End of training

e4ae988 verified 8 days ago

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	---
	library_name: transformers
	base_model: sulaimank/w2vbert-lingala-waxal-punct-v2
	tags:
	- generated_from_trainer
	model-index:
	- name: w2vbert-lingala-sd2
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# w2vbert-lingala-sd2

	This model is a fine-tuned version of [sulaimank/w2vbert-lingala-waxal-punct-v2](https://huggingface.co/sulaimank/w2vbert-lingala-waxal-punct-v2) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0545
	- Wer Keep: 0.0915
	- Cer Keep: 0.0214
	- Zindi Keep: 0.9436
	- Wer Strip: 0.0403
	- Zindi Strip: 0.9746
	- Zindi Lower: 0.9829

	## 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: 3e-05
	- train_batch_size: 16
	- eval_batch_size: 16
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 32
	- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.98) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 8.0

	### Training results

	\| Training Loss \| Epoch \| Step \| Cer Keep \| Validation Loss \| Wer Keep \| Wer Strip \| Zindi Keep \| Zindi Lower \| Zindi Strip \|
	\|:-------------:\|:------:\|:----:\|:--------:\|:---------------:\|:--------:\|:---------:\|:----------:\|:-----------:\|:-----------:\|
	\| 3.6428 \| 0.1758 \| 200 \| 0.9997 \| 3.2376 \| 1.0 \| 1.0 \| 0.0001 \| 0.0001 \| 0.0001 \|
	\| 1.6433 \| 0.3516 \| 400 \| 0.0408 \| 0.1868 \| 0.1654 \| 0.1120 \| 0.8969 \| 0.9459 \| 0.9292 \|
	\| 0.5927 \| 0.5275 \| 600 \| 0.0336 \| 0.1295 \| 0.1356 \| 0.0799 \| 0.9154 \| 0.9661 \| 0.9494 \|
	\| 0.5845 \| 0.7033 \| 800 \| 0.0314 \| 0.1090 \| 0.1237 \| 0.0728 \| 0.9224 \| 0.9662 \| 0.9533 \|
	\| 0.9751 \| 0.8791 \| 1000 \| 0.0296 \| 0.1028 \| 0.1163 \| 0.0651 \| 0.9270 \| 0.9686 \| 0.9583 \|
	\| 0.3809 \| 1.0545 \| 1200 \| 0.0282 \| 0.0974 \| 0.1109 \| 0.0594 \| 0.9304 \| 0.9718 \| 0.9620 \|
	\| 0.5085 \| 1.2303 \| 1400 \| 0.0301 \| 0.0975 \| 0.1158 \| 0.0632 \| 0.9271 \| 0.9684 \| 0.9591 \|
	\| 0.2797 \| 1.4062 \| 1600 \| 0.0286 \| 0.0970 \| 0.1128 \| 0.0606 \| 0.9293 \| 0.9714 \| 0.9611 \|
	\| 0.442 \| 1.5820 \| 1800 \| 0.0274 \| 0.0912 \| 0.1092 \| 0.0588 \| 0.9317 \| 0.9727 \| 0.9624 \|
	\| 0.6423 \| 1.7578 \| 2000 \| 0.0272 \| 0.0906 \| 0.1108 \| 0.0607 \| 0.9310 \| 0.9710 \| 0.9613 \|
	\| 0.2759 \| 1.9336 \| 2200 \| 0.0274 \| 0.0824 \| 0.1080 \| 0.0556 \| 0.9323 \| 0.9734 \| 0.9643 \|
	\| 0.3426 \| 2.1090 \| 2400 \| 0.0262 \| 0.0833 \| 0.1049 \| 0.0541 \| 0.9344 \| 0.9745 \| 0.9653 \|
	\| 0.1779 \| 2.2848 \| 2600 \| 0.0269 \| 0.0845 \| 0.1073 \| 0.0557 \| 0.9329 \| 0.9734 \| 0.9642 \|
	\| 0.3073 \| 2.4607 \| 2800 \| 0.0265 \| 0.0838 \| 0.1065 \| 0.0552 \| 0.9335 \| 0.9740 \| 0.9646 \|
	\| 0.3184 \| 2.6365 \| 3000 \| 0.0267 \| 0.0742 \| 0.1074 \| 0.0553 \| 0.9330 \| 0.9740 \| 0.9645 \|
	\| 0.3589 \| 2.8123 \| 3200 \| 0.0262 \| 0.0726 \| 0.1045 \| 0.0535 \| 0.9346 \| 0.9755 \| 0.9656 \|
