Instructions to use date3k2/mamba-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use date3k2/mamba-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="date3k2/mamba-text-classification", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("date3k2/mamba-text-classification", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("date3k2/mamba-text-classification", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d1d1c62bd08084efaf1d6d84c0221c089f3d4720fb2a975c3d09920e85cbf746
- Size of remote file:
- 5.11 kB
- SHA256:
- 244b2a76a6e2ed1b76a131d337a76afe6d224b8d1bce1a87d0cabfd5d06f5e45
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