Instructions to use WindyTranslate/translate-en-cpf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-en-cpf 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-cpf")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-en-cpf") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-en-cpf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d9167cec0e654c62bea4ceb0d954b0ebd52a42626f6a3596989b7a4b3261e97a
- Size of remote file:
- 807 kB
- SHA256:
- 264235493711acf369933c2dee7d9fed8536260daa3c0b520686b197a39dc6e5
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