	\| 0.3602 \| 2.9881 \| 3400 \| 0.0254 \| 0.0720 \| 0.1028 \| 0.0517 \| 0.9359 \| 0.9761 \| 0.9669 \|
	\| 0.259 \| 3.1635 \| 3600 \| 0.0259 \| 0.0754 \| 0.1029 \| 0.0516 \| 0.9356 \| 0.9764 \| 0.9669 \|
	\| 0.1736 \| 3.3393 \| 3800 \| 0.0251 \| 0.0722 \| 0.1012 \| 0.0492 \| 0.9368 \| 0.9783 \| 0.9686 \|
	\| 0.1477 \| 3.5152 \| 4000 \| 0.0699 \| 0.0992 \| 0.0242 \| 0.9383 \| 0.0486 \| 0.9690 \| 0.9780 \|
	\| 0.1615 \| 3.6910 \| 4200 \| 0.0676 \| 0.0994 \| 0.0240 \| 0.9383 \| 0.0485 \| 0.9692 \| 0.9786 \|
	\| 0.2897 \| 3.8668 \| 4400 \| 0.0686 \| 0.0984 \| 0.0241 \| 0.9388 \| 0.0479 \| 0.9695 \| 0.9784 \|
	\| 0.3471 \| 4.0422 \| 4600 \| 0.0647 \| 0.0993 \| 0.0241 \| 0.9383 \| 0.0480 \| 0.9695 \| 0.9786 \|
	\| 0.5524 \| 4.2180 \| 4800 \| 0.0659 \| 0.0994 \| 0.0236 \| 0.9385 \| 0.0468 \| 0.9703 \| 0.9789 \|
	\| 0.3344 \| 4.3938 \| 5000 \| 0.0647 \| 0.0984 \| 0.0235 \| 0.9390 \| 0.0471 \| 0.9702 \| 0.9792 \|
	\| 0.3536 \| 4.5697 \| 5200 \| 0.0641 \| 0.1005 \| 0.0237 \| 0.9379 \| 0.0470 \| 0.9702 \| 0.9796 \|
	\| 0.1587 \| 4.7455 \| 5400 \| 0.0638 \| 0.0961 \| 0.0230 \| 0.9405 \| 0.0451 \| 0.9713 \| 0.9801 \|
	\| 0.3888 \| 4.9213 \| 5600 \| 0.0615 \| 0.0958 \| 0.0235 \| 0.9404 \| 0.0447 \| 0.9715 \| 0.9801 \|
	\| 0.2487 \| 5.0967 \| 5800 \| 0.0614 \| 0.0975 \| 0.0232 \| 0.9397 \| 0.0459 \| 0.9709 \| 0.9799 \|
	\| 0.0854 \| 5.2725 \| 6000 \| 0.0604 \| 0.0950 \| 0.0226 \| 0.9412 \| 0.0442 \| 0.9720 \| 0.9808 \|
	\| 0.2548 \| 5.4484 \| 6200 \| 0.0618 \| 0.0948 \| 0.0226 \| 0.9413 \| 0.0439 \| 0.9722 \| 0.9809 \|
	\| 0.1518 \| 5.6242 \| 6400 \| 0.0599 \| 0.0951 \| 0.0225 \| 0.9412 \| 0.0443 \| 0.9719 \| 0.9807 \|
	\| 0.4898 \| 5.8 \| 6600 \| 0.0586 \| 0.0946 \| 0.0223 \| 0.9416 \| 0.0431 \| 0.9727 \| 0.9812 \|
	\| 0.2699 \| 5.9758 \| 6800 \| 0.0572 \| 0.0936 \| 0.0222 \| 0.9421 \| 0.0420 \| 0.9734 \| 0.9820 \|
	\| 0.316 \| 6.1512 \| 7000 \| 0.0576 \| 0.0942 \| 0.0219 \| 0.9420 \| 0.0432 \| 0.9727 \| 0.9815 \|
	\| 0.3514 \| 6.3270 \| 7200 \| 0.0562 \| 0.0921 \| 0.0218 \| 0.9431 \| 0.0413 \| 0.9738 \| 0.9823 \|
	\| 0.1803 \| 6.5029 \| 7400 \| 0.0560 \| 0.0922 \| 0.0219 \| 0.9429 \| 0.0413 \| 0.9739 \| 0.9822 \|
	\| 0.1222 \| 6.6787 \| 7600 \| 0.0564 \| 0.0931 \| 0.0219 \| 0.9425 \| 0.0414 \| 0.9738 \| 0.9823 \|
	\| 0.1351 \| 6.8545 \| 7800 \| 0.0552 \| 0.0917 \| 0.0216 \| 0.9433 \| 0.0403 \| 0.9744 \| 0.9830 \|
	\| 0.2344 \| 7.0299 \| 8000 \| 0.0548 \| 0.0917 \| 0.0214 \| 0.9434 \| 0.0405 \| 0.9744 \| 0.9828 \|
	\| 0.4722 \| 7.2057 \| 8200 \| 0.0547 \| 0.0911 \| 0.0213 \| 0.9438 \| 0.0403 \| 0.9745 \| 0.9829 \|
	\| 0.1888 \| 7.3815 \| 8400 \| 0.0545 \| 0.0912 \| 0.0214 \| 0.9437 \| 0.0405 \| 0.9744 \| 0.9828 \|
	\| 0.2479 \| 7.5574 \| 8600 \| 0.0548 \| 0.0920 \| 0.0216 \| 0.9432 \| 0.0407 \| 0.9743 \| 0.9828 \|
	\| 0.1695 \| 7.7332 \| 8800 \| 0.0544 \| 0.0919 \| 0.0215 \| 0.9433 \| 0.0408 \| 0.9742 \| 0.9826 \|
	\| 0.3146 \| 7.9090 \| 9000 \| 0.0545 \| 0.0915 \| 0.0214 \| 0.9436 \| 0.0403 \| 0.9746 \| 0.9829 \|


