Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use MelikeDulkadir/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MelikeDulkadir/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MelikeDulkadir/output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MelikeDulkadir/output") model = AutoModelForSequenceClassification.from_pretrained("MelikeDulkadir/output") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e8ca0f44a3bc04cf48f0968734dc35ab483d43e6cfd68e979b79110584062e30
- Size of remote file:
- 268 MB
- SHA256:
- 2be4eae9a073e7b7da74fbe5b5e3eb216ff6a7d089d99d5b74e4f2d4cf1a3628
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.