--- license: mit language: - ru - kbd widget: - text: "Я иду домой." example_title: "Я иду домой." - text: "Дети играют во дворе." example_title: "Дети играют во дворе." - text: "Сколько тебе лет?" example_title: "Сколько тебе лет?" - text: "На следующий день мы отправились в путь." example_title: "На следующий день мы отправились в путь." --- # m2m100_ru_kbd_44K This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on a ru-kbd dataset, containing 45K sentences from books, textbooks, dictionaries etc.. It achieves the following results on the evaluation set: - Loss: 0.9399 - Bleu: 22.389 - Gen Len: 16.562 ## 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 2.2391 | 0.18 | 1000 | 1.9921 | 7.4066 | 16.377 | | 1.8436 | 0.36 | 2000 | 1.6756 | 9.3443 | 18.428 | | 1.63 | 0.53 | 3000 | 1.5361 | 10.9057 | 17.134 | | 1.5205 | 0.71 | 4000 | 1.3994 | 12.6061 | 17.471 | | 1.4471 | 0.89 | 5000 | 1.3107 | 14.4452 | 16.985 | | 1.1915 | 1.07 | 6000 | 1.2462 | 15.1903 | 16.544 | | 1.1165 | 1.25 | 7000 | 1.1917 | 16.3859 | 17.044 | | 1.0654 | 1.43 | 8000 | 1.1351 | 17.617 | 16.481 | | 1.0464 | 1.6 | 9000 | 1.0939 | 18.649 | 16.517 | | 1.0376 | 1.78 | 10000 | 1.0603 | 18.2567 | 17.152 | | 1.0027 | 1.96 | 11000 | 1.0184 | 20.6011 | 16.875 | | 0.7741 | 2.14 | 12000 | 1.0159 | 20.4801 | 16.488 | | 0.7566 | 2.32 | 13000 | 0.9899 | 21.6967 | 16.681 | | 0.7346 | 2.49 | 14000 | 0.9738 | 21.8249 | 16.679 | | 0.7397 | 2.67 | 15000 | 0.9555 | 21.569 | 16.608 | | 0.6919 | 2.85 | 16000 | 0.9441 | 22.4658 | 16.493 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.10.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1