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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ilhami/Tr_En_AcademicTranslation") model = AutoModelForSeq2SeqLM.from_pretrained("ilhami/Tr_En_AcademicTranslation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download source.spm from ilhami/Tr_En_AcademicTranslation: direct link, hf CLI and curl.
- Browser
- Download file 840 kB
-
https://huggingface.co/ilhami/Tr_En_AcademicTranslation/resolve/refs%2Fpr%2F2/source.spm
- Command line
-
hf download hf://ilhami/Tr_En_AcademicTranslation@refs/pr/2/source.spm
-
curl -L -o source.spm https://huggingface.co/ilhami/Tr_En_AcademicTranslation/resolve/refs%2Fpr%2F2/source.spm
840 kB
- Xet hash:
- 312ebeeadebd9679a22c18f452a727ff8aa50d04076a131152b90394d0d62133
- Size of remote file:
- 840 kB
- SHA256:
- 4a6356e3ee892f2896dbf0b5cabeb51fdf16e03c70b0702cefc8b847e504241c
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