Instructions to use WindyTranslate/translate-en-sem with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-en-sem 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="WindyTranslate/translate-en-sem")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-en-sem") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-en-sem", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 068f8787ec4b538747d59ea7d2a26b5a90a44b7e798a3d0e7c3b4cda677f62de
- Size of remote file:
- 808 kB
- SHA256:
- 8890a67599b7a5178a68cf986d70884c0b8f41a67e208d022f9e29f3246277e2
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