Instructions to use contemmcm/0ec236299b317d76c0f94de06fc85471 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use contemmcm/0ec236299b317d76c0f94de06fc85471 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="contemmcm/0ec236299b317d76c0f94de06fc85471")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("contemmcm/0ec236299b317d76c0f94de06fc85471") model = AutoModelForSequenceClassification.from_pretrained("contemmcm/0ec236299b317d76c0f94de06fc85471", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24e43fee0780e02f04a8ba9fef33e10dc6a3f90ba3cb37f675ba219d04826b59
- Size of remote file:
- 5.97 kB
- SHA256:
- 68f30f49f1fa42c188274e380f2e3cdcfa5c76d0bb0d6239f4191be2324d6202
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