Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use kimsiun/ec_classfication with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kimsiun/ec_classfication with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kimsiun/ec_classfication")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kimsiun/ec_classfication") model = AutoModelForSequenceClassification.from_pretrained("kimsiun/ec_classfication") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4cb7d8aad1b513a6658f84a2fb3529769b0dd76d929bc5a661dcda16d57c5c19
- Size of remote file:
- 3.58 kB
- SHA256:
- 223fb0a493bdd7845110cd7423de23b18c47f8e66cab5d9d6498dde270db0008
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