Instructions to use khalidalt/DeBERTa-v3-large-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use khalidalt/DeBERTa-v3-large-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="khalidalt/DeBERTa-v3-large-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("khalidalt/DeBERTa-v3-large-mnli") model = AutoModelForSequenceClassification.from_pretrained("khalidalt/DeBERTa-v3-large-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from khalidalt/DeBERTa-v3-large-mnli: direct link, hf CLI and curl.
- Browser
- Download file 1.74 GB
-
https://huggingface.co/khalidalt/DeBERTa-v3-large-mnli/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://khalidalt/DeBERTa-v3-large-mnli/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/khalidalt/DeBERTa-v3-large-mnli/resolve/main/pytorch_model.bin
1.74 GB
- Xet hash:
- e90a7ed14ee17636fb0fcfe250fe8f93154af305a275f24aa142bafd965fd6bc
- Size of remote file:
- 1.74 GB
- SHA256:
- 6421f561358fc200624b17c798f3b8f9a9c9185b69867765a66b0499df6be055
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