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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-xls-r-300m |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: ft_0131_kor_eng |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ft_0131_kor_eng |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5441 |
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- Cer: 0.0980 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Cer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 5.6525 | 0.24 | 500 | 4.2248 | 1.0 | |
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| 2.7344 | 0.48 | 1000 | 1.6790 | 0.5047 | |
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| 1.2753 | 0.72 | 1500 | 0.7988 | 0.1950 | |
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| 0.8951 | 0.96 | 2000 | 0.6650 | 0.1611 | |
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| 0.7622 | 1.2 | 2500 | 0.6036 | 0.1453 | |
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| 0.6851 | 1.44 | 3000 | 0.5689 | 0.1384 | |
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| 0.6464 | 1.68 | 3500 | 0.5471 | 0.1315 | |
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| 0.6219 | 1.92 | 4000 | 0.5334 | 0.1261 | |
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| 0.5558 | 2.16 | 4500 | 0.5340 | 0.1255 | |
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| 0.5121 | 2.41 | 5000 | 0.5098 | 0.1243 | |
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| 0.509 | 2.65 | 5500 | 0.5241 | 0.1217 | |
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| 0.51 | 2.89 | 6000 | 0.4947 | 0.1181 | |
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| 0.4499 | 3.13 | 6500 | 0.5327 | 0.1168 | |
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| 0.4484 | 3.37 | 7000 | 0.4865 | 0.1158 | |
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| 0.434 | 3.61 | 7500 | 0.4708 | 0.1169 | |
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| 0.4316 | 3.85 | 8000 | 0.4781 | 0.1127 | |
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| 0.399 | 4.09 | 8500 | 0.5105 | 0.1137 | |
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| 0.3889 | 4.33 | 9000 | 0.4750 | 0.1099 | |
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| 0.3741 | 4.57 | 9500 | 0.4841 | 0.1124 | |
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| 0.3837 | 4.81 | 10000 | 0.4879 | 0.1112 | |
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| 0.3708 | 5.05 | 10500 | 0.4899 | 0.1124 | |
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| 0.3307 | 5.29 | 11000 | 0.5045 | 0.1090 | |
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| 0.3431 | 5.53 | 11500 | 0.4755 | 0.1102 | |
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| 0.333 | 5.77 | 12000 | 0.4579 | 0.1103 | |
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| 0.3415 | 6.01 | 12500 | 0.5191 | 0.1080 | |
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| 0.2997 | 6.25 | 13000 | 0.4742 | 0.1085 | |
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| 0.3087 | 6.49 | 13500 | 0.5069 | 0.1092 | |
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| 0.303 | 6.73 | 14000 | 0.4754 | 0.1092 | |
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| 0.3077 | 6.97 | 14500 | 0.4999 | 0.1095 | |
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| 0.2758 | 7.22 | 15000 | 0.4894 | 0.1073 | |
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| 0.2743 | 7.46 | 15500 | 0.4973 | 0.1045 | |
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| 0.2818 | 7.7 | 16000 | 0.4817 | 0.1078 | |
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| 0.2746 | 7.94 | 16500 | 0.4788 | 0.1058 | |
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| 0.2508 | 8.18 | 17000 | 0.4743 | 0.1070 | |
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| 0.2526 | 8.42 | 17500 | 0.5032 | 0.1036 | |
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| 0.2586 | 8.66 | 18000 | 0.4616 | 0.1049 | |
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| 0.2534 | 8.9 | 18500 | 0.4569 | 0.1078 | |
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| 0.2331 | 9.14 | 19000 | 0.4889 | 0.1044 | |
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| 0.2324 | 9.38 | 19500 | 0.4783 | 0.1040 | |
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| 0.2444 | 9.62 | 20000 | 0.4836 | 0.1057 | |
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| 0.2334 | 9.86 | 20500 | 0.4749 | 0.1060 | |
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| 0.2278 | 10.1 | 21000 | 0.4877 | 0.1041 | |
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| 0.2048 | 10.34 | 21500 | 0.4940 | 0.1036 | |
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| 0.2149 | 10.58 | 22000 | 0.4963 | 0.1043 | |
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| 0.2069 | 10.82 | 22500 | 0.5082 | 0.1028 | |
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| 0.2049 | 11.06 | 23000 | 0.5214 | 0.1042 | |
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| 0.1906 | 11.3 | 23500 | 0.5023 | 0.1045 | |
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| 0.1997 | 11.54 | 24000 | 0.5035 | 0.1013 | |
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| 0.1933 | 11.78 | 24500 | 0.5121 | 0.1029 | |
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| 0.1965 | 12.03 | 25000 | 0.5307 | 0.1046 | |
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| 0.1814 | 12.27 | 25500 | 0.5169 | 0.1039 | |
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| 0.1774 | 12.51 | 26000 | 0.5022 | 0.1019 | |
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| 0.1784 | 12.75 | 26500 | 0.5129 | 0.1032 | |
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| 0.1807 | 12.99 | 27000 | 0.4875 | 0.1019 | |
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| 0.1704 | 13.23 | 27500 | 0.4937 | 0.1029 | |
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| 0.1628 | 13.47 | 28000 | 0.5232 | 0.1032 | |
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| 0.1578 | 13.71 | 28500 | 0.5100 | 0.1019 | |
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| 0.1594 | 13.95 | 29000 | 0.5183 | 0.0993 | |
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| 0.162 | 14.19 | 29500 | 0.5220 | 0.1030 | |
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| 0.1564 | 14.43 | 30000 | 0.5304 | 0.1009 | |
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| 0.1514 | 14.67 | 30500 | 0.5265 | 0.1009 | |
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| 0.157 | 14.91 | 31000 | 0.5193 | 0.1011 | |
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| 0.1401 | 15.15 | 31500 | 0.5608 | 0.1022 | |
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| 0.1416 | 15.39 | 32000 | 0.5208 | 0.1016 | |
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| 0.1472 | 15.63 | 32500 | 0.5275 | 0.1011 | |
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| 0.1415 | 15.87 | 33000 | 0.5314 | 0.1005 | |
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| 0.1438 | 16.11 | 33500 | 0.5321 | 0.1016 | |
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| 0.1304 | 16.35 | 34000 | 0.5244 | 0.1020 | |
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| 0.135 | 16.59 | 34500 | 0.5379 | 0.1005 | |
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| 0.1352 | 16.84 | 35000 | 0.5279 | 0.0998 | |
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| 0.1305 | 17.08 | 35500 | 0.5386 | 0.0985 | |
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| 0.1273 | 17.32 | 36000 | 0.5349 | 0.0992 | |
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| 0.1252 | 17.56 | 36500 | 0.5348 | 0.0990 | |
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| 0.1232 | 17.8 | 37000 | 0.5322 | 0.0982 | |
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| 0.1284 | 18.04 | 37500 | 0.5403 | 0.0985 | |
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| 0.1181 | 18.28 | 38000 | 0.5344 | 0.0987 | |
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| 0.123 | 18.52 | 38500 | 0.5333 | 0.0983 | |
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| 0.112 | 18.76 | 39000 | 0.5424 | 0.0978 | |
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| 0.1172 | 19.0 | 39500 | 0.5356 | 0.0990 | |
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| 0.1169 | 19.24 | 40000 | 0.5410 | 0.0984 | |
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| 0.1125 | 19.48 | 40500 | 0.5416 | 0.0980 | |
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| 0.1081 | 19.72 | 41000 | 0.5448 | 0.0981 | |
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| 0.12 | 19.96 | 41500 | 0.5441 | 0.0980 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu118 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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