Instructions to use rsaketh02/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rsaketh02/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rsaketh02/model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rsaketh02/model") model = AutoModelForSequenceClassification.from_pretrained("rsaketh02/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 97283ea00f8572b21be6021f6e661061f614618d31c605135faf55e2a99a30e4
- Size of remote file:
- 268 MB
- SHA256:
- 7b7c4102864256b609187a0e720c69e37b4c4c3a692666df88e09f12989bf988
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