--- license: cc-by-nc-4.0 base_model: facebook/mms-1b-all tags: - generated_from_trainer metrics: - wer model-index: - name: All-mms1ball-Dec1 results: [] --- # All-mms1ball-Dec1 This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2261 - Wer: 0.5776 ## 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: 3 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 75.7763 | 0.0050 | 100 | 4.0652 | 1.0 | | 2.6846 | 0.0101 | 200 | 2.1046 | 0.8644 | | 2.0892 | 0.0151 | 300 | 2.0278 | 0.7559 | | 2.0608 | 0.0202 | 400 | 1.9999 | 0.7563 | | 2.2366 | 0.0252 | 500 | 1.8535 | 0.7402 | | 1.9112 | 0.0302 | 600 | 1.9858 | 0.7770 | | 1.9297 | 0.0353 | 700 | 1.8104 | 0.7605 | | 1.8322 | 0.0403 | 800 | 1.7293 | 0.7351 | | 1.9999 | 0.0454 | 900 | 1.8046 | 0.7471 | | 1.7927 | 0.0504 | 1000 | 1.6266 | 0.7112 | | 1.758 | 0.0554 | 1100 | 1.5815 | 0.7251 | | 1.802 | 0.0605 | 1200 | 1.7270 | 0.7197 | | 1.8543 | 0.0655 | 1300 | 1.6053 | 0.7029 | | 1.8065 | 0.0706 | 1400 | 1.5376 | 0.6837 | | 1.7766 | 0.0756 | 1500 | 1.5990 | 0.7104 | | 1.7397 | 0.0806 | 1600 | 1.5854 | 0.7021 | | 1.6967 | 0.0857 | 1700 | 1.5414 | 0.7214 | | 1.6538 | 0.0907 | 1800 | 1.6067 | 0.7237 | | 1.7187 | 0.0958 | 1900 | 1.5579 | 0.7087 | | 1.8067 | 0.1008 | 2000 | 1.7592 | 0.6927 | | 1.6497 | 0.1058 | 2100 | 1.6402 | 0.7268 | | 1.8631 | 0.1109 | 2200 | 1.4714 | 0.6692 | | 1.6328 | 0.1159 | 2300 | 1.5083 | 0.6820 | | 1.7545 | 0.1210 | 2400 | 1.5050 | 0.7249 | | 1.6864 | 0.1260 | 2500 | 1.4886 | 0.7038 | | 1.7195 | 0.1310 | 2600 | 1.5079 | 0.6585 | | 1.6702 | 0.1361 | 2700 | 1.4524 | 0.6896 | | 1.6112 | 0.1411 | 2800 | 1.5310 | 0.6637 | | 1.7391 | 0.1462 | 2900 | 1.7407 | 0.7562 | | 1.8044 | 0.1512 | 3000 | 1.5270 | 0.6762 | | 1.6354 | 0.1562 | 3100 | 1.4674 | 0.6870 | | 1.6693 | 0.1613 | 3200 | 1.4190 | 0.6663 | | 1.601 | 0.1663 | 3300 | 1.4157 | 0.6634 | | 1.4786 | 0.1714 | 3400 | 1.5873 | 0.6997 | | 1.6173 | 0.1764 | 3500 | 1.4899 | 0.6713 | | 1.6289 | 0.1815 | 3600 | 1.5515 | 0.6878 | | 1.5271 | 0.1865 | 3700 | 1.5557 | 0.6821 | | 1.5864 | 0.1915 | 3800 | 1.5196 | 0.6830 | | 1.6389 | 0.1966 | 3900 | 1.6335 | 0.6787 | | 1.737 | 0.2016 | 4000 | 1.4266 | 0.6589 | | 1.4226 | 0.2067 | 4100 | 1.5183 | 0.6574 | | 1.7838 | 0.2117 | 4200 | 1.5056 | 0.6927 | | 1.7542 | 0.2167 | 4300 | 1.5173 | 0.6726 | | 1.4174 | 0.2218 | 4400 | 1.5330 | 0.6743 | | 1.7392 | 0.2268 | 4500 | 1.4587 | 0.6589 | | 1.6215 | 0.2319 | 4600 | 1.4066 | 0.6607 | | 1.5882 | 0.2369 | 4700 | 1.4013 | 0.6430 | | 1.5614 | 0.2419 | 4800 | 1.4256 | 0.6366 | | 1.6021 | 0.2470 | 4900 | 1.5503 | 0.6450 | | 1.6551 | 0.2520 | 5000 | 1.5671 | 0.6685 | | 1.6113 | 0.2571 | 5100 | 1.4772 | 0.6682 | | 1.6276 | 0.2621 | 5200 | 1.4259 | 0.6838 | | 1.4248 | 0.2671 | 5300 | 1.4761 | 0.6097 | | 1.6195 | 0.2722 | 5400 | 1.3756 | 0.6340 | | 1.4586 | 0.2772 | 5500 | 1.4569 | 0.6381 | | 1.5018 | 0.2823 | 5600 | 1.3874 | 0.6060 | | 1.5791 | 0.2873 | 5700 | 1.3675 | 0.6143 | | 1.478 | 0.2923 | 5800 | 1.3716 | 0.6160 | | 1.3886 | 0.2974 | 5900 | 1.3523 | 0.6084 | | 1.4822 | 0.3024 | 6000 | 1.4189 | 0.6206 | | 1.3936 | 0.3075 | 6100 | 1.5999 | 0.6650 | | 1.7154 | 0.3125 | 6200 | 1.4897 | 0.6438 | | 1.4976 | 0.3175 | 6300 | 1.3664 | 0.5995 | | 1.5092 | 0.3226 | 6400 | 1.4601 | 0.6162 | | 1.4872 | 0.3276 | 6500 | 1.3488 | 0.6119 | | 1.6109 | 0.3327 | 6600 | 1.3319 | 0.6050 | | 1.529 | 0.3377 | 6700 | 1.3900 | 0.6207 | | 1.4621 | 0.3427 | 6800 | 1.5310 | 0.6547 | | 1.5824 | 0.3478 | 6900 | 1.5412 | 0.6198 | | 1.5586 | 0.3528 | 7000 | 1.3602 | 0.6082 | | 1.4643 | 0.3579 | 7100 | 1.2699 | 0.5903 | | 1.4514 | 0.3629 | 7200 | 1.3596 | 0.6034 | | 1.3783 | 0.3679 | 7300 | 1.5230 | 0.6452 | | 1.4566 | 0.3730 | 7400 | 1.3042 | 0.5975 | | 1.6092 | 0.3780 | 7500 | 1.4030 | 0.6238 | | 1.378 | 0.3831 | 7600 | 1.3766 | 0.5873 | | 1.3222 | 0.3881 | 7700 | 1.4285 | 0.6241 | | 1.6084 | 0.3931 | 7800 | 1.2604 | 0.6030 | | 1.3567 | 0.3982 | 7900 | 1.3863 | 0.5975 | | 1.3939 | 0.4032 | 8000 | 1.2574 | 0.5806 | | 1.4154 | 0.4083 | 8100 | 1.2763 | 0.5825 | | 1.5155 | 0.4133 | 8200 | 1.2933 | 0.6195 | | 1.4151 | 0.4183 | 8300 | 1.2543 | 0.5926 | | 1.4554 | 0.4234 | 8400 | 1.3933 | 0.6348 | | 1.4414 | 0.4284 | 8500 | 1.3746 | 0.6122 | | 1.6169 | 0.4335 | 8600 | 1.4666 | 0.5990 | | 1.4456 | 0.4385 | 8700 | 1.2792 | 0.6025 | | 1.5 | 0.4435 | 8800 | 1.2372 | 0.6242 | | 1.3626 | 0.4486 | 8900 | 1.2482 | 0.5949 | | 1.4589 | 0.4536 | 9000 | 1.3655 | 0.5937 | | 1.4927 | 0.4587 | 9100 | 1.2873 | 0.5987 | | 1.4479 | 0.4637 | 9200 | 1.3235 | 0.5998 | | 1.3844 | 0.4688 | 9300 | 1.2443 | 0.6015 | | 1.4684 | 0.4738 | 9400 | 1.2813 | 0.5946 | | 1.3035 | 0.4788 | 9500 | 1.4207 | 0.6015 | | 1.5696 | 0.4839 | 9600 | 1.2985 | 0.6050 | | 1.3457 | 0.4889 | 9700 | 1.2844 | 0.5656 | | 1.4152 | 0.4940 | 9800 | 1.2718 | 0.6010 | | 1.5372 | 0.4990 | 9900 | 1.4137 | 0.5873 | | 1.3527 | 0.5040 | 10000 | 1.3788 | 0.5740 | | 1.3174 | 0.5091 | 10100 | 1.3241 | 0.5668 | | 1.4634 | 0.5141 | 10200 | 1.3513 | 0.6071 | | 1.3983 | 0.5192 | 10300 | 1.2526 | 0.5718 | | 1.4222 | 0.5242 | 10400 | 1.2591 | 0.5957 | | 1.4156 | 0.5292 | 10500 | 1.2508 | 0.6071 | | 1.4235 | 0.5343 | 10600 | 1.2258 | 0.5842 | | 1.5522 | 0.5393 | 10700 | 1.2606 | 0.6380 | | 1.5539 | 0.5444 | 10800 | 1.3713 | 0.6309 | | 1.2924 | 0.5494 | 10900 | 1.2261 | 0.5776 | ### Framework versions - Transformers 4.43.4 - Pytorch 2.4.1 - Datasets 3.0.0 - Tokenizers 0.19.1