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
| 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 | |
| [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) | |
| ## Files Included | |
| - `model.safetensors` — model weights | |
| - Full tokenizer (`tokenizer.json`, `spm.model`, `tokenizer_config.json`, etc.) | |
| - `config.json` | |
| ## Usage | |
| ```python | |
| 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) | |