Instructions to use pixelsandpointers/bert-base-uncased-next-turn-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pixelsandpointers/bert-base-uncased-next-turn-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pixelsandpointers/bert-base-uncased-next-turn-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pixelsandpointers/bert-base-uncased-next-turn-classifier") model = AutoModelForSequenceClassification.from_pretrained("pixelsandpointers/bert-base-uncased-next-turn-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e439677ccefc609a371d7f14340debeb83111c84257cf0fad90ecb09db94d8f0
- Size of remote file:
- 438 MB
- SHA256:
- 0c081d60f9a7ce9a39305459b732b9170f925461baafa4973a7ccb9e541fddae
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