Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use wnic00/distilbert-p1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wnic00/distilbert-p1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wnic00/distilbert-p1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wnic00/distilbert-p1") model = AutoModelForSequenceClassification.from_pretrained("wnic00/distilbert-p1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90f425bdf9a09855f37b5db8c14cd2b0ba7baf362d0fae5ccebf8a227bf3b576
- Size of remote file:
- 541 MB
- SHA256:
- 109b48069bd86814bac195713a8a68981e6b5015066e74ebfce1510ac64bab51
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