# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM
tokenizer = AutoTokenizer.from_pretrained("rinkorn/marian-finetuned-kde4-en-to-ru")
model = AutoModelForMultimodalLM.from_pretrained("rinkorn/marian-finetuned-kde4-en-to-ru")Quick Links
marian-finetuned-kde4-en-to-ru
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ru on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3947
- Bleu: 34.8576
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: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
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Model tree for rinkorn/marian-finetuned-kde4-en-to-ru
Base model
Helsinki-NLP/opus-mt-en-ru
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="rinkorn/marian-finetuned-kde4-en-to-ru")