Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Hwijung/distilbert-base-uncased-finetuned-clinc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hwijung/distilbert-base-uncased-finetuned-clinc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hwijung/distilbert-base-uncased-finetuned-clinc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hwijung/distilbert-base-uncased-finetuned-clinc") model = AutoModelForSequenceClassification.from_pretrained("Hwijung/distilbert-base-uncased-finetuned-clinc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ddd79fb38df096e94ea47c0e3c2a5331094d8809802ccf536384a9249a37213d
- Size of remote file:
- 268 MB
- SHA256:
- 07371cf9e23d4700f1bae3208c8a1ffc4eb5db9460427d192dcf1dbc215bc58d
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