Instructions to use rmhirota/model_dir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rmhirota/model_dir with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rmhirota/model_dir")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rmhirota/model_dir") model = AutoModelForSequenceClassification.from_pretrained("rmhirota/model_dir") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4659fd3229c23da209774e742276196cc5de54258b0086d214d76b17471af215
- Size of remote file:
- 4.47 kB
- SHA256:
- bd442375ca9581b6d8369f1950af05799c94d04e675717c6a30e2967e6d7abb7
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