Instructions to use alexantonov/ru-chv-marian-bt-3m-8lrs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alexantonov/ru-chv-marian-bt-3m-8lrs with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("alexantonov/ru-chv-marian-bt-3m-8lrs") model = AutoModelForSeq2SeqLM.from_pretrained("alexantonov/ru-chv-marian-bt-3m-8lrs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
model
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 3.0417
- eval_bleu: 28.8383
- eval_chrf: 55.0894
- eval_gen_len: 54.0711
- eval_runtime: 44.1674
- eval_samples_per_second: 45.214
- eval_steps_per_second: 0.725
- epoch: 7.7271
- step: 132010
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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- 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
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
Framework versions
- Transformers 5.6.2
- Pytorch 2.5.1+cu121
- Datasets 4.8.5
- Tokenizers 0.22.2
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