Instructions to use Christian2903/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Christian2903/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Christian2903/model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Christian2903/model") model = AutoModelForSequenceClassification.from_pretrained("Christian2903/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb4b1f058a60c0e3c47c1a4e82fe3c186ed42adba248422ef88997a84c5d9128
- Size of remote file:
- 4.6 kB
- SHA256:
- c081242bb577d5b751dc2f171617aa3b022f945128635bba1a68e47780cc5773
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