Instructions to use LiefOlsonMd/deberta-v3-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiefOlsonMd/deberta-v3-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="LiefOlsonMd/deberta-v3-base")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LiefOlsonMd/deberta-v3-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
base_model: microsoft/deberta-v3-base
tags:
- deberta
- deberta-v3
- fill-mask
library_name: transformers
DeBERTa-v3-base
This repository contains the official microsoft/deberta-v3-base model in safetensors format together with the complete tokenizer files for maximum compatibility.
Original Model
Files Included
model.safetensors— model weights- Full tokenizer (
tokenizer.json,spm.model,tokenizer_config.json, etc.) config.json
Usage
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("LiefOlsonMd/deberta-v3-base")
tokenizer = AutoTokenizer.from_pretrained("LiefOlsonMd/deberta-v3-base")
Model Details
- Architecture: DeBERTa-v3-base
- Backbone parameters: 86M
- Embedding parameters: ~98M (128k SentencePiece vocabulary)
- Total parameters: ≈ 184M
- Weights:
model.safetensors(~371 MB)
License
MIT License (same as the original model)