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chv-ru-marian-bt-3m-768d
alexantonov/chv-ru-marian-bt-3m-768d
chv-ru-marian-bt-3m-768d is a machine learning model from alexantonov. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- marian
- Access
- Public
- Created
- Jul 23, 2026
- Updated
- Jul 23, 2026
- SHA
- 336b675b
Model card
From the Hugging Face model README
<!-- 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. -->
model
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.6487
- Bleu: 30.433
- Chrf: 56.1326
- Gen Len: 38.7462
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: 0.0003
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: inverse_sqrt
- lr_scheduler_warmup_steps: 8000
- num_epochs: 10
- label_smoothing_factor: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf | Gen Len |
|---|---|---|---|---|---|---|
| 15.3114 | 0.1148 | 2000 | 7.5526 | 0.3798 | 10.781 | 127.0 |
| 11.6538 | 0.2296 | 4000 | 5.4532 | 4.4773 | 25.6875 | 58.9224 |
| 9.1924 | 0.3445 | 6000 | 4.3517 | 9.1151 | 36.3059 | 36.6315 |
| 8.3172 | 0.4593 | 8000 | 3.9632 | 12.0107 | 39.6421 | 40.9618 |
| 7.7347 | 0.5741 | 10000 | 3.6736 | 14.5443 | 42.359 | 37.2294 |
| 7.3219 | 0.6889 | 12000 | 3.5034 | 16.4317 | 44.0952 | 36.8227 |
| 7.0472 | 0.8037 | 14000 | 3.3783 | 17.9723 | 46.0555 | 39.5261 |
| 6.8362 | 0.9186 | 16000 | 3.2669 | 19.1662 | 47.165 | 36.9328 |
| 6.6218 | 1.0334 | 18000 | 3.1924 | 20.8058 | 48.1287 | 38.9710 |
| 6.4850 | 1.1482 | 20000 | 3.1457 | 21.0326 | 48.6305 | 37.5979 |
| 6.3948 | 1.2630 | 22000 | 3.0906 | 21.4722 | 48.9497 | 36.1912 |
| 6.3099 | 1.3778 | 24000 | 3.0665 | 22.9794 | 50.1652 | 37.3778 |
| 6.2460 | 1.4926 | 26000 | 3.0223 | 22.8858 | 49.9641 | 37.6744 |
| 6.1850 | 1.6075 | 28000 | 2.9911 | 23.1469 | 50.6156 | 36.7462 |
| 6.1272 | 1.7223 | 30000 | 2.9886 | 23.7444 | 51.0271 | 37.1958 |
| 6.0775 | 1.8371 | 32000 | 2.9556 | 23.8764 | 50.9297 | 36.5979 |
| 6.0334 | 1.9519 | 34000 | 2.9273 | 25.4098 | 51.9017 | 36.6744 |
| 5.8709 | 2.0667 | 36000 | 2.9126 | 25.2662 | 52.1812 | 37.9328 |
| 5.8711 | 2.1815 | 38000 | 2.8997 | 25.1416 | 51.9853 | 36.6744 |
| 5.8527 | 2.2964 | 40000 | 2.8828 | 25.6802 | 52.5512 | 37.0811 |
| 5.8321 | 2.4112 | 42000 | 2.8662 | 25.3716 | 52.2047 | 37.1912 |
| 5.8096 | 2.5260 | 44000 | 2.8545 | 25.6273 | 52.4418 | 37.8946 |
