Instructions to use rayschwartz/text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rayschwartz/text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rayschwartz/text-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rayschwartz/text-classification") model = AutoModelForSequenceClassification.from_pretrained("rayschwartz/text-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d110ff51844053ffb73b426904bab19f9d5904641d94766000d76803ec2c5ee
- Size of remote file:
- 268 MB
- SHA256:
- 4ab12f2a67db8314270bcd5c65e1649c698ab9e1e4f3652909ab2df812bd7041
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