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
File size: 1,041 Bytes
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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)
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