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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 489673aa8c050b472320444e204325d06b4714467dd024bf7e0ea68285f5188f
- Size of remote file:
- 517 MB
- SHA256:
- a60198e0567b57773bd095a5c283e6f0a68fbdc2d51f14d9ced5b19f4149cb1c
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