Instructions to use contemmcm/6092b68db85e70fdf09a4e82345627f1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use contemmcm/6092b68db85e70fdf09a4e82345627f1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="contemmcm/6092b68db85e70fdf09a4e82345627f1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("contemmcm/6092b68db85e70fdf09a4e82345627f1") model = AutoModelForSequenceClassification.from_pretrained("contemmcm/6092b68db85e70fdf09a4e82345627f1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1352e7ae35d85f4b89e60c5870dbbc7d952fe05433bc7a72385a9937ccdb78ac
- Size of remote file:
- 5.97 kB
- SHA256:
- c7e235fdbad8ff0763c9a9d0dec380bd07eb36bfb0a4cbfe98aea43cfbc70259
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