wav2vec2-large-mms-1b-DZ-kabyle-testt

This model is a fine-tuned version of facebook/mms-1b-all on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: nan
• Wer: 1.0
• Bleu: 0.0
• Rouge: 0.0

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: 0.0001
• train_batch_size: 8
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Bleu	Rouge
15.4732	1.0	146	inf	0.9998	{'bleu': 0.0, 'precisions': [0.0024630541871921183, 0.0, 0.0, 0.0], 'brevity_penalty': 0.0060533535353636545, 'length_ratio': 0.16374269005847952, 'translation_length': 812, 'reference_length': 4959}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
3.5259	2.0	292	inf	1.0	0.0	0.0
2.8886	3.0	438	inf	0.9256	{'bleu': 0.012706304633238194, 'precisions': [0.15207808564231737, 0.04889779559118237, 0.016554578375581996, 0.0049157303370786515], 'brevity_penalty': 0.4555685552993787, 'length_ratio': 0.5598448792526001, 'translation_length': 3176, 'reference_length': 5673}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.0962	4.0	584	inf	0.9552	{'bleu': 0.0051587494823155545, 'precisions': [0.13643331630045988, 0.0589159465828751, 0.022809123649459785, 0.007889546351084813], 'brevity_penalty': 0.1487530765061357, 'length_ratio': 0.34417868448821665, 'translation_length': 1957, 'reference_length': 5686}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.2088	5.0	730	inf	0.9165	{'bleu': 0.016805294416329334, 'precisions': [0.18280321565886054, 0.07291185971389016, 0.03056768558951965, 0.01073345259391771], 'brevity_penalty': 0.3674938907007195, 'length_ratio': 0.4997379912663755, 'translation_length': 2861, 'reference_length': 5725}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.3138	6.0	876	inf	0.9986	{'bleu': 0.0, 'precisions': [0.011157601115760111, 0.021739130434782608, 0.0, 0.0], 'brevity_penalty': 0.001070521373788099, 'length_ratio': 0.1275573741327166, 'translation_length': 717, 'reference_length': 5621}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.3795	7.0	1022	inf	0.9986	{'bleu': 0.0, 'precisions': [0.011188811188811189, 0.022222222222222223, 0.0, 0.0], 'brevity_penalty': 0.0010531769082091937, 'length_ratio': 0.1272921488338971, 'translation_length': 715, 'reference_length': 5617}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.3643	8.0	1168	inf	0.9986	{'bleu': 0.0, 'precisions': [0.0111731843575419, 0.022222222222222223, 0.0, 0.0], 'brevity_penalty': 0.0010588639835161769, 'length_ratio': 0.12737946984522328, 'translation_length': 716, 'reference_length': 5621}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.4279	9.0	1314	inf	0.9986	{'bleu': 0.0, 'precisions': [0.011157601115760111, 0.021739130434782608, 0.0, 0.0], 'brevity_penalty': 0.001070521373788099, 'length_ratio': 0.1275573741327166, 'translation_length': 717, 'reference_length': 5621}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.4179	10.0	1460	inf	0.9986	{'bleu': 0.0, 'precisions': [0.011157601115760111, 0.021739130434782608, 0.0, 0.0], 'brevity_penalty': 0.001070521373788099, 'length_ratio': 0.1275573741327166, 'translation_length': 717, 'reference_length': 5621}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.4451	11.0	1606	inf	0.9986	{'bleu': 0.0, 'precisions': [0.011188811188811189, 0.022222222222222223, 0.0, 0.0], 'brevity_penalty': 0.0010531769082091937, 'length_ratio': 0.1272921488338971, 'translation_length': 715, 'reference_length': 5617}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.4373	12.0	1752	inf	0.9986	{'bleu': 0.0, 'precisions': [0.0111731843575419, 0.021739130434782608, 0.0, 0.0], 'brevity_penalty': 0.0010647959791569663, 'length_ratio': 0.12747017981128717, 'translation_length': 716, 'reference_length': 5617}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.3163	13.0	1898	inf	0.9986	{'bleu': 0.0, 'precisions': [0.011142061281337047, 0.02127659574468085, 0.0, 0.0], 'brevity_penalty': 0.0010822740953120335, 'length_ratio': 0.12773527842020993, 'translation_length': 718, 'reference_length': 5621}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.4059	14.0	2044	inf	0.9986	{'bleu': 0.0, 'precisions': [0.0111731843575419, 0.021739130434782608, 0.0, 0.0], 'brevity_penalty': 0.0010647959791569663, 'length_ratio': 0.12747017981128717, 'translation_length': 716, 'reference_length': 5617}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.44	