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:
- 3788d0fdad7483f77f9daedecff96d7f91d2dc06dc9b89b26c9583a62126be89
- Size of remote file:
- 2.74 kB
- SHA256:
- bfcd76008ac333db5209f9ba9aded2929557ff1a3d94ca5de542c8e4fe5ab5b1
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