Instructions to use taskydata/deberta-v3-base_v_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use taskydata/deberta-v3-base_v_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="taskydata/deberta-v3-base_v_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("taskydata/deberta-v3-base_v_1") model = AutoModelForSequenceClassification.from_pretrained("taskydata/deberta-v3-base_v_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from taskydata/deberta-v3-base_v_1: direct link, hf CLI and curl.
- Browser
- Download file 3.9 kB
-
https://huggingface.co/taskydata/deberta-v3-base_v_1/resolve/main/training_args.bin
- Command line
-
hf download hf://taskydata/deberta-v3-base_v_1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/taskydata/deberta-v3-base_v_1/resolve/main/training_args.bin
3.9 kB
- Xet hash:
- 9987d2815d41ce4c5170b4204bd8fb5510bf071b984c72216c8493fe800fb51c
- Size of remote file:
- 3.9 kB
- SHA256:
- 7b9b2d92f235054ecc9c23f741487e6500d3c9de3242629b8f31d1575f37aa79
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