15.0	2190	inf	0.9986	{'bleu': 0.0, 'precisions': [0.0111731843575419, 0.022222222222222223, 0.0, 0.0], 'brevity_penalty': 0.0010588639835161769, 'length_ratio': 0.12737946984522328, 'translation_length': 716, 'reference_length': 5621}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.3549	16.0	2336	inf	0.9986	{'bleu': 0.0, 'precisions': [0.0111731843575419, 0.022222222222222223, 0.0, 0.0], 'brevity_penalty': 0.0010588639835161769, 'length_ratio': 0.12737946984522328, 'translation_length': 716, 'reference_length': 5621}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.4238	17.0	2482	inf	0.9986	{'bleu': 0.0, 'precisions': [0.0111731843575419, 0.022222222222222223, 0.0, 0.0], 'brevity_penalty': 0.0010588639835161769, 'length_ratio': 0.12737946984522328, 'translation_length': 716, 'reference_length': 5621}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.3961	18.0	2628	inf	0.9986	{'bleu': 0.0, 'precisions': [0.011188811188811189, 0.022222222222222223, 0.0, 0.0], 'brevity_penalty': 0.0010531769082091937, 'length_ratio': 0.1272921488338971, 'translation_length': 715, 'reference_length': 5617}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.4546	19.0	2774	inf	0.9986	{'bleu': 0.0, 'precisions': [0.011204481792717087, 0.022727272727272728, 0.0, 0.0], 'brevity_penalty': 0.001041652610251504, 'length_ratio': 0.12711411785650703, 'translation_length': 714, 'reference_length': 5617}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.4024	20.0	2920	inf	0.9986	{'bleu': 0.0, 'precisions': [0.011188811188811189, 0.022222222222222223, 0.0, 0.0], 'brevity_penalty': 0.0010531769082091937, 'length_ratio': 0.1272921488338971, 'translation_length': 715, 'reference_length': 5617}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.4229	21.0	3066	inf	0.9986	{'bleu': 0.0, 'precisions': [0.011188811188811189, 0.022222222222222223, 0.0, 0.0], 'brevity_penalty': 0.0010531769082091937, 'length_ratio': 0.1272921488338971, 'translation_length': 715, 'reference_length': 5617}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.3279	22.0	3212	inf	0.9986	{'bleu': 0.0, 'precisions': [0.011157601115760111, 0.021739130434782608, 0.0, 0.0], 'brevity_penalty': 0.001070521373788099, 'length_ratio': 0.1275573741327166, 'translation_length': 717, 'reference_length': 5621}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.3955	23.0	3358	inf	0.9986	{'bleu': 0.0, 'precisions': [0.011188811188811189, 0.022222222222222223, 0.0, 0.0], 'brevity_penalty': 0.0010531769082091937, 'length_ratio': 0.1272921488338971, 'translation_length': 715, 'reference_length': 5617}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.3205	24.0	3504	inf	0.9986	{'bleu': 0.0, 'precisions': [0.0111731843575419, 0.021739130434782608, 0.0, 0.0], 'brevity_penalty': 0.0010647959791569663, 'length_ratio': 0.12747017981128717, 'translation_length': 716, 'reference_length': 5617}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.3394	25.0	3650	inf	0.9986	{'bleu': 0.0, 'precisions': [0.0111731843575419, 0.021739130434782608, 0.0, 0.0], 'brevity_penalty': 0.0010647959791569663, 'length_ratio': 0.12747017981128717, 'translation_length': 716, 'reference_length': 5617}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.3832	26.0	3796	inf	0.9986	{'bleu': 0.0, 'precisions': [0.0111731843575419, 0.021739130434782608, 0.0, 0.0], 'brevity_penalty': 0.0010647959791569663, 'length_ratio': 0.12747017981128717, 'translation_length': 716, 'reference_length': 5617}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.3691	27.0	3942	inf	0.9986	{'bleu': 0.0, 'precisions': [0.011157601115760111, 0.02127659574468085, 0.0, 0.0], 'brevity_penalty': 0.0010765102889743517, 'length_ratio': 0.12764821078867722, 'translation_length': 717, 'reference_length': 5617}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
2.3989	28.0	4088	inf	0.9986	{'bleu': 0.0, 'precisions': [0.011157601115760111, 0.021739130434782608, 0.0, 0.0], 'brevity_penalty': 0.001070521373788099, 'length_ratio': 0.1275573741327166, 'translation_length': 717, 'reference_length': 5621}	{'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
0.0	29.0	4234	nan	1.0	0.0	0.0
0.0	30.0	4380	nan	1.0	0.0	0.0

Framework versions

• Transformers 4.49.0
• Pytorch 2.6.0+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0