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
distilbert-base-uncased-finetuned-clinc / runs /Feb01_15-31-26_GPU-PC /events.out.tfevents.1675233098.GPU-PC.18350.0
- Xet hash:
- d637285d46cd69bee511d6cd98c2c193e8b5992b66ece0b48cc684b655326258
- Size of remote file:
- 12.7 kB
- SHA256:
- 5ec3178859f58918e544c53e596f5113b950d1c7fca89db61171b9030cabad55
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