Instructions to use mstaron/SingBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mstaron/SingBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mstaron/SingBERTa")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mstaron/SingBERTa") model = AutoModelForMaskedLM.from_pretrained("mstaron/SingBERTa") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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# the models are already pre-defined, so we do not need to train anything here
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features = pipeline(
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"feature-extraction",
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model=
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tokenizer=
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return_tensor = False
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)
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# the models are already pre-defined, so we do not need to train anything here
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features = pipeline(
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"feature-extraction",
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model=model,
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tokenizer=tokenizer,
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return_tensor = False
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)
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