Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use transformersbook/distilbert-base-uncased-distilled-clinc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use transformersbook/distilbert-base-uncased-distilled-clinc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="transformersbook/distilbert-base-uncased-distilled-clinc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("transformersbook/distilbert-base-uncased-distilled-clinc") model = AutoModelForSequenceClassification.from_pretrained("transformersbook/distilbert-base-uncased-distilled-clinc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f84c7ba2b8751b0c27733482d81efdcad32bdd7129318b57979cdcf569bb3b15
- Size of remote file:
- 2.99 kB
- SHA256:
- 2720558bfde22dfeb8fd6445127ed6823377daff15a3115c03cc2723132bd22d
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