Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use keremp/deberta-em with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keremp/deberta-em with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="keremp/deberta-em")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("keremp/deberta-em") model = AutoModelForSequenceClassification.from_pretrained("keremp/deberta-em", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from keremp/deberta-em: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/keremp/deberta-em/resolve/main/training_args.bin
- Command line
-
hf download hf://keremp/deberta-em/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/keremp/deberta-em/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- da2d8eb315ff9c5aceaba5beffa8dcbba5650c4c9e0a93149cec5c355db445d3
- Size of remote file:
- 6.78 kB
- SHA256:
- 2cd9bf73465d401d03972af4081963bc4d2f5c5e4c1905770994723b76d8326b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.