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:
- 36998bf627388ce69bf948641f9a6ab546b5a472f2ef6ab63966feafa9b7cd0a
- Size of remote file:
- 541 MB
- SHA256:
- e3ea13b0aa84acbfd74b547418ab700339a65f333fcf30976fe40b9a878a7b09
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.