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End of training

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  *.zip filter=lfs diff=lfs merge=lfs -text
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+ predictions_common_voice_13_en_common_voice_13_en_test_wer33.93_hyp.trn.snt.utt filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,120 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: Lakoc/DeCRED_small_cv
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - common_voice_13_0
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+ metrics:
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+ - wer
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+ model-index:
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+ - name: DeCRED_small_cv_linear_mixing
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+ results: []
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+ ---
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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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+
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+ # DeCRED_small_cv_linear_mixing
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+
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+ This model is a fine-tuned version of [Lakoc/DeCRED_small_cv](https://huggingface.co/Lakoc/DeCRED_small_cv) on the common_voice_13_0 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.5358
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+ - Cer: 0.1334
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+ - Wer: 0.2616
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+ - Mer: 0.2546
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+ - Wil: 0.4037
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+ - Wip: 0.5963
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+ - Hits: 34174
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+ - Substitutions: 8492
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+ - Deletions: 1958
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+ - Insertions: 1223
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0005
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+ - train_batch_size: 128
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+ - eval_batch_size: 128
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+ - seed: 42
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+ - distributed_type: multi-GPU
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 512
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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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+ - num_epochs: 50.0
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Cer | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|
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+ | 3.0556 | 0.98 | 22 | 3.0178 | 1.2906 | 1.4035 | 0.9218 | 0.9902 | 0.0098 | 5310 | 35886 | 3428 | 23314 |
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+ | 2.9712 | 2.0 | 45 | 2.9272 | 1.0482 | 1.2086 | 0.8876 | 0.9816 | 0.0184 | 6832 | 33809 | 3983 | 16140 |
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+ | 2.8778 | 2.98 | 67 | 2.8449 | 0.8931 | 1.0776 | 0.8519 | 0.9699 | 0.0301 | 8362 | 31916 | 4346 | 11823 |
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+ | 2.8329 | 4.0 | 90 | 2.7632 | 0.7772 | 0.9734 | 0.8097 | 0.9523 | 0.0477 | 10207 | 29712 | 4705 | 9021 |
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+ | 2.7126 | 4.98 | 112 | 2.6890 | 0.6915 | 0.9029 | 0.7722 | 0.9331 | 0.0669 | 11886 | 27857 | 4881 | 7553 |
