Instructions to use Kamer/model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kamer/model2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kamer/model2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kamer/model2") model = AutoModelForSequenceClassification.from_pretrained("Kamer/model2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f99c66ef76f60149ac79bf678dd9437f15173b6d084a7bef7247d3a76b5191b
- Size of remote file:
- 268 MB
- SHA256:
- b23fcf1651146fb84616a6823e870e5fb3918be8ec715b240f071d5726dac3ce
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