	### Framework versions

	- Transformers 4.57.6
	- Pytorch 2.13.0+cu130
	- Datasets 3.6.0
	- Tokenizers 0.22.2

	---
	library_name: transformers
	base_model: sulaimank/w2vbert-lingala-waxal-punct-v2
	tags:
	- generated_from_trainer
	model-index:
	- name: w2vbert-lingala-sd2
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# w2vbert-lingala-sd2

	This model is a fine-tuned version of [sulaimank/w2vbert-lingala-waxal-punct-v2](https://huggingface.co/sulaimank/w2vbert-lingala-waxal-punct-v2) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0545
	- Wer Keep: 0.0915
	- Cer Keep: 0.0214
	- Zindi Keep: 0.9436
	- Wer Strip: 0.0403
	- Zindi Strip: 0.9746
	- Zindi Lower: 0.9829

	## 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: 3e-05
	- train_batch_size: 16
	- eval_batch_size: 16
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 32
	- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.98) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 8.0

	### Training results

	\| Training Loss \| Epoch \| Step \| Cer Keep \| Validation Loss \| Wer Keep \| Wer Strip \| Zindi Keep \| Zindi Lower \| Zindi Strip \|
	\|:-------------:\|:------:\|:----:\|:--------:\|:---------------:\|:--------:\|:---------:\|:----------:\|:-----------:\|:-----------:\|
	\| 3.6428 \| 0.1758 \| 200 \| 0.9997 \| 3.2376 \| 1.0 \| 1.0 \| 0.0001 \| 0.0001 \| 0.0001 \|
	\| 1.6433 \| 0.3516 \| 400 \| 0.0408 \| 0.1868 \| 0.1654 \| 0.1120 \| 0.8969 \| 0.9459 \| 0.9292 \|
	\| 0.5927 \| 0.5275 \| 600 \| 0.0336 \| 0.1295 \| 0.1356 \| 0.0799 \| 0.9154 \| 0.9661 \| 0.9494 \|
	\| 0.5845 \| 0.7033 \| 800 \| 0.0314 \| 0.1090 \| 0.1237 \| 0.0728 \| 0.9224 \| 0.9662 \| 0.9533 \|
	\| 0.9751 \| 0.8791 \| 1000 \| 0.0296 \| 0.1028 \| 0.1163 \| 0.0651 \| 0.9270 \| 0.9686 \| 0.9583 \|
	\| 0.3809 \| 1.0545 \| 1200 \| 0.0282 \| 0.0974 \| 0.1109 \| 0.0594 \| 0.9304 \| 0.9718 \| 0.9620 \|
	\| 0.5085 \| 1.2303 \| 1400 \| 0.0301 \| 0.0975 \| 0.1158 \| 0.0632 \| 0.9271 \| 0.9684 \| 0.9591 \|