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+ | 2.7523 | 6.0 | 135 | 2.6153 | 0.5987 | 0.8231 | 0.7289 | 0.9078 | 0.0922 | 13658 | 25936 | 5030 | 5764 |
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+ | 2.5892 | 6.98 | 157 | 2.5484 | 0.5370 | 0.7637 | 0.6887 | 0.8801 | 0.1199 | 15403 | 24075 | 5146 | 4857 |
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+ | 2.5555 | 8.0 | 180 | 2.4819 | 0.4797 | 0.7039 | 0.6449 | 0.8463 | 0.1537 | 17294 | 22237 | 5093 | 4083 |
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+ | 2.4556 | 8.98 | 202 | 2.4215 | 0.4348 | 0.6531 | 0.6033 | 0.8103 | 0.1897 | 19160 | 20536 | 4928 | 3678 |
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+ | 2.4722 | 10.0 | 225 | 2.3615 | 0.3908 | 0.5995 | 0.5587 | 0.7687 | 0.2313 | 21129 | 18865 | 4630 | 3255 |
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+ | 2.3578 | 10.98 | 247 | 2.3072 | 0.3534 | 0.5561 | 0.5215 | 0.7310 | 0.2690 | 22766 | 17449 | 4409 | 2958 |
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+ | 2.317 | 12.0 | 270 | 2.2533 | 0.3181 | 0.5166 | 0.4877 | 0.6952 | 0.3048 | 24219 | 16266 | 4139 | 2647 |
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+ | 2.2757 | 12.98 | 292 | 2.2044 | 0.2935 | 0.4833 | 0.4587 | 0.6633 | 0.3367 | 25452 | 15275 | 3897 | 2393 |
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+ | 2.2332 | 14.0 | 315 | 2.1560 | 0.2705 | 0.4522 | 0.4312 | 0.6319 | 0.3681 | 26614 | 14339 | 3671 | 2169 |
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+ | 2.1887 | 14.98 | 337 | 2.1122 | 0.2526 | 0.4289 | 0.4106 | 0.6072 | 0.3928 | 27479 | 13605 | 3540 | 1996 |
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+ | 2.1393 | 16.0 | 360 | 2.0689 | 0.2352 | 0.4049 | 0.3888 | 0.5811 | 0.4189 | 28408 | 12909 | 3307 | 1854 |
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+ | 2.0646 | 16.98 | 382 | 2.0297 | 0.2213 | 0.3855 | 0.3712 | 0.5598 | 0.4402 | 29141 | 12367 | 3116 | 1719 |
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+ | 2.0398 | 18.0 | 405 | 1.9911 | 0.2090 | 0.3687 | 0.3557 | 0.5407 | 0.4593 | 29799 | 11895 | 2930 | 1629 |
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+ | 2.0011 | 18.98 | 427 | 1.9562 | 0.1994 | 0.3552 | 0.3432 | 0.5243 | 0.4757 | 30335 | 11455 | 2834 | 1561 |
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+ | 2.0078 | 20.0 | 450 | 1.9219 | 0.1911 | 0.3435 | 0.3325 | 0.5108 | 0.4892 | 30776 | 11131 | 2717 | 1482 |
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+ | 1.9608 | 20.98 | 472 | 1.8910 | 0.1843 | 0.3340 | 0.3236 | 0.4992 | 0.5008 | 31153 | 10840 | 2631 | 1435 |
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+ | 1.9634 | 22.0 | 495 | 1.8605 | 0.1782 | 0.3258 | 0.3158 | 0.4888 | 0.5112 | 31498 | 10582 | 2544 | 1413 |
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+ | 1.8888 | 22.98 | 517 | 1.8332 | 0.1730 | 0.3181 | 0.3086 | 0.4790 | 0.5210 | 31806 | 10335 | 2483 | 1375 |
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+ | 1.9019 | 24.0 | 540 | 1.8064 | 0.1688 | 0.3118 | 0.3026 | 0.4711 | 0.5289 | 32063 | 10141 | 2420 | 1353 |
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+ | 1.8107 | 24.98 | 562 | 1.7823 | 0.1660 | 0.3077 | 0.2987 | 0.4658 | 0.5342 | 32237 | 10016 | 2371 | 1342 |
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+ | 1.8168 | 26.0 | 585 | 1.7588 | 0.1639 | 0.3029 | 0.2942 | 0.4598 | 0.5402 | 32427 | 9875 | 2322 | 1318 |
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+ | 1.7804 | 26.98 | 607 | 1.7377 | 0.1612 | 0.2988 | 0.2903 | 0.4543 | 0.5457 | 32593 | 9734 | 2297 | 1301 |
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+ | 1.8311 | 28.0 | 630 | 1.7171 | 0.1585 | 0.2954 | 0.2871 | 0.4499 | 0.5501 | 32726 | 9624 | 2274 | 1283 |