| 5.7882 | 2.6408 | 46000 | 2.8543 | 26.1349 | 52.9929 | 37.4114 |
| 5.7684 | 2.7556 | 48000 | 2.8205 | 26.4363 | 53.3924 | 36.9664 |
| 5.7501 | 2.8705 | 50000 | 2.8160 | 27.1292 | 53.4956 | 37.8227 |
| 5.7336 | 2.9853 | 52000 | 2.8162 | 26.544 | 53.2085 | 38.4542 |
| 5.6070 | 3.1001 | 54000 | 2.8178 | 26.8796 | 53.4375 | 39.0046 |
| 5.6057 | 3.2149 | 56000 | 2.8008 | 27.0065 | 53.5326 | 36.8563 |
| 5.6023 | 3.3297 | 58000 | 2.7851 | 27.1542 | 53.5006 | 38.3395 |
| 5.5990 | 3.4445 | 60000 | 2.7909 | 27.0665 | 53.5857 | 38.5597 |
| 5.5951 | 3.5593 | 62000 | 2.7780 | 27.0759 | 53.5168 | 38.3778 |
| 5.5826 | 3.6742 | 64000 | 2.7747 | 27.504 | 54.0484 | 37.8227 |
| 5.5766 | 3.7890 | 66000 | 2.7720 | 27.5317 | 54.1437 | 38.0093 |
| 5.5671 | 3.9038 | 68000 | 2.7590 | 27.7362 | 54.0257 | 38.3059 |
| 5.5157 | 4.0186 | 70000 | 2.7634 | 27.9822 | 54.4644 | 36.8227 |
| 5.4430 | 4.1334 | 72000 | 2.7551 | 27.7185 | 54.1158 | 38.4878 |
| 5.4518 | 4.2482 | 74000 | 2.7523 | 28.4761 | 54.5614 | 37.8563 |
| 5.4538 | 4.3631 | 76000 | 2.7450 | 28.2706 | 54.7321 | 37.5214 |
| 5.4535 | 4.4779 | 78000 | 2.7491 | 28.4611 | 54.4191 | 37.5597 |
| 5.4538 | 4.5927 | 80000 | 2.7365 | 27.9624 | 54.4126 | 37.4496 |
| 5.4548 | 4.7075 | 82000 | 2.7188 | 28.7067 | 54.5294 | 37.2294 |
| 5.4476 | 4.8223 | 84000 | 2.7257 | 28.6484 | 54.6074 | 36.3013 |
| 5.4376 | 4.9372 | 86000 | 2.7236 | 28.6869 | 54.7994 | 37.5261 |
| 5.3236 | 5.0520 | 88000 | 2.7222 | 28.6419 | 54.976 | 38.6744 |
| 5.3359 | 5.1668 | 90000 | 2.7177 | 28.5266 | 54.8334 | 36.9710 |
| 5.3430 | 5.2816 | 92000 | 2.7144 | 28.2914 | 54.8561 | 37.4878 |
| 5.3456 | 5.3964 | 94000 | 2.7165 | 28.7312 | 55.0545 | 36.8227 |
| 5.3448 | 5.5112 | 96000 | 2.7037 | 28.4145 | 54.7407 | 37.7845 |
| 5.3552 | 5.6261 | 98000 | 2.7103 | 29.4486 | 55.278 | 37.7462 |
| 5.3526 | 5.7409 | 100000 | 2.7127 | 28.801 | 54.9646 | 38.4878 |
| 5.3517 | 5.8557 | 102000 | 2.7003 | 29.0762 | 55.2533 | 37.7080 |
| 5.3507 | 5.9705 | 104000 | 2.6947 | 29.1968 | 55.2374 | 38.0093 |
| 5.2448 | 6.0853 | 106000 | 2.7004 | 29.2967 | 55.1747 | 36.7845 |
| 5.2526 | 6.2001 | 108000 | 2.6948 | 29.1562 | 55.2315 | 38.4878 |
| 5.2601 | 6.3150 | 110000 | 2.6999 | 29.4735 | 55.5081 | 37.7462 |
| 5.2720 | 6.4298 | 112000 | 2.6928 | 28.9911 | 55.0498 | 37.7080 |
