Translation
Transformers
PyTorch
Croatian
Swedish
marian
text2text-generation
Generated from Trainer
Instructions to use oskarandrsson/mt-hr-sv-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oskarandrsson/mt-hr-sv-finetuned with Transformers:
# 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="oskarandrsson/mt-hr-sv-finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("oskarandrsson/mt-hr-sv-finetuned") model = AutoModelForSeq2SeqLM.from_pretrained("oskarandrsson/mt-hr-sv-finetuned") - Notebooks
- Google Colab
- Kaggle
mt-hr-sv-finetuned
This model is a fine-tuned version of Helsinki-NLP/opus-mt-hr-sv on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.9565
- eval_bleu: 49.8248
- eval_runtime: 873.8605
- eval_samples_per_second: 16.982
- eval_steps_per_second: 4.246
- epoch: 5.0
- step: 27825
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-06
- train_batch_size: 24
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1
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