Instructions to use oskarandrsson/mt-en-sv-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oskarandrsson/mt-en-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-en-sv-finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("oskarandrsson/mt-en-sv-finetuned") model = AutoModelForSeq2SeqLM.from_pretrained("oskarandrsson/mt-en-sv-finetuned") - Notebooks
- Google Colab
- Kaggle
mt-en-sv-finetuned
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-sv. It achieves the following results on the Tatoeba.en.sv evaluation set:
- Bleu: 67.28528945378108
Model description
- source_lang = en
- target_lang = sv
Intended uses & limitations
More information needed
Training and evaluation data
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 24
- eval_batch_size: 4
- mixed_precision_training: Native AMP
Training results
| testset | BLEU |
|---|---|
| Tatoeba.en.sv | 67.28 |
Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1
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