Instructions to use pixelsandpointers/distilbert-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/distilbert-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/distilbert-base-uncased-next-turn-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pixelsandpointers/distilbert-base-uncased-next-turn-classifier") model = AutoModelForSequenceClassification.from_pretrained("pixelsandpointers/distilbert-base-uncased-next-turn-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81bc75a55d44a1858d8270076a583d33d2b345aa95d22dd599e42cf301dbd9e7
- Size of remote file:
- 268 MB
- SHA256:
- ff807a17cdf675f0f08e93d4b083d50ea4eb67bddb37a3f6aeda6e6e89ac25dc
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