Instructions to use Kamer/finalModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kamer/finalModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kamer/finalModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kamer/finalModel") model = AutoModelForSequenceClassification.from_pretrained("Kamer/finalModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 40287278182a57aace419cd685b05851bc44f396f5dbb0e818cf42de8976ef75
- Size of remote file:
- 268 MB
- SHA256:
- 87895dfa38d9597fc18cfeb53c7c17b28c1e64499ea8ed56187bc8e725bf4c75
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