Instructions to use DevBeom/roberta-beomsang with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DevBeom/roberta-beomsang with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DevBeom/roberta-beomsang")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DevBeom/roberta-beomsang") model = AutoModelForSequenceClassification.from_pretrained("DevBeom/roberta-beomsang", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90914a873040ed29a1e2d8a6678a388780a8892a65a578b7dccf250e96e26e73
- Size of remote file:
- 499 MB
- SHA256:
- a0d33fe7eb012daf4480454a28c57632660d9afc14c5b029f0fdaeb6c6b6108b
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