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:
- 94f376de8c799bfbf3a2f1ea7e599891ace9b071f278c792724bb46630df5d34
- Size of remote file:
- 438 MB
- SHA256:
- e8136abe46d63bd06de4c4c167864ff3adb34dbd4056f240d957a7829a44119f
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