README.md

metadata

license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2-large-mms-1b-kazakh-speech2ner-ksc_t-16b-8ep
    results: []

wav2vec2-large-mms-1b-kazakh-speech2ner-ksc_t-16b-8ep

This model is a fine-tuned version of facebook/mms-1b-all on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2356
• Wer: 0.3093

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: 16
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
5.1	0.22	2000	5.0320	1.0014
3.8579	0.43	4000	3.8757	1.0001
3.617	0.65	6000	3.6434	1.0001
3.56	0.87	8000	3.5268	0.9999
3.4363	1.09	10000	3.4524	0.9999
3.3759	1.3	12000	3.4010	0.9999
3.3024	1.52	14000	3.3323	1.0000
2.721	1.74	16000	2.4051	1.0048
0.3937	1.96	18000	0.3398	0.3681
0.3456	2.17	20000	0.3013	0.3523
0.3269	2.39	22000	0.2836	0.3410
0.3275	2.61	24000	0.2738	0.3346
0.3053	2.83	26000	0.2667	0.3294
0.3041	3.04	28000	0.2619	0.3263
0.3084	3.26	30000	0.2574	0.3230
0.2915	3.48	32000	0.2547	0.3207
0.2865	3.69	34000	0.2521	0.3187
0.2814	3.91	36000	0.2500	0.3172
0.2961	4.13	38000	0.2479	0.3159
0.3037	4.35	40000	0.2464	0.3147
0.3023	4.56	42000	0.2448	0.3148
0.2977	4.78	44000	0.2433	0.3139
0.2933	5.0	46000	0.2422	0.3133
0.2838	5.22	48000	0.2413	0.3120
0.2833	5.43	50000	0.2401	0.3123
0.2774	5.65	52000	0.2398	0.3112
0.2813	5.87	54000	0.2389	0.3111
0.2779	6.08	56000	0.2380	0.3108
0.2872	6.3	58000	0.2376	0.3106
0.2758	6.52	60000	0.2372	0.3106
0.275	6.74	62000	0.2369	0.3095
0.2749	6.95	64000	0.2364	0.3100
0.2828	7.17	66000	0.2362	0.3098
0.2749	7.39	68000	0.2359	0.3093
0.2775	7.61	70000	0.2358	0.3093
0.2744	7.82	72000	0.2356	0.3093

Framework versions

• Transformers 4.33.0.dev0
• Pytorch 2.0.1+cu117
• Datasets 2.13.1
• Tokenizers 0.13.3