Instructions to use javilonso/rm23_ctry1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use javilonso/rm23_ctry1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="javilonso/rm23_ctry1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("javilonso/rm23_ctry1") model = AutoModelForSequenceClassification.from_pretrained("javilonso/rm23_ctry1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 84f506cd69da70d833b32ccfef83b376d91b514fa277985eefca1ec63c0a737d
- Size of remote file:
- 3.9 kB
- SHA256:
- 512ac6fa29160f163a6ec911a0bdc391848a5eb6de24f7b6c41ae373fa4cb9a2
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