	\| 0.2797 \| 1.4062 \| 1600 \| 0.0286 \| 0.0970 \| 0.1128 \| 0.0606 \| 0.9293 \| 0.9714 \| 0.9611 \|
	\| 0.442 \| 1.5820 \| 1800 \| 0.0274 \| 0.0912 \| 0.1092 \| 0.0588 \| 0.9317 \| 0.9727 \| 0.9624 \|
	\| 0.6423 \| 1.7578 \| 2000 \| 0.0272 \| 0.0906 \| 0.1108 \| 0.0607 \| 0.9310 \| 0.9710 \| 0.9613 \|
	\| 0.2759 \| 1.9336 \| 2200 \| 0.0274 \| 0.0824 \| 0.1080 \| 0.0556 \| 0.9323 \| 0.9734 \| 0.9643 \|
	\| 0.3426 \| 2.1090 \| 2400 \| 0.0262 \| 0.0833 \| 0.1049 \| 0.0541 \| 0.9344 \| 0.9745 \| 0.9653 \|
	\| 0.1779 \| 2.2848 \| 2600 \| 0.0269 \| 0.0845 \| 0.1073 \| 0.0557 \| 0.9329 \| 0.9734 \| 0.9642 \|
	\| 0.3073 \| 2.4607 \| 2800 \| 0.0265 \| 0.0838 \| 0.1065 \| 0.0552 \| 0.9335 \| 0.9740 \| 0.9646 \|
	\| 0.3184 \| 2.6365 \| 3000 \| 0.0267 \| 0.0742 \| 0.1074 \| 0.0553 \| 0.9330 \| 0.9740 \| 0.9645 \|
	\| 0.3589 \| 2.8123 \| 3200 \| 0.0262 \| 0.0726 \| 0.1045 \| 0.0535 \| 0.9346 \| 0.9755 \| 0.9656 \|
	\| 0.3602 \| 2.9881 \| 3400 \| 0.0254 \| 0.0720 \| 0.1028 \| 0.0517 \| 0.9359 \| 0.9761 \| 0.9669 \|
	\| 0.259 \| 3.1635 \| 3600 \| 0.0259 \| 0.0754 \| 0.1029 \| 0.0516 \| 0.9356 \| 0.9764 \| 0.9669 \|
	\| 0.1736 \| 3.3393 \| 3800 \| 0.0251 \| 0.0722 \| 0.1012 \| 0.0492 \| 0.9368 \| 0.9783 \| 0.9686 \|
	\| 0.1477 \| 3.5152 \| 4000 \| 0.0699 \| 0.0992 \| 0.0242 \| 0.9383 \| 0.0486 \| 0.9690 \| 0.9780 \|
	\| 0.1615 \| 3.6910 \| 4200 \| 0.0676 \| 0.0994 \| 0.0240 \| 0.9383 \| 0.0485 \| 0.9692 \| 0.9786 \|
	\| 0.2897 \| 3.8668 \| 4400 \| 0.0686 \| 0.0984 \| 0.0241 \| 0.9388 \| 0.0479 \| 0.9695 \| 0.9784 \|
	\| 0.3471 \| 4.0422 \| 4600 \| 0.0647 \| 0.0993 \| 0.0241 \| 0.9383 \| 0.0480 \| 0.9695 \| 0.9786 \|
	\| 0.5524 \| 4.2180 \| 4800 \| 0.0659 \| 0.0994 \| 0.0236 \| 0.9385 \| 0.0468 \| 0.9703 \| 0.9789 \|
	\| 0.3344 \| 4.3938 \| 5000 \| 0.0647 \| 0.0984 \| 0.0235 \| 0.9390 \| 0.0471 \| 0.9702 \| 0.9792 \|
	\| 0.3536 \| 4.5697 \| 5200 \| 0.0641 \| 0.1005 \| 0.0237 \| 0.9379 \| 0.0470 \| 0.9702 \| 0.9796 \|
	\| 0.1587 \| 4.7455 \| 5400 \| 0.0638 \| 0.0961 \| 0.0230 \| 0.9405 \| 0.0451 \| 0.9713 \| 0.9801 \|
