Instructions to use ilhami/Tr_En_AcademicTranslation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ilhami/Tr_En_AcademicTranslation 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="ilhami/Tr_En_AcademicTranslation")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ilhami/Tr_En_AcademicTranslation") model = AutoModelForSeq2SeqLM.from_pretrained("ilhami/Tr_En_AcademicTranslation") - Notebooks
- Google Colab
- Kaggle
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README.md
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Turkish to English Machine Translation
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Dataset : Sel, İ. , Üzen, H. & Hanbay, D. (2021). Creating a Parallel Corpora for Turkish-English Academic Translations . Computer Science , 5th International Artificial Intelligence and Data Processing symposium , 335-340 . DOI: 10.53070/bbd.990959
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Transformer Based(6 Layer)
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Turkish to English Machine Translation
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Dataset : Sel, İ. , Üzen, H. & Hanbay, D. (2021). Creating a Parallel Corpora for Turkish-English Academic Translations . Computer Science , 5th International Artificial Intelligence and Data Processing symposium , 335-340 . DOI: 10.53070/bbd.990959
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Transformer Based(6 Layer)
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