Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use pepa/deberta-v3-large-fever with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pepa/deberta-v3-large-fever with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pepa/deberta-v3-large-fever")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pepa/deberta-v3-large-fever") model = AutoModelForSequenceClassification.from_pretrained("pepa/deberta-v3-large-fever", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from pepa/deberta-v3-large-fever: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/pepa/deberta-v3-large-fever/resolve/main/training_args.bin
- Command line
-
hf download hf://pepa/deberta-v3-large-fever/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/pepa/deberta-v3-large-fever/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- 991cbd0535153ea5800aa4296e0d70c593fe450c68289789ad1be89cfff028c5
- Size of remote file:
- 3.38 kB
- SHA256:
- 07f47095595d6da85490f13a88c41d9b297b0f42d50387b828991daca97b0a29
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.