	\| 0.3888 \| 4.9213 \| 5600 \| 0.0615 \| 0.0958 \| 0.0235 \| 0.9404 \| 0.0447 \| 0.9715 \| 0.9801 \|
	\| 0.2487 \| 5.0967 \| 5800 \| 0.0614 \| 0.0975 \| 0.0232 \| 0.9397 \| 0.0459 \| 0.9709 \| 0.9799 \|
	\| 0.0854 \| 5.2725 \| 6000 \| 0.0604 \| 0.0950 \| 0.0226 \| 0.9412 \| 0.0442 \| 0.9720 \| 0.9808 \|
	\| 0.2548 \| 5.4484 \| 6200 \| 0.0618 \| 0.0948 \| 0.0226 \| 0.9413 \| 0.0439 \| 0.9722 \| 0.9809 \|
	\| 0.1518 \| 5.6242 \| 6400 \| 0.0599 \| 0.0951 \| 0.0225 \| 0.9412 \| 0.0443 \| 0.9719 \| 0.9807 \|
	\| 0.4898 \| 5.8 \| 6600 \| 0.0586 \| 0.0946 \| 0.0223 \| 0.9416 \| 0.0431 \| 0.9727 \| 0.9812 \|
	\| 0.2699 \| 5.9758 \| 6800 \| 0.0572 \| 0.0936 \| 0.0222 \| 0.9421 \| 0.0420 \| 0.9734 \| 0.9820 \|
	\| 0.316 \| 6.1512 \| 7000 \| 0.0576 \| 0.0942 \| 0.0219 \| 0.9420 \| 0.0432 \| 0.9727 \| 0.9815 \|
	\| 0.3514 \| 6.3270 \| 7200 \| 0.0562 \| 0.0921 \| 0.0218 \| 0.9431 \| 0.0413 \| 0.9738 \| 0.9823 \|
	\| 0.1803 \| 6.5029 \| 7400 \| 0.0560 \| 0.0922 \| 0.0219 \| 0.9429 \| 0.0413 \| 0.9739 \| 0.9822 \|
	\| 0.1222 \| 6.6787 \| 7600 \| 0.0564 \| 0.0931 \| 0.0219 \| 0.9425 \| 0.0414 \| 0.9738 \| 0.9823 \|
	\| 0.1351 \| 6.8545 \| 7800 \| 0.0552 \| 0.0917 \| 0.0216 \| 0.9433 \| 0.0403 \| 0.9744 \| 0.9830 \|
	\| 0.2344 \| 7.0299 \| 8000 \| 0.0548 \| 0.0917 \| 0.0214 \| 0.9434 \| 0.0405 \| 0.9744 \| 0.9828 \|
	\| 0.4722 \| 7.2057 \| 8200 \| 0.0547 \| 0.0911 \| 0.0213 \| 0.9438 \| 0.0403 \| 0.9745 \| 0.9829 \|
	\| 0.1888 \| 7.3815 \| 8400 \| 0.0545 \| 0.0912 \| 0.0214 \| 0.9437 \| 0.0405 \| 0.9744 \| 0.9828 \|
	\| 0.2479 \| 7.5574 \| 8600 \| 0.0548 \| 0.0920 \| 0.0216 \| 0.9432 \| 0.0407 \| 0.9743 \| 0.9828 \|
	\| 0.1695 \| 7.7332 \| 8800 \| 0.0544 \| 0.0919 \| 0.0215 \| 0.9433 \| 0.0408 \| 0.9742 \| 0.9826 \|
	\| 0.3146 \| 7.9090 \| 9000 \| 0.0545 \| 0.0915 \| 0.0214 \| 0.9436 \| 0.0403 \| 0.9746 \| 0.9829 \|


	### Framework versions

	- Transformers 4.57.6
	- Pytorch 2.13.0+cu130
	- Datasets 3.6.0
	- Tokenizers 0.22.2