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+ | 1.7236 | 28.98 | 652 | 1.6988 | 0.1564 | 0.2928 | 0.2846 | 0.4466 | 0.5534 | 32837 | 9549 | 2238 | 1278 |
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+ | 1.741 | 30.0 | 675 | 1.6809 | 0.1548 | 0.2902 | 0.2822 | 0.4433 | 0.5567 | 32947 | 9474 | 2203 | 1274 |
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+ | 1.7035 | 30.98 | 697 | 1.6650 | 0.1523 | 0.2880 | 0.2801 | 0.4403 | 0.5597 | 33036 | 9398 | 2190 | 1263 |
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+ | 1.7518 | 32.0 | 720 | 1.6496 | 0.1504 | 0.2856 | 0.2778 | 0.4371 | 0.5629 | 33135 | 9320 | 2169 | 1255 |
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+ | 1.6684 | 32.98 | 742 | 1.6360 | 0.1491 | 0.2837 | 0.2760 | 0.4345 | 0.5655 | 33214 | 9255 | 2155 | 1250 |
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+ | 1.7251 | 34.0 | 765 | 1.6228 | 0.1474 | 0.2810 | 0.2734 | 0.4311 | 0.5689 | 33320 | 9175 | 2129 | 1236 |
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+ | 1.6414 | 34.98 | 787 | 1.6112 | 0.1457 | 0.2788 | 0.2713 | 0.4280 | 0.5720 | 33416 | 9099 | 2109 | 1232 |
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+ | 1.6707 | 36.0 | 810 | 1.6001 | 0.1444 | 0.2770 | 0.2696 | 0.4255 | 0.5745 | 33490 | 9034 | 2100 | 1229 |
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+ | 1.6509 | 36.98 | 832 | 1.5904 | 0.1428 | 0.2749 | 0.2675 | 0.4225 | 0.5775 | 33588 | 8959 | 2077 | 1233 |
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+ | 1.654 | 38.0 | 855 | 1.5812 | 0.1416 | 0.2732 | 0.2658 | 0.4199 | 0.5801 | 33670 | 8889 | 2065 | 1237 |
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+ | 1.6305 | 38.98 | 877 | 1.5732 | 0.1403 | 0.2718 | 0.2645 | 0.4180 | 0.5820 | 33730 | 8841 | 2053 | 1235 |
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+ | 1.6381 | 40.0 | 900 | 1.5658 | 0.1391 | 0.2703 | 0.2630 | 0.4158 | 0.5842 | 33811 | 8790 | 2023 | 1250 |
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+ | 1.6393 | 40.98 | 922 | 1.5595 | 0.1382 | 0.2689 | 0.2616 | 0.4138 | 0.5862 | 33870 | 8741 | 2013 | 1246 |
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+ | 1.6111 | 42.0 | 945 | 1.5537 | 0.1372 | 0.2675 | 0.2602 | 0.4117 | 0.5883 | 33937 | 8685 | 2002 | 1250 |
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+ | 1.6041 | 42.98 | 967 | 1.5490 | 0.1362 | 0.2659 | 0.2587 | 0.4096 | 0.5904 | 34000 | 8636 | 1988 | 1240 |
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+ | 1.6301 | 44.0 | 990 | 1.5448 | 0.1353 | 0.2647 | 0.2575 | 0.4079 | 0.5921 | 34052 | 8593 | 1979 | 1239 |
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+ | 1.5736 | 44.98 | 1012 | 1.5416 | 0.1347 | 0.2638 | 0.2567 | 0.4066 | 0.5934 | 34087 | 8561 | 1976 | 1235 |
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+ | 1.6209 | 46.0 | 1035 | 1.5389 | 0.1341 | 0.2626 | 0.2556 | 0.4050 | 0.5950 | 34135 | 8522 | 1967 | 1230 |
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+ | 1.5892 | 46.98 | 1057 | 1.5372 | 0.1337 | 0.2620 | 0.2550 | 0.4042 | 0.5958 | 34156 | 8502 | 1966 | 1225 |
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+ | 1.6066 | 48.0 | 1080 | 1.5361 | 0.1335 | 0.2617 | 0.2548 | 0.4039 | 0.5961 | 34167 | 8496 | 1961 | 1223 |
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+ | 1.6445 | 48.89 | 1100 | 1.5358 | 0.1334 | 0.2616 | 0.2546 | 0.4037 | 0.5963 | 34174 | 8492 | 1958 | 1223 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.40.0.dev0
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+ - Pytorch 2.2.0+rocm5.6
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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