Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Kayvane/distilbert-undersampled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kayvane/distilbert-undersampled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kayvane/distilbert-undersampled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kayvane/distilbert-undersampled") model = AutoModelForSequenceClassification.from_pretrained("Kayvane/distilbert-undersampled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b17770f76dd28ca231730f2e58bc0c79849c584361921d92d6970a8763e27ed
- Size of remote file:
- 268 MB
- SHA256:
- e278017ed886a4579b2480dfb3cc8d541ca0c50a202c96456a2d9e8b1fa35e19
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