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:
- f1b51cb3e6ae56fd171656827c2c005c763f8717fe9173534702e2aaccbce1d3
- Size of remote file:
- 268 MB
- SHA256:
- b32db16cb83ab8899b1a3beb602a62c255fe721c34edbe77666f8945bf07053b
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