Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use KCourtney/distilbert-base-uncased-distilled-clinc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KCourtney/distilbert-base-uncased-distilled-clinc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KCourtney/distilbert-base-uncased-distilled-clinc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KCourtney/distilbert-base-uncased-distilled-clinc") model = AutoModelForSequenceClassification.from_pretrained("KCourtney/distilbert-base-uncased-distilled-clinc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2cd7f19923babcd01404deb8398b9b5bb563bfecca45f3c0d2068f05d1da5a8a
- Size of remote file:
- 268 MB
- SHA256:
- 96c894dd0ac7a9b3e17075c69218692dd18776c0f334561791852e87f66dbc48
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