| 5.2777 | 6.5446 | 114000 | 2.6920 | 29.6562 | 55.4142 | 38.0811 |
| 5.2761 | 6.6594 | 116000 | 2.6817 | 29.0561 | 55.2792 | 37.0429 |
| 5.2729 | 6.7742 | 118000 | 2.6852 | 29.1892 | 55.1699 | 37.0811 |
| 5.2832 | 6.8891 | 120000 | 2.6769 | 28.997 | 55.1977 | 38.0093 |
| 5.2701 | 7.0038 | 122000 | 2.6877 | 29.0789 | 55.1696 | 37.7126 |
| 5.1779 | 7.1187 | 124000 | 2.6873 | 29.2204 | 55.3161 | 37.6362 |
| 5.1866 | 7.2335 | 126000 | 2.6780 | 29.5141 | 55.2472 | 38.0429 |
| 5.2074 | 7.3483 | 128000 | 2.6748 | 29.6794 | 55.5231 | 38.2630 |
| 5.2044 | 7.4631 | 130000 | 2.6751 | 29.3888 | 55.4729 | 38.4160 |
| 5.2087 | 7.5779 | 132000 | 2.6770 | 29.3841 | 55.3863 | 36.6744 |
| 5.2190 | 7.6928 | 134000 | 2.6803 | 29.035 | 55.3936 | 38.5261 |
| 5.2207 | 7.8076 | 136000 | 2.6735 | 29.2547 | 55.2594 | 37.7126 |
| 5.2225 | 7.9224 | 138000 | 2.6665 | 29.324 | 55.6016 | 37.2677 |
| 5.1466 | 8.0372 | 140000 | 2.6641 | 29.5036 | 55.4835 | 38.2677 |
| 5.1225 | 8.1520 | 142000 | 2.6676 | 29.8431 | 55.7386 | 37.5643 |
| 5.1401 | 8.2668 | 144000 | 2.6719 | 29.7275 | 55.4219 | 39.0429 |
| 5.1472 | 8.3817 | 146000 | 2.6618 | 29.8157 | 55.7436 | 36.4496 |
| 5.1575 | 8.4965 | 148000 | 2.6755 | 30.0132 | 55.9124 | 37.9328 |
| 5.1576 | 8.6113 | 150000 | 2.6609 | 30.0136 | 55.8688 | 37.0811 |
| 5.1658 | 8.7261 | 152000 | 2.6572 | 30.0187 | 55.6733 | 37.3395 |
| 5.1640 | 8.8409 | 154000 | 2.6592 | 29.9868 | 55.8766 | 38.7080 |
| 5.1676 | 8.9558 | 156000 | 2.6601 | 29.4069 | 55.5662 | 37.6744 |
| 5.0678 | 9.0706 | 158000 | 2.6620 | 29.616 | 55.7595 | 37.7462 |
| 5.0780 | 9.1854 | 160000 | 2.6649 | 29.8854 | 56.0274 | 37.7126 |
| 5.0895 | 9.3002 | 162000 | 2.6657 | 29.8918 | 56.0764 | 37.2294 |
| 5.1025 | 9.4150 | 164000 | 2.6630 | 29.2364 | 55.6724 | 38.3778 |
| 5.1070 | 9.5298 | 166000 | 2.6585 | 30.0675 | 55.8914 | 37.5643 |
| 5.1173 | 9.6447 | 168000 | 2.6487 | 30.433 | 56.1326 | 38.7462 |
| 5.1246 | 9.7595 | 170000 | 2.6550 | 30.0248 | 55.958 | 37.8227 |
| 5.1281 | 9.8743 | 172000 | 2.6506 | 29.818 | 55.7533 | 37.7462 |
| 5.1283 | 9.9891 | 174000 | 2.6502 | 30.06 | 55.9464 | 37.5979 |
| 5.1283 | 10.0 | 174190 | 2.6380 | 30.189 | 56.0749 | 36.8563 |
Framework versions
- Transformers 5.6.2
- Pytorch 2.5.1+cu121
- Datasets 4.8.5
- Tokenizers 0.22.2
- Library
- transformers
- Type
- marian
- Created
- Jul 23, 2026
- Updated
- Jul 23, 2026