Instructions to use rpii2023/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rpii2023/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rpii2023/model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rpii2023/model") model = AutoModelForSequenceClassification.from_pretrained("rpii2023/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4cf16c95a6d940334819ed460dc7b521c394022d3600134e260d9d787236e060
- Size of remote file:
- 3.52 kB
- SHA256:
- 6d2b4a55f25a3b81156f2f9c5a8bcd4b6b5feda91f04739001c323e78100f658
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