Instructions to use perkan/shortM-opus-mt-tc-base-en-sr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use perkan/shortM-opus-mt-tc-base-en-sr 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="perkan/shortM-opus-mt-tc-base-en-sr")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("perkan/shortM-opus-mt-tc-base-en-sr") model = AutoModelForSeq2SeqLM.from_pretrained("perkan/shortM-opus-mt-tc-base-en-sr") - Notebooks
- Google Colab
- Kaggle
Model for English to Serbian translation. Base model is HelsinkiNLP sh model. Fine-tuned using OPUS-100 dataset, which was modified with Paraphrasing Database size M.
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