Instructions to use mr-shreshth/DeBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mr-shreshth/DeBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mr-shreshth/DeBERTa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mr-shreshth/DeBERTa") model = AutoModelForSequenceClassification.from_pretrained("mr-shreshth/DeBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mr-shreshth/DeBERTa: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/mr-shreshth/DeBERTa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mr-shreshth/DeBERTa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mr-shreshth/DeBERTa/resolve/main/pytorch_model.bin
557 MB
- Xet hash:
- 30a752e64d95ef1495d9c4f6a479c678d323ed0c993c1946f024c1154cda75d8
- Size of remote file:
- 557 MB
- SHA256:
- 1ceb9276df7cd6331932ad419f08584a1b182119281460a5d690e6c58448cb69
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