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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ec57fab456f82e7a68903a15b2261e965fb258571e21de13f10d525d831932c
- Size of remote file:
- 738 MB
- SHA256:
- 3b83bf089ff3f3ef48a390e81f2583d4528036ab8730eb31ba73436ef48f479f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.