1000	1.0090	0.7501	0.4066
0.7529	1.2	1500	0.7609	0.6195	0.3460
0.6441	1.6	2000	0.6322	0.5524	0.3171
0.607	2.0	2500	0.5795	0.5202	0.3065
0.4744	2.4	3000	0.5848	0.5096	0.3056
0.4604	2.8	3500	0.5341	0.4666	0.2907
0.3763	3.2	4000	0.5060	0.4416	0.2812
0.3952	3.6	4500	0.5214	0.4566	0.2850
0.3962	4.0	5000	0.4890	0.4324	0.2784
0.3137	4.4	5500	0.4833	0.4165	0.2713
0.316	4.8	6000	0.5005	0.4182	0.2738
0.2721	5.2	6500	0.4961	0.4171	0.2732
0.2561	5.6	7000	0.4742	0.3997	0.2645
0.2854	6.0	7500	0.4600	0.3991	0.2662
0.2599	6.4	8000	0.4541	0.4022	0.2659
0.2249	6.8	8500	0.4586	0.3911	0.2615
0.1931	7.2	9000	0.4721	0.3871	0.2614
0.195	7.6	9500	0.4636	0.3898	0.2608
0.1991	8.0	10000	0.4259	0.3716	0.2555
0.1657	8.4	10500	0.4548	0.3714	0.2573
0.1802	8.8	11000	0.4540	0.3582	0.2526
0.1359	9.2	11500	0.4685	0.3652	0.2552
0.1419	9.6	12000	0.4524	0.3561	0.2512
0.1531	10.0	12500	0.4443	0.3578	0.2514
0.1313	10.4	13000	0.4536	0.3536	0.2495
0.1269	10.8	13500	0.4563	0.3517	0.2480
0.102	11.2	14000	0.4606	0.3424	0.2476
0.103	11.6	14500	0.4611	0.3489	0.2477
0.1088	12.0	15000	0.4505	0.3362	0.2447
0.0917	12.4	15500	0.4741	0.3404	0.2458
0.0847	12.8	16000	0.4714	0.3340	0.2440
0.0768	13.2	16500	0.4943	0.3286	0.2427
0.0789	13.6	17000	0.4813	0.3308	0.2429
0.0797	14.0	17500	0.4861	0.3288	0.2423
0.0698	14.4	18000	0.5003	0.3271	0.2416
0.0686	14.8	18500	0.4940	0.3263	0.2414

Framework versions

Transformers 4.36.2
Pytorch 1.13.0+cu116
Datasets 2.15.0
Tokenizers 0.15.2

Type: wav2vec2
License: apache-2.0
Created: Sep 10, 2024
Updated: Sep 12, 2024