Instructions to use feradauto/scibert_nlp4sg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use feradauto/scibert_nlp4sg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="feradauto/scibert_nlp4sg")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("feradauto/scibert_nlp4sg") model = AutoModelForSequenceClassification.from_pretrained("feradauto/scibert_nlp4sg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a749475e0f51839504996b90f840c84b498ca521c41b2bb679780729de5d8290
- Size of remote file:
- 440 MB
- SHA256:
- a45e458d89fd22a6e6985733621da4685767ad85773e36e12709affbe2d616ba
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