Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use keremp/deberta-em-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keremp/deberta-em-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="keremp/deberta-em-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("keremp/deberta-em-large") model = AutoModelForSequenceClassification.from_pretrained("keremp/deberta-em-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from keremp/deberta-em-large: direct link, hf CLI and curl.
- Browser
- Download file 1.74 GB
-
https://huggingface.co/keremp/deberta-em-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://keremp/deberta-em-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/keremp/deberta-em-large/resolve/main/pytorch_model.bin
1.74 GB
- Xet hash:
- 40c2015fe32a43a5a123c83696e955c942becf985fb7846cda68bfd35f48d953
- Size of remote file:
- 1.74 GB
- SHA256:
- c490618ec90b96e5b6cea4382e114030b33c2ee526b3085b17e05fdc222bd967
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